Aws Certified Machine Learning Engineer Associate Mla C01 Practice Test - Set 1

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AWS Certified Machine Learning Engineer – Associate (MLA-C01) - Exam Information

Exam Information

Exam Code

Aws Certified Machine Learning Engineer Associate Mla C01

Exam Title

AWS Certified Machine Learning Engineer – Associate (MLA-C01)

Vendor

AWS

Difficulty

Intermediate

Duration

130 Minutes

Question Format

Multiple Choice

Last Updated

March 10, 2025

The AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam tests ML engineering skills on AWS.

Practice Test

Shop Best AWS Certified Machine Learning Engineer – Associate (MLA-C01) Resources Worldwide Amazon

1. Which feature of Amazon SageMaker enables automatic hyperparameter tuning for machine learning models?

SageMaker Ground Truth
SageMaker Autopilot
SageMaker Hyperparameter Optimization
SageMaker Neo

2. What is the primary use case for Amazon SageMaker Ground Truth?

Labeling data for machine learning
Building machine learning models
Deploying machine learning models
Monitoring machine learning models

3. Which AWS service is used for building, training, and deploying machine learning models?

Amazon SageMaker
Amazon Rekognition
AWS DeepLens
AWS Lambda

4. What is the purpose of Amazon SageMaker Autopilot?

Automate machine learning model creation
Label data for machine learning
Deploy machine learning models
Monitor machine learning models

5. Which AWS service is used for real-time machine learning inference?

Amazon SageMaker Endpoint
Amazon Rekognition
AWS DeepLens
AWS Lambda

6. What is the primary use case for Amazon SageMaker Neo?

Optimize machine learning models for deployment
Build machine learning models
Label data for machine learning
Monitor machine learning models

7. Which AWS service is used for monitoring machine learning models in production?

Amazon SageMaker Model Monitor
Amazon CloudWatch
AWS Lambda
AWS Glue

8. What is the purpose of Amazon SageMaker Experiments?

Track and compare machine learning experiments
Label data for machine learning
Deploy machine learning models
Monitor machine learning models

9. Which AWS service is used for feature engineering in machine learning?

Amazon SageMaker Feature Store
Amazon Rekognition
AWS DeepLens
AWS Lambda

10. What is the primary use case for Amazon SageMaker Debugger?

Debug and profile machine learning models
Label data for machine learning
Deploy machine learning models
Monitor machine learning models

11. Which AWS service is used for building and deploying conversational AI applications?

Amazon Lex
Amazon Polly
Amazon Comprehend
Amazon Rekognition

12. What is the purpose of Amazon SageMaker Pipelines?

Automate and manage machine learning workflows
Label data for machine learning
Deploy machine learning models
Monitor machine learning models

13. Which AWS service is used for real-time speech recognition?

Amazon Transcribe
Amazon Polly
Amazon Comprehend
Amazon Rekognition

14. What is the primary use case for Amazon SageMaker Clarify?

Explain and interpret machine learning models
Label data for machine learning
Deploy machine learning models
Monitor machine learning models

15. Which AWS service is used for natural language processing (NLP)?

Amazon Comprehend
Amazon SageMaker
AWS Lambda
AWS Glue

16. What is the purpose of Amazon SageMaker Edge Manager?

Manage machine learning models on edge devices
Label data for machine learning
Deploy machine learning models
Monitor machine learning models

17. Which AWS service is used for text-to-speech conversion?

Amazon Polly
Amazon Comprehend
Amazon Transcribe
Amazon Rekognition

18. What is the primary use case for Amazon SageMaker Data Wrangler?

Prepare and clean data for machine learning
Label data for machine learning
Deploy machine learning models
Monitor machine learning models

19. Which AWS service is used for image and video analysis?

Amazon Rekognition
Amazon SageMaker
AWS DeepLens
AWS Lambda

20. What is the purpose of Amazon SageMaker JumpStart?

Quickly start machine learning projects with pre-built solutions
Label data for machine learning
Deploy machine learning models
Monitor machine learning models

21. Which Amazon SageMaker feature automatically tunes hyperparameters for machine learning models?

SageMaker Autopilot
SageMaker Hyperparameter Optimization
SageMaker Ground Truth
SageMaker Neo

22. What is the maximum number of training jobs that can run concurrently in Amazon SageMaker?

10
20
50
100

23. Which AWS service is used for natural language processing (NLP) in machine learning workflows?

Amazon Comprehend
Amazon SageMaker
AWS Lambda
AWS Glue

24. What is the purpose of Amazon SageMaker Ground Truth?

To label data for machine learning
To build machine learning models
To deploy machine learning models
To monitor machine learning models

25. Which Amazon SageMaker feature provides pre-built machine learning solutions?

SageMaker JumpStart
SageMaker Autopilot
SageMaker Ground Truth
SageMaker Neo

26. What is the maximum size of a single training dataset in Amazon SageMaker?

1 TB
5 TB
10 TB
No limit

27. Which AWS service is used for building conversational interfaces in machine learning applications?

Amazon Lex
Amazon Polly
Amazon Comprehend
Amazon Rekognition

28. What is the purpose of Amazon SageMaker Autopilot?

To automate machine learning model creation
To label data for machine learning
To deploy machine learning models
To monitor machine learning models

