MLA-C01 — AWS Certified Machine Learning Engineer - Associate
Amazon

AWS Certified Machine Learning Engineer - Associate (MLA-C01) Practice Questions

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115 questions
June 14, 2026 updated
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Domain coverage

  • Data Preparation for Machine Learning (ML) (28%)
  • ML Model Development (26%)
  • Deployment and Orchestration of ML Workflows (22%)
  • ML Solution Monitoring, Maintenance, and Security (24%)

Sample Questions (12 of 115 shown)

Q1 Data Preparation for Machine Learning (28%)
Which Amazon SageMaker feature provides a centralized repository for storing, sharing, and discovering ML features for both training and real-time inference?
  1. SageMaker Model Registry
  2. SageMaker Feature Store
  3. SageMaker Experiments
  4. SageMaker Pipelines
✓ Correct Answer: B
SageMaker Feature Store provides an online store (low-latency key-value lookup) for real-time inference and an offline store (S3-based) for training and batch use. It eliminates feature duplication between training and serving, and prevents training/serving skew. Model Registry catalogs trained models, Experiments tracks training runs, and Pipelines orchestrates ML workflows.
Q2 Data Preparation for Machine Learning (28%)
Which AWS service provides a visual data preparation tool that lets analysts clean and normalize datasets with 250+ pre-built transformations and no code?
  1. AWS Glue DataBrew
  2. Amazon Athena
  3. AWS Lake Formation
  4. Amazon EMR
✓ Correct Answer: A
AWS Glue DataBrew is a visual, no-code data preparation service offering 250+ transformations (filtering, normalization, encoding, missing value imputation). Athena runs serverless SQL on S3, Lake Formation manages data lake security, and EMR runs Spark/Hadoop. DataBrew jobs can output cleaned data back to S3 for downstream ML training.
Q3 Data Preparation for Machine Learning (28%)
An ML engineer needs to detect statistical bias in training data and explain individual predictions using SHAP values. Which SageMaker capability supports both requirements?
  1. SageMaker Debugger
  2. SageMaker Model Monitor
  3. SageMaker Clarify
  4. SageMaker Ground Truth
✓ Correct Answer: C
SageMaker Clarify performs pre-training and post-training bias detection (e.g., class imbalance, DPL) and generates SHAP-based global and local explainability feature attributions. Debugger captures training tensors for issue detection, Model Monitor monitors deployed endpoints for drift, and Ground Truth is a data labeling service.
Q4 Data Preparation for Machine Learning (28%)
An ML engineer is configuring a SageMaker Clarify processing job to compute pre-training bias metrics on a new customer dataset, such as Difference in Positive Proportions in Labels (DPL) and Class Imbalance (CI). Which two parameters must be specified in the analysis configuration for the bias analysis to run correctly? (Select TWO.)
  1. Dataset column headers
  2. The facet configuration identifying the sensitive attribute columns
  3. The label configuration identifying the target variable column
  4. The predictor configuration pointing to the trained model endpoint
  5. The group_variable for Conditional Demographic Disparity (CDD) metrics
✓ Correct Answer: B, C
For pre-training bias metrics, SageMaker Clarify needs to know which columns are sensitive attributes (facet configuration) and which column is the target variable (label configuration) to analyze differences in label distribution across groups. The predictor configuration is for post-training bias and explainability. Group_variable is for CDD specifically, not required for DPL and CI.
Q5 Data Preparation for Machine Learning (28%)
A company needs to ensure that training datasets containing PII are only stored and processed in the eu-west-1 region. Which action best reflects the core principle of keeping data restricted to approved regions and storage locations?
  1. Create CloudTrail alerts for S3 and SageMaker in other regions.
  2. Remove PII columns before storing data for feature engineering.
  3. Encrypt S3 data using a KMS key created in eu-west-1.
  4. Use SCPs to deny regions not on the approved list and allow only a set of approved S3 buckets.
✓ Correct Answer: D
The most appropriate approach is a whitelist strategy: explicitly allow only approved regions and specific S3 locations using SCPs (Service Control Policies) via AWS Organizations. This prevents accidental non-compliant storage or processing in unapproved regions.
Q6 Data Preparation for Machine Learning (28%)
A team needs to extract printed text from scanned loan applications (OCR). The team does not need custom fields beyond standard form and table extraction. Which action best applies the core principle of data minimization while meeting the requirements on AWS?
  1. Use Amazon Textract and store only the extracted text output
  2. Use Amazon Rekognition Custom Labels for text extraction
  3. Run open-source OCR on EC2 and archive all original images
  4. Train a custom OCR model in SageMaker and retain all scans
✓ Correct Answer: A
Amazon Textract is the AWS managed service designed specifically for OCR on scanned documents, forms, and tables. Data minimization means collecting and retaining only the data needed for the stated purpose. Storing only the extracted text (not the original scans) best aligns with this principle.
Q7 Data Preparation for Machine Learning (28%)
A team has existing producers and consumers that use the Apache Kafka protocol and client libraries. The team wants to use a fully managed AWS service to ingest high-throughput streams with minimal application changes, while preserving Kafka concepts such as topics and partitions. Which AWS service should the team use?
