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MLA-C01 AWS CERTIFIED MACHINE
LEARNING ENGINEER – ASSOCIATE
CERTIFICATION EXAM | STUDY GUIDE |
LATEST UPDATE 2026/2027 | 200+ PRACTICE
QUESTIONS AND ANSWERS | EXAM REVIEW
TABLE OF CONTENTS
1. Data Preparation for Machine Learning (28%)
2. ML Model Development (26%)
3. Deployment and Orchestration of ML Workflows (22%)
4. ML Solution Monitoring, Maintenance, and Security (24%)
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DOMAIN 1: DATA PREPARATION FOR MACHINE LEARNING (28%)
Question 1: A team has 1 TB of training data in Amazon S3 and runs SageMaker training
jobs. The job is read-bound. What feature optimizes I/O?
A) Use SageMaker File mode (download all data first)
B) Use SageMaker FastFile mode or Pipe mode (stream data, no full download)
C) Move data to Amazon EBS
D) Use smaller instances
Correct Answer: B) Use SageMaker FastFile mode or Pipe mode (stream data, no full
download)
File mode downloads everything before training (slow startup). FastFile / Pipe streams from
S3 during training, providing faster startup and lower disk needs. FastFile is the modern
default; Pipe is legacy.
Question 2: An ML engineer is preparing a tabular dataset with 150 features for a fraud-
classification model. Many features are highly correlated. Which SageMaker feature should
be used to identify and remove redundant features?
A) SageMaker Data Wrangler
B) SageMaker Feature Store
C) SageMaker Clarify
D) SageMaker Ground Truth
Correct Answer: A) SageMaker Data Wrangler
SageMaker Data Wrangler provides visual data preparation and feature engineering
capabilities, including correlation analysis and feature selection. Feature Store is for storing
and reusing features, Clarify is for bias detection, and Ground Truth is for data labeling.
Question 3: A company needs to ingest streaming clickstream data for real-time ML
inference. Which AWS service should be used?
A) AWS Glue
B) Amazon Kinesis Data Streams
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C) Amazon S3
D) Amazon RDS
Correct Answer: B) Amazon Kinesis Data Streams
Kinesis Data Streams is designed for real-time streaming data ingestion. Glue is for batch
ETL, S3 is object storage, and RDS is a relational database.
Question 4: An ML engineer is using SageMaker Feature Store. What is the primary purpose
of this service?
A) To store and serve features for training and inference
B) To train models
C) To deploy models
D) To monitor models
Correct Answer: A) To store and serve features for training and inference
SageMaker Feature Store provides a centralized repository for features, enabling feature
reuse across teams and ensuring consistency between training and inference.
Question 5: A team needs to label a large dataset of images for a computer vision model.
Which AWS service is most appropriate?
A) Amazon SageMaker Ground Truth
B) Amazon Rekognition
C) Amazon Textract
D) Amazon Comprehend
Correct Answer: A) Amazon SageMaker Ground Truth
Ground Truth provides data labeling services with human annotators and automated
labeling. Rekognition, Textract, and Comprehend are pre-built AI services, not labeling tools.
Question 6: What is the primary purpose of SageMaker Clarify?
A) To detect bias in training data and models
B) To label data
C) To train models
D) To deploy models
Correct Answer: A) To detect bias in training data and models
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SageMaker Clarify detects bias in training data and models, and explains model predictions.
It is a critical tool for responsible AI.
Question 7: An ML engineer needs to transform raw data into features for training. Which
AWS service provides a visual interface for data preparation?
A) AWS Glue DataBrew
B) Amazon SageMaker Studio
C) AWS Lambda
D) Amazon EMR
Correct Answer: A) AWS Glue DataBrew
Glue DataBrew provides a visual interface for data preparation, allowing users to clean and
transform data without writing code. It is designed for data analysts and data scientists.
Question 8: What is the purpose of SageMaker Data Wrangler?
A) To import, prepare, transform, and analyze data
B) To deploy models
C) To monitor models
D) To label data
Correct Answer: A) To import, prepare, transform, and analyze data
SageMaker Data Wrangler provides an end-to-end solution for data preparation, including
data import, transformation, analysis, and export.
Question 9: A company has sensitive PII data in S3. Which AWS service should be used to
scan for and classify sensitive data?
A) Amazon Macie
B) Amazon GuardDuty
C) AWS Shield
D) Amazon Inspector
Correct Answer: A) Amazon Macie
Macie uses machine learning to discover, classify, and protect sensitive data in S3.
GuardDuty is for threat detection, Shield is for DDoS protection, and Inspector is for
vulnerability assessment.