29. Which Amazon SageMaker feature optimizes models for deployment on specific hardware?

SageMaker Neo
SageMaker Autopilot
SageMaker Ground Truth
SageMaker JumpStart

30. What is the maximum number of endpoints that can be created in Amazon SageMaker?

50
100
200
500

31. Which AWS service is used for time-series forecasting in machine learning workflows?

Amazon Forecast
Amazon SageMaker
AWS DeepLens
AWS Lambda

32. What is the purpose of Amazon SageMaker Experiments?

To track and compare machine learning experiments
To label data for machine learning
To deploy machine learning models
To monitor machine learning models

33. Which Amazon SageMaker feature provides an integrated development environment for ML?

SageMaker Studio
SageMaker Autopilot
SageMaker Ground Truth
SageMaker Neo

34. What is the maximum number of models that can be stored in Amazon SageMaker?

100
200
500
1000

35. Which AWS service is used for personalized recommendations in machine learning applications?

Amazon Personalize
Amazon SageMaker
AWS DeepLens
AWS Lambda

36. What is the purpose of Amazon SageMaker Debugger?

To debug and profile machine learning models
To label data for machine learning
To deploy machine learning models
To monitor machine learning models

37. Which Amazon SageMaker feature provides a centralized repository for ML features?

SageMaker Feature Store
SageMaker Autopilot
SageMaker Ground Truth
SageMaker Neo

38. What is the maximum number of training jobs that can be stored in Amazon SageMaker?

100
200
500
1000

39. Which AWS service is used for explainability in machine learning models?

Amazon SageMaker Clarify
Amazon Comprehend
Amazon Transcribe
Amazon Rekognition

40. What is the purpose of Amazon SageMaker Model Monitor?

To monitor machine learning models in production
To label data for machine learning
To deploy machine learning models
To build machine learning models

41. Which Amazon SageMaker feature helps with data preparation for ML?

SageMaker Data Wrangler
SageMaker Autopilot
SageMaker Ground Truth
SageMaker Neo

42. What is the maximum number of experiments that can be stored in Amazon SageMaker?

100
200
500
1000

43. Which AWS service is used for building and deploying conversational AI applications?

Amazon Lex
Amazon Polly
Amazon Comprehend
Amazon Rekognition

44. What is the purpose of Amazon SageMaker Pipelines?

To automate and manage machine learning workflows
To label data for machine learning
To deploy machine learning models
To monitor machine learning models

45. Which Amazon SageMaker feature manages models on edge devices?

SageMaker Edge Manager
SageMaker Autopilot
SageMaker Ground Truth
SageMaker Neo

46. What is the maximum number of model monitors that can be created in Amazon SageMaker?

50
100
200
500

47. Which AWS service is used for human review of ML predictions?

Amazon Augmented AI (A2I)
Amazon SageMaker
AWS DeepLens
AWS Lambda

48. What is the purpose of Amazon SageMaker Model Registry?

To catalog and manage machine learning models
To label data for machine learning
To deploy machine learning models
To monitor machine learning models

49. Which Amazon SageMaker feature provides pre-built machine learning algorithms?

SageMaker Built-in Algorithms
SageMaker Autopilot
SageMaker Ground Truth
SageMaker Neo

50. What is the maximum number of feature groups that can be stored in Amazon SageMaker Feature Store?

50
100
200
500

51. Which AWS service is used for document text extraction in machine learning workflows?

Amazon Textract
Amazon Rekognition
Amazon Comprehend
Amazon Transcribe

52. What is the purpose of Amazon SageMaker Processing Jobs?

To run data processing workloads
To label data for machine learning
To deploy machine learning models
To monitor machine learning models

53. Which Amazon SageMaker feature provides managed spot training for ML models?

SageMaker Managed Spot Training
SageMaker Autopilot
SageMaker Ground Truth
SageMaker Neo

54. What is the maximum number of pipelines that can be stored in Amazon SageMaker?

50
100
200
500

55. Which AWS service is used for anomaly detection in machine learning workflows?

Amazon Lookout for Metrics
Amazon SageMaker
AWS DeepLens
AWS Lambda

56. What is the purpose of Amazon SageMaker Batch Transform?

To process large datasets with trained models
To label data for machine learning
To deploy machine learning models
To monitor machine learning models

57. Which Amazon SageMaker feature provides distributed training for ML models?

SageMaker Distributed Training
SageMaker Autopilot
SageMaker Ground Truth
SageMaker Neo

58. What is the maximum number of human review workflows that can be created in Amazon A2I?

50
100
200
500

59. Which AWS service is used for building and deploying custom machine learning models?

Amazon SageMaker
Amazon Rekognition
AWS DeepLens
AWS Lambda

60. What is the purpose of Amazon SageMaker Model Registry?

To catalog and manage machine learning models
To label data for machine learning
To deploy machine learning models
To monitor machine learning models

The Aws Certified Machine Learning Engineer Associate Mla C01 certification is a globally recognized credential for IT professionals. This practice test helps you prepare by covering key topics like hardware, networking, troubleshooting, and security.

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