100+ Free AWS ML Engineer Associate Practice Questions

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Question 1

Score: 0/0

Which Amazon SageMaker feature provides a centralized repository for storing, sharing, and discovering ML features for both training and real-time inference?

SageMaker Model Registry

SageMaker Feature Store

SageMaker Experiments

SageMaker Pipelines

to track

2026 Statistics

Key Facts: AWS ML Engineer Associate Exam

Exam Questions

AWS (50 scored + 15 unscored)

720/1000

Passing Score

AWS (scaled)

170 min

Exam Duration

AWS

$150

Exam Fee

AWS USD

28%

Data Preparation

Largest domain

3 years

Validity

AWS recertification

The AWS MLA-C01 exam has 65 questions (50 scored + 15 unscored) in 170 minutes with a passing score of 720/1000. Domains: Data Preparation for ML (28%), ML Model Development (26%), Deployment & Orchestration (22%), Monitoring, Maintenance & Security (24%). Released October 2024, valid 3 years. Exam fee is $150. Available at Pearson VUE or PSI testing centers and online proctored.

Sample AWS ML Engineer Associate Practice Questions

Try these sample questions to test your AWS ML Engineer Associate exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1Which Amazon SageMaker feature provides a centralized repository for storing, sharing, and discovering ML features for both training and real-time inference?

A.SageMaker Model Registry

B.SageMaker Feature Store

C.SageMaker Experiments

D.SageMaker Pipelines

Explanation: SageMaker Feature Store provides an online store (low-latency, key-value lookup for real-time inference) and an offline store (S3-backed, for training and batch use cases). It eliminates feature duplication between training and serving and prevents training/serving skew. The Model Registry catalogs trained models, Experiments tracks training runs, and Pipelines orchestrates ML workflows.

2An ML engineer needs to detect statistical bias in training data and explain individual predictions using SHAP values. Which SageMaker capability supports both requirements?

A.SageMaker Debugger

B.SageMaker Model Monitor

C.SageMaker Clarify

D.SageMaker Ground Truth

Explanation: SageMaker Clarify performs pre-training and post-training bias detection (e.g., class imbalance, DPL) and produces SHAP-based feature attributions for global and local explainability. Debugger captures training tensors for issue detection, Model Monitor watches deployed endpoints for drift, and Ground Truth is a data labeling service.

3Which SageMaker inference option is most cost-effective for sporadic, unpredictable traffic where cold-start latency is acceptable?

A.Real-time endpoints

B.Serverless inference

C.Batch transform

D.Asynchronous inference

Explanation: SageMaker Serverless Inference automatically provisions and scales compute on demand, charging only for the duration of inference. It is ideal for sporadic workloads where occasional cold starts are acceptable. Real-time endpoints have provisioned capacity (higher cost), batch transform is for offline jobs, and async inference handles long-running requests with large payloads.

4A team must process inference requests up to 1 GB in size with processing times of up to 60 minutes per request. Which SageMaker endpoint type is appropriate?

A.Real-time endpoint

B.Serverless inference

C.Asynchronous inference

D.Multi-model endpoint

Explanation: SageMaker Asynchronous Inference is designed for large payloads (up to 1 GB) and long processing times (up to 60 minutes). Requests are queued and clients receive notifications when results are ready in S3. Real-time endpoints have a 6 MB payload limit and 60-second timeout; serverless has similar limits; multi-model endpoints share hosting but use real-time semantics.

5Which AWS service provides a visual data preparation tool that lets analysts clean and normalize datasets with 250+ pre-built transformations and no code?

A.AWS Glue DataBrew

B.Amazon Athena

C.AWS Lake Formation

D.Amazon EMR

Explanation: 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 governs data lake security, and EMR runs Spark/Hadoop. DataBrew jobs can output cleaned data back to S3 for downstream ML training.

6An ML engineer wants to run hyperparameter tuning that adapts intelligently based on past trial results to minimize total trials needed. Which SageMaker tuning strategy should they choose?

A.Random search

B.Grid search

C.Bayesian optimization

D.Manual search

Explanation: SageMaker Automatic Model Tuning supports Bayesian optimization, which builds a probabilistic surrogate model of the objective function and uses past trial outcomes to select promising hyperparameter combinations next. This is more sample-efficient than random or grid search. SageMaker also supports Random, Hyperband, and Grid strategies.

7Which SageMaker tuning strategy uses early-stopping of underperforming trials to allocate budget to promising configurations and is best for deep learning?

A.Bayesian optimization

B.Random search

C.Hyperband

D.Grid search

Explanation: Hyperband is a multi-fidelity tuning strategy that runs many trials at low budget, kills the worst performers early, and promotes promising ones to higher budgets. It's especially effective for deep learning where each training epoch is expensive. Bayesian optimization doesn't terminate early; random/grid have no learning component.

8Which Amazon Bedrock feature lets you connect a foundation model to enterprise data sources for retrieval-augmented generation (RAG) without building your own ingestion pipeline?

A.Bedrock Agents

B.Bedrock Knowledge Bases

C.Bedrock Guardrails

D.Bedrock Studio

Explanation: Bedrock Knowledge Bases is a managed RAG service that ingests data from S3, chunks and embeds it, stores vectors in OpenSearch Serverless, Pinecone, Aurora pgvector, MongoDB, or Redis Enterprise, and handles retrieval at inference time. Agents orchestrate multi-step actions, Guardrails enforce safety policies, and Studio is a low-code playground.

9A binary classifier predicts fraud (positive class) on a highly imbalanced dataset. The business cares about catching as many fraudulent transactions as possible, even at the cost of false alarms. Which metric should be optimized?

