Pass MLS-C01 Exam with Online AWS Certified Machine Learning - Specialty Dumps - Free Latest Dumps for IT Certification Exams

Pass MLS-C01 Exam with Online AWS Certified Machine Learning – Specialty Dumps

dumps2024-01-06T10:37:52+08:00

To take the AWS Certified Machine Learning – Specialty MLS-C01 exam, you need to have at least two years of hands-on experience developing, architecting, and running ML or deep learning workloads in the AWS Cloud. The real AWS MLS-C01 exam contains 65 multiple-choice or multiple-response questions, and you have 180 minutes to answer all the questions. AWS MLS-C01 exam languages are English, Japanese, Korean, and Simplified Chinese. We have cracked the latest online MLS-C01 exam dumps, which can guarantee you success. Free AWS Certified Machine Learning – Specialty MLS-C01 exam dumps are below.

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1. A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined.

What feature engineering and model development approach should the Specialist take with a dataset this large?

Use an Amazon SageMaker notebook for both feature engineering and model development

Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development

Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development

Use Amazon ML for both feature engineering and model development.

2. A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS

Which approach should the Specialist use for training a model using that data?

Write a direct connection to the SQL database within the notebook and pull data in

Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.

Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in

Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

3. Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

Recall

Misclassification rate

Mean absolute percentage error (MAPE)

Area Under the ROC Curve (AUC)

4. A Machine Learning Specialist is using Amazon Sage Maker to host a model for a highly available customer-facing application.

The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production. To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed

What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if the model does not perform as expected.

Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.

Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.

Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.

5. A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter.

Which machine learning approach should be used to solve this problem?

Logistic regression

Random Cut Forest (RCF)

Principal component analysis (PCA)

Linear regression

6. A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket A Machine Learning Specialist wants to use SQL to run queries on this data.

Which solution requires the LEAST effort to be able to query this data?

Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.

Use AWS Glue to catalogue the data and Amazon Athena to run queries

Use AWS Batch to run ETL on the data and Amazon Aurora to run the quenes

Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries

7. A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs

What does the Specialist need to do1?

Bundle the NVIDIA drivers with the Docker image

Build the Docker container to be NVIDIA-Docker compatible

Organize the Docker container's file structure to execute on GPU instances.

Set the GPU flag in the Amazon SageMaker Create TrainingJob request body

8. A large JSON dataset for a project has been uploaded to a private Amazon S3 bucket The Machine Learning Specialist wants to securely access and explore the data from an Amazon SageMaker notebook instance A new VPC was created and assigned to the Specialist

How can the privacy and integrity of the data stored in Amazon S3 be maintained while granting access to the Specialist for analysis?

Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled Use an S3 ACL to open read privileges to the everyone group

Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Copy the JSON dataset from Amazon S3 into the ML storage volume on the SageMaker notebook instance and work against the local dataset

Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Define a custom S3 bucket policy to only allow requests from your VPC to access the S3 bucket

Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled. Generate an S3 pre-signed URL for access to data in the bucket

9. Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20 85%

The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 1312%

The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 - 0.32).

The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32

10. A Machine Learning Specialist is building a supervised model that will evaluate customers' satisfaction with their mobile phone service based on recent usage The model's output should infer whether or not a customer is likely to switch to a competitor in the next 30 days.

Which of the following modeling techniques should the Specialist use1?

Time-series prediction

Anomaly detection

Binary classification

Regression

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11. A web-based company wants to improve its conversion rate on its landing page Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker However there is an overfitting problem training data shows 90% accuracy in predictions, while test data shows 70% accuracy only

The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases

Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

Increase the randomization of training data in the mini-batches used in training.

Allocate a higher proportion of the overall data to the training dataset

Apply L1 or L2 regularization and dropouts to the training.

Reduce the number of layers and units (or neurons) from the deep learning network.

12. A Machine Learning Specialist was given a dataset consisting of unlabeled data The Specialist must create a model that can help the team classify the data into different buckets.

What model should be used to complete this work?

K-means clustering

Random Cut Forest (RCF)

XGBoost

BlazingText

13. A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has 15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.

Which model should be used for categorizing new products using the provided dataset for training?

