AWS ML Workflow Automation Quiz

1. What does SageMaker Model Monitor primarily track in production models?

Data drift and model quality degradation

Only inference latency

Training dataset size only

Cloud storage costs

2. What is the primary advantage of using SageMaker Pipelines over manual workflow orchestration?

Reduced complexity, reproducibility, and automated scheduling of ML workflows

Lower storage costs only

Faster data ingestion

Better model accuracy guaranteed

3. True or False: SageMaker Clarify helps explain model predictions and detect bias in ML workflows.

True

False

4. Which service integrates with SageMaker to enable continuous training pipelines triggered by new data?

AWS EventBridge and Lambda

Amazon CloudWatch only

AWS CloudFormation

Amazon SNS

5. What does Amazon SageMaker Canvas allow non-technical users to do?

Build and deploy ML models without coding

Write custom training algorithms

Manage AWS infrastructure

Monitor cloud costs only

6. True or False: SageMaker supports automatic model versioning and rollback strategies.

True

False

7. Which SageMaker component manages multi-step dependencies and conditional logic in pipelines?

Pipeline Parameters and Conditional Steps

Training Jobs only

Processing Jobs

Model Endpoints

8. What is the primary use case for SageMaker Batch Transform in ML workflows?

Processing large datasets without real-time requirements

Training models in parallel

Real-time single prediction serving

Feature extraction only

9. True or False: SageMaker Autopilot automatically selects algorithms and tunes hyperparameters for tabular data.

True

False

10. Which SageMaker feature enables automated model retraining when data drift is detected?

Model Monitor with EventBridge integration

Processing Jobs only

Batch Transform

Endpoint Auto Scaling

11. Which AWS service is primarily used to build, train, and deploy ML models at scale?

Amazon SageMaker

AWS Lambda

Amazon EC2

AWS Glue

12. True or False: AWS Step Functions can be used to orchestrate SageMaker pipeline executions.

True

False

13. Which deployment option allows real-time predictions with low latency in SageMaker?

Batch transform

Real-time endpoints

Asynchronous endpoints

Lambda inference

14. What is the primary benefit of using SageMaker Feature Store in ML workflows?

Centralized feature storage and reusability across models

Automatic model training without data

Direct integration with data warehouses only

Real-time model inference only

15. True or False: SageMaker Model Registry tracks model versions and metadata automatically.

True

False

16. Which AWS service is commonly used alongside SageMaker for data extraction and transformation?

AWS Glue

Amazon Kinesis

AWS Step Functions

Amazon S3 Select

17. What does hyperparameter tuning in SageMaker use to find optimal model parameters?

Bayesian optimization and grid search

Random forest ensemble methods

Gradient descent only

Manual parameter specification

18. True or False: SageMaker Pipelines automatically scales compute resources based on data size.

True

False

19. Which SageMaker component handles data preprocessing and feature engineering in pipelines?

Processing Step

Training Step

Transform Step

Tuning Step

20. What is the main purpose of SageMaker Pipelines in ML workflow automation?

To store ML model artifacts

To orchestrate multi-step ML workflows with dependencies

To monitor model performance metrics

To encrypt sensitive training data

AWS ML Engineer ML Workflow Automation Quiz

1. What does SageMaker Model Monitor primarily track in production models?

2.

What first name or nickname would you like us to use?

2. What is the primary advantage of using SageMaker Pipelines over manual workflow orchestration?

3. True or False: SageMaker Clarify helps explain model predictions and detect bias in ML workflows.

4. Which service integrates with SageMaker to enable continuous training pipelines triggered by new data?

5. What does Amazon SageMaker Canvas allow non-technical users to do?

6. True or False: SageMaker supports automatic model versioning and rollback strategies.

7. Which SageMaker component manages multi-step dependencies and conditional logic in pipelines?

8. What is the primary use case for SageMaker Batch Transform in ML workflows?

9. True or False: SageMaker Autopilot automatically selects algorithms and tunes hyperparameters for tabular data.

10. Which SageMaker feature enables automated model retraining when data drift is detected?

11. Which AWS service is primarily used to build, train, and deploy ML models at scale?

12. True or False: AWS Step Functions can be used to orchestrate SageMaker pipeline executions.

13. Which deployment option allows real-time predictions with low latency in SageMaker?

14. What is the primary benefit of using SageMaker Feature Store in ML workflows?

15. True or False: SageMaker Model Registry tracks model versions and metadata automatically.

16. Which AWS service is commonly used alongside SageMaker for data extraction and transformation?

17. What does hyperparameter tuning in SageMaker use to find optimal model parameters?

18. True or False: SageMaker Pipelines automatically scales compute resources based on data size.

19. Which SageMaker component handles data preprocessing and feature engineering in pipelines?

20. What is the main purpose of SageMaker Pipelines in ML workflow automation?