  1. Amazon Managed Streaming for Apache Kafka (Amazon MSK)
  2. Amazon Kinesis Data Streams
  3. Amazon Kinesis Data Firehose
  4. Amazon SageMaker Feature Store
✓ Correct Answer: A
Amazon MSK is the fully managed AWS service for running Apache Kafka, allowing teams to ingest stream data at scale while retaining the Kafka API and client libraries. This directly meets the requirement of preserving Kafka-specific constructs like topics and partitions with minimal application changes.
Q8 Data Preparation for Machine Learning (28%)
A team is preparing a 5 TB image dataset for distributed training on Amazon SageMaker. The team wants to maximize training throughput and minimize data loading bottlenecks. Which statement about preparing the training data so the training infrastructure can load it efficiently is INCORRECT?
  1. Compress files when the training job is not CPU-bound on decompression
  2. Upload a single large file per epoch to reduce S3 GET overhead
  3. Store many small objects to maximize S3 request parallelism
  4. Shard the dataset into multiple medium-sized files per channel
✓ Correct Answer: B
Efficient SageMaker training input typically comes from balanced sharding: enough files to allow parallel reads without pathological small-object overhead. A single very large file forces serial or limited parallel reads and makes retries and partial re-reads expensive, which can degrade overall training throughput.
Q9 Data Preparation for Machine Learning (28%)
A team is preparing training data for an XGBoost model on Amazon SageMaker using tabular data. The team uses a mix of transformations (imputation, one-hot encoding, and numeric scaling) and must ensure feature consistency between training and inference across future model versions. Which method should the team NOT use?
  1. Use the same preprocessing script in both a SageMaker Processing step and the inference container
  2. Store transformation artifacts (e.g., encoder/scaler) from training and load them at inference time
  3. Manually re-create the transformations in inference code to match training
  4. Register feature definitions and transformations and reuse them through SageMaker Feature Store
✓ Correct Answer: C
To ensure feature consistency, transformation logic and feature definitions should be reusable and applied consistently in both training and inference. Manually reimplementing preprocessing for inference creates two sources of truth, making it easy for the online path to diverge from the training pipeline as code and schemas evolve.
Q10 Data Preparation for Machine Learning (28%)
A company uses a hybrid cloud environment. A model deployed on-premises uses data from Amazon S3 to serve a real-time conversation engine for customers. The model uses sensitive data. An ML engineer needs to implement a solution to identify and remove sensitive data. Which solution meets these requirements with the LEAST management overhead?
  1. Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove sensitive data.
  2. Deploy the model on an Amazon ECS cluster with AWS Fargate. Create an AWS Batch job to identify and remove sensitive data.
  3. Use Amazon Macie to identify sensitive data. Create a set of AWS Lambda functions to remove sensitive data.
  4. Use Amazon Comprehend to identify sensitive data. Launch Amazon EC2 instances to remove sensitive data.
✓ Correct Answer: C
Amazon Macie is a fully managed data security service that uses ML and pattern matching to automatically discover, classify, and protect sensitive data in AWS. It is best suited for identifying sensitive data with minimal effort. Lambda functions can then process and remove the identified data without managing servers.
Q11 Data Preparation for Machine Learning (28%)
An ML engineer is preparing a dataset for a sales forecasting model. The dataset has a daily_revenue feature with several extreme values due to promotional campaigns. These values are legitimate business data but cause model instability during training. The engineer wants to mitigate the impact of these extreme values without deleting the records. Which data transformation technique should the engineer apply?
  1. Apply winsorization to clip extreme values to specified percentiles
  2. Delete all records containing extreme values
  3. Impute extreme values using the feature's mean
  4. Normalize the feature using Min-Max scaling
✓ Correct Answer: A
Winsorization replaces values beyond specified percentiles (e.g., 1st and 99th) with those percentile values, effectively limiting the impact of extremes without deleting data points. This matches both requirements: "do not delete records" and "mitigate extreme value impact."
Q12 Data Preparation for Machine Learning (28%)
An ML engineer uses min-max normalization on training data in AWS Glue DataBrew. Before passing production inference data to the model for predictions, the engineer must normalize the production data the same way as the training data. Which solution meets this requirement?
  1. Apply statistics from a well-known dataset to normalize production samples
  2. Retain the min-max normalization statistics from the training set. Use these values to normalize production samples.
  3. Compute a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all production samples.
  4. Compute a new set of min-max normalization statistics from each production sample. Use these values to normalize all production samples.
✓ Correct Answer: B
The key principle is data preprocessing consistency. The same statistics (min and max values) computed from the training set must be reused to normalize production data. This ensures the model sees the same data distribution in production as during training. Recomputing statistics from production data (C, D) would change the distribution and lead to unstable predictions.