An XGBoost model where the objective parameter is set to multi: softmax

A deep convolutional neural network (CNN) with a softmax activation function for the last layer

A regression forest where the number of trees is set equal to the number of product categories

A DeepAR forecasting model based on a recurrent neural network (RNN)

14. A Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors While exploring the data, the Specialist notices that the magnitude of the input features vary greatly The Specialist does not want variables with a larger magnitude to dominate the model

What should the Specialist do to prepare the data for model training'?

Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution

Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude

Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude

Apply the orthogonal sparse Diagram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

15. A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

16. A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents

How should a Machine Learning Specialist address this issue for future documents?

Convert current documents to SSML with pronunciation tags

Create an appropriate pronunciation lexicon.

Output speech marks to guide in pronunciation

Use Amazon Lex to preprocess the text files for pronunciation

17. A Machine Learning Specialist is using Apache Spark for pre-processing training data. As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it.

Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE)

Download the AWS SDK for the Spark environment

Install the SageMaker Spark library in the Spark environment.

Use the appropriate estimator from the SageMaker Spark Library to train a model.

Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.

Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker

Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.

18. A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested. The company also wants be able to save the results in its data lake for later processing and analysis

What is the MOST efficient way to accomplish these tasks?

Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection Then use Kinesis Data Firehose to stream the results to Amazon S3

Ingest the data into Apache Spark Streaming using Amazon EM

and use Spark MLlib with k-means to perform anomaly detection Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake

Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.

Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data

19. A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulent.

How should the Specialist frame this business problem?

Streaming classification

Binary classification

Multi-category classification

Regression classification

20. Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3

The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3

Which approach will provide the information required for further analysis?

Use Amazon Comprehend with the transcribed files to build the key topics

Use Amazon Translate with the transcribed files to train and build a model for the key topics

Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics

Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics

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21. A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable

What should be done to reduce the impact of having such a large number of features?

Perform one-hot encoding on highly correlated features

Use matrix multiplication on highly correlated features.

Create a new feature space using principal component analysis (PCA)

Apply the Pearson correlation coefficient

22. A Machine Learning Specialist wants to determine the appropriate SageMaker Variant Invocations Per Instance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS As this is the first deployment, the Specialist intends to set the invocation safety factor to 0 5

Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the sageMaker variant invocations Per instance setting?

600

2,400

23. A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago

Which method should the Specialist try to improve model performance?

The model needs to be completely re-engineered because it is unable to handle product inventory changes

The model's hyperparameters should be periodically updated to prevent drift

The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes

The model should be periodically retrained using the original training data plus new data as product inventory changes

24. A manufacturer of car engines collects data from cars as they are being driven. The data collected includes timestamp, engine temperature, rotations per minute (RPM), and other sensor readings The company wants to predict when an engine is going to have a problem so it can notify drivers in advance to get engine maintenance The engine data is loaded into a data lake for training.

Which is the MOST suitable predictive model that can be deployed into production'?

Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.

This data requires an unsupervised learning algorithm Use Amazon SageMaker k-means to cluster the data

Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a convolutional neural network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.

This data is already formulated as a time series Use Amazon SageMaker seq2seq to model the time series.

25. A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset

Which tool should be used to improve the validation accuracy?

Amazon Comprehend syntax analysts and entity detection

Amazon SageMaker BlazingText allow mode

Natural Language Toolkit (NLTK) stemming and stop word removal

Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers

26. A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches

What actions would allow the Specialist to get relevant numerical representations?

Reduce image resolution and use reduced resolution pixel values as features

Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels

Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer

Average colors by channel to obtain three-dimensional representations of images.

27. A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users

The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and 999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns Using this dataset for training, the Data Science team trained a random forest model that converged with over 99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.

Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

Add more deep trees to the random forest to enable the model to learn more features.

indicate a copy of the samples in the test database in the training dataset

Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

Change the cost function so that false negatives have a higher impact on the cost value than false positives

Change the cost function so that false positives have a higher impact on the cost value than false negatives

28. While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown.

What does this mean?

The model might have prediction errors over a range of target values.

The dataset cannot be accurately represented using the regression model

There are too many variables in the model

The model is predicting its target values perfectly.

29. During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates.

What is the MOST likely cause of this issue?