MLA-C01 AWS CERTIFIED MACHINE
LEARNING ENGINEER – ASSOCIATE
CERTIFICATION EXAM | STUDY GUIDE |
LATEST UPDATE 2026/2027 | 200+ PRACTICE
QUESTIONS AND ANSWERS | EXAM REVIEW
TABLE OF CONTENTS
1. Data Preparation for Machine Learning (28%)
2. ML Model Development (26%)
3. Deployment and Orchestration of ML Workflows (22%)
4. ML Solution Monitoring, Maintenance, and Security (24%)
, Page 2 of 71
DOMAIN 1: DATA PREPARATION FOR MACHINE LEARNING (28%)
Question 1: A team has 1 TB of training data in Amazon S3 and runs SageMaker training
jobs. The job is read-bound. What feature optimizes I/O?
A) Use SageMaker File mode (download all data first)
B) Use SageMaker FastFile mode or Pipe mode (stream data, no full download)
C) Move data to Amazon EBS
D) Use smaller instances
Correct Answer: B) Use SageMaker FastFile mode or Pipe mode (stream data, no full
download)
File mode downloads everything before training (slow startup). FastFile / Pipe streams from
S3 during training, providing faster startup and lower disk needs. FastFile is the modern
default; Pipe is legacy.
Question 2: An ML engineer is preparing a tabular dataset with 150 features for a fraud-
classification model. Many features are highly correlated. Which SageMaker feature should
be used to identify and remove redundant features?
A) SageMaker Data Wrangler
B) SageMaker Feature Store
C) SageMaker Clarify
D) SageMaker Ground Truth
Correct Answer: A) SageMaker Data Wrangler
SageMaker Data Wrangler provides visual data preparation and feature engineering
capabilities, including correlation analysis and feature selection. Feature Store is for storing
and reusing features, Clarify is for bias detection, and Ground Truth is for data labeling.
Question 3: A company needs to ingest streaming clickstream data for real-time ML
inference. Which AWS service should be used?
A) AWS Glue
B) Amazon Kinesis Data Streams
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C) Amazon S3
D) Amazon RDS
Correct Answer: B) Amazon Kinesis Data Streams
Kinesis Data Streams is designed for real-time streaming data ingestion. Glue is for batch
ETL, S3 is object storage, and RDS is a relational database.
Question 4: An ML engineer is using SageMaker Feature Store. What is the primary purpose
of this service?
A) To store and serve features for training and inference
B) To train models
C) To deploy models
D) To monitor models
Correct Answer: A) To store and serve features for training and inference
SageMaker Feature Store provides a centralized repository for features, enabling feature
reuse across teams and ensuring consistency between training and inference.
Question 5: A team needs to label a large dataset of images for a computer vision model.
Which AWS service is most appropriate?
A) Amazon SageMaker Ground Truth
B) Amazon Rekognition
C) Amazon Textract
D) Amazon Comprehend
Correct Answer: A) Amazon SageMaker Ground Truth
Ground Truth provides data labeling services with human annotators and automated
labeling. Rekognition, Textract, and Comprehend are pre-built AI services, not labeling tools.
Question 6: What is the primary purpose of SageMaker Clarify?
A) To detect bias in training data and models
B) To label data
C) To train models
D) To deploy models
Correct Answer: A) To detect bias in training data and models
, Page 4 of 71
SageMaker Clarify detects bias in training data and models, and explains model predictions.
It is a critical tool for responsible AI.
Question 7: An ML engineer needs to transform raw data into features for training. Which
AWS service provides a visual interface for data preparation?
A) AWS Glue DataBrew
B) Amazon SageMaker Studio
C) AWS Lambda
D) Amazon EMR
Correct Answer: A) AWS Glue DataBrew
Glue DataBrew provides a visual interface for data preparation, allowing users to clean and
transform data without writing code. It is designed for data analysts and data scientists.
Question 8: What is the purpose of SageMaker Data Wrangler?
A) To import, prepare, transform, and analyze data
B) To deploy models
C) To monitor models
D) To label data
Correct Answer: A) To import, prepare, transform, and analyze data
SageMaker Data Wrangler provides an end-to-end solution for data preparation, including
data import, transformation, analysis, and export.
Question 9: A company has sensitive PII data in S3. Which AWS service should be used to
scan for and classify sensitive data?
A) Amazon Macie
B) Amazon GuardDuty
C) AWS Shield
D) Amazon Inspector
Correct Answer: A) Amazon Macie
Macie uses machine learning to discover, classify, and protect sensitive data in S3.
GuardDuty is for threat detection, Shield is for DDoS protection, and Inspector is for
vulnerability assessment.