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Frequently Asked Questions

The key difference is focus and audience. MLS-C01 (Machine Learning - Specialty) is heavily focused on data science and algorithm understanding — it tests deep knowledge of ML algorithms, feature engineering theory, and statistical concepts. MLA-C01 is focused on ML engineering and operations — it tests your ability to build, deploy, and maintain ML systems using SageMaker and related AWS services. Think of MLS-C01 as the what and why of ML, while MLA-C01 is the how of putting ML into production. If you're a hands-on engineer building MLOps pipelines, MLA-C01 is the more relevant certification.

Yes, SageMaker is the central service across all four domains. You need practical knowledge of SageMaker Studio, Data Wrangler, Processing jobs, Autopilot, Hyperparameter Tuning, Experiments, Model Registry, Pipelines, endpoints (real-time, serverless, batch), Model Monitor, Clarify, Edge Manager, and Feature Store. Beyond SageMaker, expect questions on supporting data services (S3, Glue, Athena, Kinesis, Step Functions), CI/CD (CodePipeline, CodeBuild), monitoring (CloudWatch), and security (IAM, KMS, VPCs). Understanding how these services integrate into end-to-end ML pipelines is essential.

The MLA-C01 features four question types: (1) Multiple choice — one correct answer from four options; (2) Multiple response — select two or more correct answers from five or more options; (3) Ordering — arrange 3-5 steps in the correct sequence for a specified ML task (e.g., the correct order of steps in a SageMaker pipeline); (4) Matching — pair items from two lists (e.g., match ML algorithms to their appropriate use cases). The ordering and matching types are unique to this exam among AWS Associate certifications, requiring deeper procedural knowledge.

Our product delivers 400+ practice questions organized across all four domains with correct weightings, using all four exam question types (multiple choice, multiple response, ordering, matching). Each question includes detailed explanations that break down the correct answer and explain why each distractor is wrong — building the practical MLOps thinking you need. You get full-length simulation exams (65 questions, 130 minutes), domain-wise practice modules, and a downloadable PDF study guide with SageMaker workflow diagrams, model deployment comparison tables, and MLOps pipeline architecture blueprints.

Study time varies by background. ML engineers with 1+ year of SageMaker experience typically need 6-8 weeks (80-100 hours). Those with general ML knowledge but new to SageMaker may need 10-12 weeks (120-150 hours). We recommend: (1) Review the official exam guide and in-scope services list; (2) Complete AWS Skill Builder's MLA-C01 Exam Prep course; (3) Build hands-on experience with SageMaker (especially Studio, Pipelines, Model Monitor); (4) Use our practice tests for domain-wise assessment across all four question types; (5) Take full-length simulation exams to build time management; (6) Focus final review on weaker domains.

Yes, MLOps is a major focus spanning multiple domains. Domain 3 (Deployment and Orchestration, 22%) directly tests CI/CD for ML using SageMaker Pipelines, CodePipeline, and Step Functions. Domain 4 (Monitoring, Maintenance, and Security, 24%) tests model monitoring, drift detection, and automated retraining — the operational side of MLOps. Combined, 46% of the exam content relates to operationalizing ML. You should understand ML pipeline orchestration, model versioning, canary deployments, A/B testing for models, automated retraining triggers, and infrastructure as code for ML environments.

The MLA-C01 is one of the most strategically valuable AWS certifications for the AI era. As organizations move ML projects from experimentation to production, the demand for ML engineers who can operationalize models skyrockets. This certification positions you for roles like ML Engineer, MLOps Engineer, AI/ML Developer, and Data Science Engineer. It serves as the associate-level ML certification that pairs perfectly with AIP-C01 (Generative AI Developer Professional) for those specializing in AI/ML. Combined with DEA-C01 (Data Engineer Associate), you demonstrate end-to-end data-to-ML pipeline expertise — a highly sought-after combination in today's job market.

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