The class distribution in the dataset is imbalanced

Dataset shuffling is disabled

The batch size is too big

The learning rate is very high

30. A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model

What should the Specialist do to address the performance issues with the current solution?

Use the SageMaker batch transform feature

Compress the training data into Apache Parquet format.

Ensure that the input mode for the training job is set to Pipe.

Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.

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31. A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes

Which function will produce the desired output?

Dropout

Smooth L1 loss

Softmax

Rectified linear units (ReLU)

32. A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant

Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced

Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker

Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker

Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.

33. An Amazon SageMaker notebook instance is launched into Amazon VPC. The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3.

Which of the following are required to access the bucket and avoid the access denied error? (Select THREE)

An AWS KMS key policy that allows access to the customer master key (CMK)

A SageMaker notebook security group that allows access to Amazon S3

An 1AM role that allows access to the specific S3 bucket

A permissive S3 bucket policy

An S3 bucket owner that matches the notebook owner

A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.

34. A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance

How should the records be stored in Amazon S3 to improve query performance?

CSV files

Parquet files

Compressed JSON

RecordIO

35. A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data

Which type of data repository is the MOST cost-effective solution?

An Amazon EBS-backed Amazon EC2 instance with hourly directories

An Amazon RDS database with hourly table partitions

An Amazon S3 data lake with hourly object prefixes

An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes

36. A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined. The model needs lo be retrained daily

Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3 then use AWS Glue to do the transformation

Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3

Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.

Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehouse stream that transforms raw record attributes into simple transformed values using SQ

37. A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city as this is a prototype, only daily data from the last year is available.

Which model is MOST likely to provide the best results in Amazon SageMaker?

Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.

Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.

Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.

Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.

38. For the given confusion matrix, what is the recall and precision of the model?

Recall = 0.92 Precision = 0.84

Recall = 0.84 Precision = 0.8

Recall = 0.92 Precision = 0.8

Recall = 0.8 Precision = 0.92

39. A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published.

A sample of the data being used is below.

Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.

What technique should be used to convert this column to binary values.

Binarization

One-hot encoding

Tokenization

Normalization transformation

40. A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data

Which AWS service should the Data Scientist use?

Amazon Athena

Amazon Redshift Spectrum

AWS Glue

Amazon QuickSight

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41. A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3.

The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3.

Which solution takes the LEAST effort to implement?

Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet

Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.

Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.

Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.

42. An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy.

Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

m5 4xlarge (general purpose)

r5.2xlarge (memory optimized)

p3.2xlarge (GPU accelerated computing)

p3 8xlarge (GPU accelerated computing)

43. A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers

Currently, the company has the following data in Amazon Aurora

• Profiles for all past and existing customers

• Profiles for all past and existing insured pets

• Policy-level information

• Premiums received

• Claims paid

What steps should be taken to implement a machine learning model to identify potential new customers on social media?

Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

44. A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet.

How should the company set up the job?

Launch the notebook instances in a public subnet and access the data through the public S3 endpoint

Launch the notebook instances in a private subnet and access the data through a NAT gateway

Launch the notebook instances in a public subnet and access the data through a NAT gateway

Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.

45. A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training.

What should the Specialist do to optimize the data for training on SageMaker?

Use the SageMaker batch transform feature to transform the training data into a DataFrame

Use AWS Glue to compress the data into the Apache Parquet format

Transform the dataset into the Recordio protobuf format

Use the SageMaker hyperparameter optimization feature to automatically optimize the data

46. A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.

What should the Specialist do to initialize the model to re-train it with the custom data?

Initialize the model with random weights in all layers including the last fully connected layer

Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

Initialize the model with random weights in all layers and replace the last fully connected layer

Initialize the model with pre-trained weights in all layers including the last fully connected layer

47. A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.

Which approach allows the Specialist to use all the data to train the model?

Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.

Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset

Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.

Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

48. A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences. The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions

Here is an example from the dataset

"The quck BROWN FOX jumps over the lazy dog "

Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Select THREE)

Perform part-of-speech tagging and keep the action verb and the nouns only

Normalize all words by making the sentence lowercase

Remove stop words using an English stopword dictionary.

Correct the typography on "quck" to "quick."

One-hot encode all words in the sentence

Tokenize the sentence into words.

