Actual Professional Machine Learning Engineer Study Guides

dumps2022-01-24T03:06:03+00:00

Actual Google Professional Machine Learning Engineer study guides are here. Starting from Feb.22, 2022, Professional Machine Learning Engineers exam will focus on Vertex AI. If you are planning to take Professional Machine Learning Engineer exam or after Feb.22, 2022, please pay more attention to updated guides.

Each candidate has 2 hours to finish Professional Machine Learning Engineer exam. The exam format is Multiple choice and Multiple select. The registration fee is $200. The available language is English. Free Google Professional Machine Learning Engineer exam study guides are available below.

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1. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

2. You are building an ML model to detect anomalies in real-time sensor data. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization .

How should you configure the pipeline?

1 = Dataflow, 2 - Al Platform, 3 = BigQuery

1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable

1 = BigQuery, 2 = AutoML, 3 = Cloud Functions

1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage

3. You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis .

Which algorithms should you use to build the model?

Classification

Reinforcement Learning

Recurrent Neural Networks (RNN)

Convolutional Neural Networks (CNN)

4. You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance .

What should you do?

Load the data into BigQuery and read the data from BigQuery.

Load the data into Cloud Bigtable, and read the data from Bigtable

Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage

Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)

5. You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer's identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases .

Which learning strategy should you recommend to train and deploy this ML model?

Differential privacy

Federated learning

MD5 to encrypt data

Data Loss Prevention API

6. You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention .

What should you do?

Normalize the data using Google Kubernetes Engine

Translate the normalization algorithm into SQL for use with BigQuery

Use the normalizer_fn argument in TensorFlow's Feature Column API

Normalize the data with Apache Spark using the Dataproc connector for BigQuery

7. Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics .

How should the data pipeline be designed?

1 = Dataflow, 2 = BigQuery

1 = Pub/Sub, 2 = Datastore

1 = Dataflow, 2 = Cloud SQL

1 = Cloud Function, 2 = Cloud SQL

8. Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model .

How should you build the classifier?

Use the Natural Language API to classify support requests

Use AutoML Natural Language to build the support requests classifier

Use an established text classification model on Al Platform to perform transfer learning

Use an established text classification model on Al Platform as-is to classify support requests

9. You are going to train a DNN regression model with Keras APIs using this code:

How many trainable weights does your model have? (The arithmetic below is correct.)

501*256+257*128+2 = 161154

500*256+256*128+128*2 = 161024

501*256+257*128+128*2=161408

500*256*0 25+256*128*0 25+128*2 = 40448

10. You work on a growing team of more than 50 data scientists who all use Al Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way .

Which strategy should you choose?

Set up restrictive I AM permissions on the Al Platform notebooks so that only a single user or group can access a given instance.

Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.

Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources

Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about Al Platform resource usage In BigQuery create a SQL view that maps users to the resources they are using.

Actual Professional Machine Learning Engineer Study Guides

Actual Professional Machine Learning Engineer Study Guides

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