A00-406 SAS Viya Supervised Machine Learning Pipelines Exam Free Dumps

dumps2024-05-09T11:14:40+08:00

A00-406 SAS Viya Supervised Machine Learning Pipelines exam is administered by SAS and Pearson VUE. There are 50-55 multiple choice and short-answer questions, and you have 90 minutes to answer all the questions. The required passing score for SAS Institute A00-406 exam is 62%. The certification is valid for 4 years. We provide the latest SAS Viya Supervised Machine Learning Pipelines A00-406 exam dumps, which can make sure you pass the test easily. Free SAS Viya Supervised Machine Learning Pipelines A00-406 exam dumps are available below.

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1. Refer to the exhibit below:

Based on the output from the Data Exploration node shown in the exhibit, which variable has the most thin tails (most platykurtic distribution)?

Logi_rfm4

Logi_rfm6

Logi_rfm8

Logi_rfm12

2. Given the following properties for a neural network model, which statement is true regrading hidden units in the model? The following SAS program is submitted:

There are no hidden units in the model.

The number of hidden units is 1.

The number of hidden units is 50.

The number of hidden units is 26.

3. Which of the following is an example of a NoSQL database that is commonly used to store unstructured data?

MySQL

MongoDB

Oracle Database

Microsoft SQL Server

4. What is the primary goal of A/B testing in the context of model deployment?

To evaluate the model's accuracy

To compare two different versions of a model or strategy to determine which performs better

To assess data quality

To create synthetic data

5. What does the term "bias" in machine learning refer to?

A model's inability to generalize to new data

Systematic errors that cause a model to consistently underpredict or overpredict

The simplicity of a model

The overall accuracy of a model

6. What is the significance of the "bias-variance trade-off" in machine learning?

It represents the trade-off between underfitting and overfitting.

It indicates the trade-off between accuracy and precision.

It refers to the trade-off between the number of features and the model's complexity.

It is not relevant in machine learning.

7. What is the purpose of cross-validation in model building and evaluation?

Splitting the dataset into training and testing sets

Reducing the dataset size

Assessing the model's generalization performance

Generating synthetic data

8. What is the purpose of a "canary release" in the context of model deployment?

To assess data quality

To deploy a new model version to a small subset of users or systems for testing

To create synthetic data

To evaluate model accuracy

9. When deploying a machine learning model, what is "model drift"?

A sudden increase in the model's accuracy

A change in the distribution of the input data or target variable over time

The process of feature extraction

A measure of feature importance

10. Which algorithm is commonly used for decision-making tasks in classification models?

K-Means

Decision Trees

Principal Component Analysis (PCA)

Linear Regression

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11. What is the primary purpose of model documentation in the model deployment phase?

To create synthetic data

To assess data quality

To provide information on the model's development, architecture, and usage

To evaluate the model's accuracy

12. Which of the following best describes unstructured data?

Data that is organized in rows and columns

Data that is difficult to process and lacks a predefined structure

Data stored in a relational database

Data with a clear schema

13. What is the main advantage of using a RESTful API (Representational State Transfer) as a data source?

Real-time data processing

Support for complex data structures

Simple and standardized communication

High security features

14. Which statements are true for the F1 score?

(Choose 2.)

F1 score is calculated based on a depth value.

F1 score is calculated based on a cut off value.

F1 score is applicable to a model with a binary target.

F1 score is applicable to a model with an interval target.

15. Which type of model is commonly used for anomaly detection in datasets?

Decision Trees

Clustering Models

Linear Regression

Principal Component Analysis (PCA)

16. What does API stand for in the context of data sources?

Application Programming Interface

Advanced Programming Integration

Automated Program Integration

Application Program Interface

17. Which data source allows for real-time data streaming and processing?

Data warehouses

Cloud storage

IoT devices

Static data files

18. What is the main advantage of ensemble methods in model building?

They require minimal data preprocessing

They produce simple and interpretable models

They combine multiple models to improve predictive performance

They work well with high-dimensional data

19. Which machine learning technique is typically used for building a model to predict a numeric target variable?

Classification

Regression

Clustering

Dimensionality reduction

20. Refer to the treemap shown in the exhibit below:

Which statement is true about the tree map for a decision tree with a binary target?

The top bar represents the node with the highest probability of event.

The darker bars represent nodes with a lower probability of event.

The top bar represents the node with the highest count.

The wider bars represent nodes with a higher probability of event.

Page 3 of 3

21. What is the primary purpose of model assessment in the context of data science and machine learning?

Data preprocessing

Model building

Evaluating and selecting the best-performing model

Data visualization

22. What is the main advantage of ensemble learning methods, such as Random Forest, in a machine learning pipeline?

They are simple and easy to interpret.

They are not suitable for large datasets.

They combine multiple models to improve predictive performance.

They require minimal data preprocessing.

23. When deploying a machine learning model, what is meant by "model latency"?

The time it takes to build a model

The time it takes to train a model

The time it takes for the model to make predictions once deployed

The time it takes to create synthetic data

24. What is the purpose of an ROC curve (Receiver Operating Characteristic) in model assessment?

To evaluate regression models

To visualize data distribution

To compare a model's true positive rate with the false positive rate

To measure feature importance

25. Which data source typically provides access to real-time financial market data?

Social media platforms

Weather stations

Stock market APIs

Online news websites

26. In model assessment, what does "cross-validation" aim to address?

Training a model

Overfitting and generalization

Data preprocessing

Model deployment

27. Which metric is commonly used to evaluate the performance of a regression model?

F1 Score

Mean Absolute Error (MAE)

Precision

Confusion Matrix

28. What is "model reevaluation" in the model deployment phase?

The process of data preprocessing

The process of selecting features

The periodic assessment of a deployed model's performance and potential retraining

The evaluation of data distribution

29. What is overfitting in machine learning, and how can it be addressed in a pipeline?

Overfitting occurs when the model is too simple and underperforms.

Overfitting occurs when the model fits the training data too closely and may not generalize well. It can be addressed by regularization techniques.

Overfitting occurs when the model is too complex and overperforms.

Overfitting is not a concern in machine learning pipelines.

30. What is a data lake?

A data storage solution designed for high-speed data retrieval

A centralized repository for storing all structured and unstructured data at any scale

A specialized database for time-series data

A backup system for relational databases

A00-406 SAS Viya Supervised Machine Learning Pipelines Exam Free Dumps

A00-406 SAS Viya Supervised Machine Learning Pipelines Exam Free Dumps

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