49. This graph shows the training and validation loss against the epochs for a neural network

The network being trained is as follows

• Two dense layers one output neuron

• 100 neurons in each layer

• 100 epochs

• Random initialization of weights

Which technique can be used to improve model performance in terms of accuracy in the validation set?

Early stopping

Random initialization of weights with appropriate seed

Increasing the number of epochs

Adding another layer with the 100 neurons

50. A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90%

What should the Specialist consider to fix this issue1?

A longer training time

Making the network larger

Using a different optimizer

Using some form of regularization

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51. Example Corp has an annual sale event from October to December. The company has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year's upcoming event.

Which method should Example Corp use to split the data into a training dataset and evaluation dataset?

Pre-split the data before uploading to Amazon S3

Have Amazon ML split the data randomly.

Have Amazon ML split the data sequentially.

Perform custom cross-validation on the data

52. A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.

Which solution requires the LEAST coding effort?

Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3

Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team

Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

53. A Machine Learning Specialist has built a model using Amazon SageMaker built-in algorithms and is not getting expected accurate results The Specialist wants to use hyperparameter optimization to increase the model's accuracy

Which method is the MOST repeatable and requires the LEAST amount of effort to achieve this?

Launch multiple training jobs in parallel with different hyperparameters

Create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters

Create a hyperparameter tuning job and set the accuracy as an objective metric.

Create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter

54. IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning.

The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data.

The following are high priorities for the company:

• Solution simplicity

• Fast development time

• Low cost

• High flexibility

What technologies meet the company's requirements?

Amazon S3 and Amazon Athena

Amazon Redshift and AWS Glue

Amazon DynamoDB and DynamoDB Accelerator (DAX)

Amazon RDS and Amazon ES

55. A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactions

What metric is BEST suited to score the model?

Precision

Recall

Area Under the ROC Curve (AUC)

Root Mean Square Error (RMSE)

56. A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent. The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not

Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

Seq2seq

XGBoost

K-means

Random Cut Forest (RCF)

57. While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function.

What should the Specialist do to ensure better convergence during backpropagation?

Dimensionality reduction

Data normalization

Model regulanzation

Data augmentation for the minority class

58. An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.

Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

Listwise deletion

Last observation carried forward

Multiple imputation

Mean substitution

59. A Machine Learning Specialist discover the following statistics while experimenting on a model.

What can the Specialist from the experiments?

The model In Experiment 1 had a high variance error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1

The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1

The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows that high bias cannot be reduced by increasing layers and neurons in the model

The model in Experiment 1 had a high random noise error that was reduced in Experiment 3 by regularization Experiment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model

60. A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis.

Which of the following services would both ingest and store this data in the correct format?

AWSDMS

Amazon Kinesis Data Streams

Amazon Kinesis Data Firehose

Amazon Kinesis Data Analytics

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61. A Machine Learning Specialist needs to move and transform data in preparation for training Some of the data needs to be processed in near-real time and other data can be moved hourly There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data.

Which of the following services can feed data to the MapReduce jobs? (Select TWO)

AWSDMS

Amazon Kinesis

AWS Data Pipeline

Amazon Athena

Amazon ES

62. An insurance company is developing a new device for vehicles that uses a camera to observe drivers'

behavior and alert them when they appear distracted. The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models

During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images.

Which of the following should be used to resolve this issue? (Select TWO)

Add vanishing gradient to the model

Perform data augmentation on the training data

Make the neural network architecture complex.

Use gradient checking in the model

Add L2 regularization to the model

63. The Chief Editor for a product catalog wants the Research and Development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand The team has a set of training data

Which machine learning algorithm should the researchers use that BEST meets their requirements?

Latent Dirichlet Allocation (LDA)

Recurrent neural network (RNN)

K-means

Convolutional neural network (CNN)

64. A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours

With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s)

Which visualization will accomplish this?

A histogram showing whether the most important input feature is Gaussian.

A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.

A scatter plot showing (he performance of the objective metric over each training iteration

A scatter plot showing the correlation between maximum tree depth and the objective metric.

65. A Machine Learning Specialist is configuring automatic model tuning in Amazon SageMaker.

When using the hyperparameter optimization feature, which of the following guidelines should be followed to improve optimization? Choose the maximum number of hyperparameters supported by

Amazon SageMaker to search the largest number of combinations possible

Specify a very large hyperparameter range to allow Amazon SageMaker to cover every possible value.