A.Accuracy

B.Precision

C.Recall

D.Specificity

Explanation: Recall (sensitivity / true positive rate) measures the fraction of actual positives correctly identified — the right metric when missing a positive is more costly than false alarms. Precision penalizes false positives. Accuracy is misleading on imbalanced data. Specificity measures true negatives correctly identified.

10Which SageMaker capability tracks lineage across data, code, hyperparameters, and metrics for ML training runs to make experiments reproducible and comparable?

A.SageMaker Model Registry

B.SageMaker Experiments

C.SageMaker Pipelines

D.SageMaker Studio

Explanation: SageMaker Experiments organizes training runs into trials and trial components, automatically logging hyperparameters, metrics, datasets, and artifacts so engineers can compare runs side by side. The Model Registry catalogs deployable models, Pipelines define DAGs of steps, and Studio is the IDE.

About the AWS ML Engineer Associate Exam

The AWS Certified Machine Learning Engineer — Associate (MLA-C01) validates the skills to ingest and prepare data for ML, train and tune models, deploy solutions, and operate ML workflows on AWS. It covers Amazon SageMaker (Studio, Pipelines, Feature Store, Model Registry, Clarify, Model Monitor, JumpStart, HyperPod), Amazon Bedrock (foundation models, Knowledge Bases, Agents, Guardrails), AWS Glue, MLOps, and responsible AI.

Questions

65 scored questions

Time Limit

170 minutes

Passing Score

720/1000 (scaled)

Exam Fee

$150 (AWS / Pearson VUE or PSI)

AWS ML Engineer Associate Exam Content Outline

28%

Data Preparation for Machine Learning (ML)

Ingest and store data with S3, AWS Glue (DataBrew, ETL jobs, crawlers, Data Catalog, interactive sessions), Athena, Lake Formation; transform, cleanse, and engineer features; ensure data quality, integrity, and labeling with SageMaker Ground Truth

26%

ML Model Development

Choose modeling approaches; train models with SageMaker built-in algorithms, script mode, BYOC, and JumpStart; tune hyperparameters with Bayesian, Random, and Hyperband; evaluate with appropriate classification, regression, and ranking metrics; manage experiments and model versions

22%

Deployment and Orchestration of ML Workflows

Choose endpoint types (real-time, serverless, async, batch transform, multi-model, multi-container, edge); design auto-scaling and deployment strategies (blue/green, canary, shadow); orchestrate workflows with SageMaker Pipelines, Step Functions, EventBridge, and CodePipeline

24%

ML Solution Monitoring, Maintenance, and Security

Monitor inference and data quality with SageMaker Model Monitor and Clarify; set up CloudWatch metrics, logs, and alarms; secure ML resources with IAM, KMS, VPC endpoints, network isolation; manage cost and apply responsible AI practices including bias and explainability

How to Pass the AWS ML Engineer Associate Exam

What You Need to Know

Passing score: 720/1000 (scaled)
Exam length: 65 questions
Time limit: 170 minutes
Exam fee: $150

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

AWS ML Engineer Associate Study Tips from Top Performers

1Master Amazon SageMaker end-to-end: Studio, training jobs, Pipelines, Feature Store, Model Registry, Clarify, Model Monitor, and inference options

2Know each SageMaker endpoint type and when to choose real-time vs serverless vs async vs batch transform vs multi-model

3Understand AWS Glue components: DataBrew, ETL jobs, crawlers, Data Catalog, and interactive sessions for ML data prep

4Practice with Bedrock foundation models, Knowledge Bases for RAG, Agents with action groups, and Guardrails for safety

5Study MLOps patterns: SageMaker Pipelines, EventBridge triggers, CodePipeline, and quality gates with ConditionStep

6Review responsible AI: SageMaker Clarify (bias, SHAP explainability), Model Cards, and Bedrock Guardrails grounding checks

7Be ready for hyperparameter tuning strategies — Bayesian, Random, and Hyperband — and when each is best

Frequently Asked Questions

What is the AWS MLA-C01 exam?

The AWS Certified Machine Learning Engineer — Associate (MLA-C01) is an associate-level AWS certification, released in October 2024, that validates skills to build, deploy, and operate machine learning solutions on AWS. It covers data preparation, model development, deployment and orchestration, and monitoring, maintenance, and security.

How many questions are on the MLA-C01 exam?

The MLA-C01 exam contains 65 questions (50 scored and 15 unscored) delivered in 170 minutes. Question types include multiple choice, multiple response, ordering, and matching. The passing score is 720 out of a scaled 100-1000.

Are there prerequisites for the MLA-C01 exam?

There are no formal prerequisites, but AWS recommends at least 1 year of experience using SageMaker and other AWS services for ML engineering tasks, plus 1 year in a development, data engineering, or DevOps role. The certification is valid for 3 years.

What is the largest domain on the MLA-C01 exam?

Data Preparation for Machine Learning is the largest domain at 28%, closely followed by ML Model Development at 26%. Together they cover more than half the exam, so deep familiarity with AWS Glue, S3, SageMaker Feature Store, SageMaker training, and hyperparameter tuning is essential.

How should I prepare for the MLA-C01 exam?

Plan 60-100 hours of study over 6-10 weeks. Use the official AWS Skill Builder MLA-C01 exam-prep plan, practice in SageMaker Studio (training jobs, Pipelines, Feature Store, Model Monitor), explore Bedrock for generative AI, and complete 100+ practice questions. Aim for 80%+ on practice tests before scheduling.

What jobs can I get with the MLA-C01 certification?

MLA-C01 demonstrates production-ready ML engineering skills on AWS and supports roles such as Machine Learning Engineer, MLOps Engineer, Applied Scientist, AI/ML Solutions Architect, and Data Engineer specializing in ML. It pairs well with the AWS AI Practitioner foundational cert and the upcoming Specialty-level credentials.