Use log-scaled hyperparameters to allow the hyperparameter space to be searched as quickly as possible

Execute only one hyperparameter tuning job at a time and improve tuning through successive rounds of experiments

66. A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.

The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:

Based on the model evaluation results, why is this a viable model for production?

The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.

The precision of the model is 86%, which is less than the accuracy of the model.

The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.

The precision of the model is 86%, which is greater than the accuracy of the model.

67. A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.

What should the Specialist do to meet this objective?

Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EM

Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EM

Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EM

Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EM

68. A Data Engineer needs to build a model using a dataset containing customer credit card information.

How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VP

Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.

Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VP

Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.

Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

69. A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.

Why is the ML Specialist not seeing the instance visible in the VPC?

Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.

Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.

Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.

Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.

70. A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data.

Which solution requires the LEAST effort to be able to query this data?

Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.

Use AWS Glue to catalogue the data and Amazon Athena to run queries.

Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.

Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.

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71. A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.

Which solution should the Specialist recommend?

Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.

A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database

Collaborative filtering based on user interactions and correlations to identify patterns in the customer database

Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database

72. A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.

Which machine learning model type should the Specialist use to accomplish this task?

Linear regression

Classification

Clustering

Reinforcement learning

73. The displayed graph is from a foresting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

The model predicts both the trend and the seasonality well.

The model predicts the trend well, but not the seasonality.

The model predicts the seasonality well, but not the trend.

The model does not predict the trend or the seasonality well.

74. A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month.

The class distribution for these features is illustrated in the figure provided.

Based on this information which model would have the HIGHEST accuracy?

Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))

Logistic regression

Support vector machine (SVM) with non-linear kernel

Single perceptron with tanh activation function

75. A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll).

The dataset:

* Must be accessible from a VPC only.

* Must not traverse the public internet.

How can these requirements be satisfied?

Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VP

Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.

Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.

Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.

76. An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.

What combination of services is the MOST efficient to accomplish the task?

Amazon Transcribe, Amazon Translate, and Amazon Comprehend

Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq

Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)

Amazon Transcribe, Amazon Translate, and Amazon SageMaker BlazingText

77. A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.

What does the Specialist need to do?

Bundle the NVIDIA drivers with the Docker image.

Build the Docker container to be NVIDIA-Docker compatible.

Organize the Docker container's file structure to execute on GPU instances.

Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body

78. A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.

What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

Receiver operating characteristic (ROC) curve

Misclassification rate

Root Mean Square Error (RM&)

L1 norm

79. An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.

What should the Specialist do to meet these requirements?

Create one-hot word encoding vectors.

Produce a set of synonyms for every word using Amazon Mechanical Turk.

Create word embedding factors that store edit distance with every other word.

Download word embedding’s pre-trained on a large corpus.

80. A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.

Which services are integrated with Amazon SageMaker to track this information? (Select TWO.)

AWS CloudTrail

AWS Health

AWS Trusted Advisor

Amazon CloudWatch

AWS Config

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81. A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.

What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

Root Mean Square Error (RMSE)

Residual plots

Area under the curve

Confusion matrix

82. A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month.

The class distribution for these features is illustrated in the figure provided.

Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?

Decision tree

Linear support vector machine (SVM)

Naive Bayesian classifier

Single Perceptron with sigmoidal activation function

83. When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

The training channel identifying the location of training data on an Amazon S3 bucket.

The validation channel identifying the location of validation data on an Amazon S3 bucket.

The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.

Hyperparameters in a JSON array as documented for the algorithm used.

The Amazon EC2 instance class specifying whether training will be run using CPU or GP

The output path specifying where on an Amazon S3 bucket the trained model will persist.

84. A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.

Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.

How should the Data Scientist correct this issue?

Drop all records from the dataset where age has been set to 0.

Replace the age field value for records with a value of 0 with the mean or median value from the dataset.

Drop the age feature from the dataset and train the model using the rest of the features.

Use k-means clustering to handle missing features.

Pass MLS-C01 Exam with Online AWS Certified Machine Learning – Specialty Dumps

Pass MLS-C01 Exam with Online AWS Certified Machine Learning – Specialty Dumps

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