AWS Certified Machine Learning (MLA-C01)

Description

Are you preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam and looking for comprehensive, exam-focused practice tests to pass on your first attempt?

This course offers 6 full-length mock exams with over 390 questions, carefully designed to simulate the real AWS exam environment and reinforce your knowledge of machine learning engineering on AWS.

These AWS Certified Machine Learning Engineer Practice Exams mirror the latest MLA-C01 exam blueprint, ensuring complete coverage of all four domains — Data Preparation, Model Development, Deployment & Orchestration, and ML Monitoring & Security.

Each question is crafted to test your practical understanding of ML model building, automation, deployment, and maintenance using AWS services like Amazon SageMaker, Glue, DataBrew, CloudFormation, Step Functions, and Bedrock.

With detailed explanations for every question, this course not only identifies your weak areas but also deepens your conceptual clarity of ML pipelines, MLOps, data transformation, CI/CD, and monitoring best practices.

Whether you’re a data scientist, ML engineer, or cloud developer, these mock exams provide everything you need to build confidence and master AWS ML engineering concepts for the MLA-C01 certification.

Comprehensive Coverage

This course is ideal for machine learning practitioners, developers, data engineers, and DevOps professionals seeking to operationalize, automate, and deploy ML solutions on AWS.

The mock tests cover:

Data Preparation for ML (28%) – Data ingestion, cleaning, transformation, feature engineering, bias detection, and handling data formats (Parquet, JSON, CSV, Avro).
Model Development (26%) – Algorithm selection, SageMaker built-in algorithms, hyperparameter tuning, model evaluation, and versioning using Model Registry.
Deployment & Orchestration (22%) – SageMaker endpoints, batch inference, IaC with CloudFormation and CDK, containerization (ECR, ECS, EKS), and CI/CD automation.
Monitoring, Maintenance & Security (24%) – Drift detection, model monitoring, cost optimization, IAM policies, network security, and auditing with CloudTrail.

You’ll gain complete familiarity with core AWS ML services including SageMaker, Bedrock, Glue, DataBrew, Lambda, CloudWatch, CloudFormation, CodePipeline, Step Functions, and Model Monitor.

Why This AWS Certified Machine Learning Engineer – Associate Practice Exam Course is Unique

6 Full-Length Mock Exams: Total 390 questions, reflecting the real AIF-C01 exam structure.
100% Syllabus Coverage: Covers all AIF-C01 domains, from AI fundamentals to Generative AI, including AWS services, AI ethics, and business use cases.
Diverse Question Categories: Prepares you across multiple knowledge and application levels:
- Ordering questions: Sequence AWS AI workflows and ML processes correctly.
- Scenario questions: Apply AI and ML concepts to practical business situations.
- AWS service-based questions: Map the right AWS service to the correct AI/ML task.
- Matching questions: Connect concepts, services, or data workflows accurately.
- Case study questions: Analyze real-world examples of AI deployments on AWS.
- Concept-based questions: Test theoretical knowledge of AI, ML, and Generative AI principles.
Real Exam-Like Format: Multiple-choice and multiple-response questions designed to simulate timing, format, and difficulty.
Comprehensive Explanations: Each question includes rationales for all answer options.
Latest Syllabus Alignment: Fully updated with 2025 AWS Certified Machine Learning Engineer – Associate exam objectives.
Every Question Mapped to Domains: Helps track coverage and focus preparation strategically.
Scenario-Based & Practical Questions: Real-world examples replicate challenges you’ll encounter on the exam and in AI deployments.
Exam Weightage Distribution: Questions follow official domain weightage for optimized preparation.
Timed Practice: Simulate real exam durations to develop time management skills.
Ideal for IT & Non-IT Professionals: Build AI literacy and practical AWS AI skills across job roles.
Randomized Question Bank: Prevent memorization and encourage active problem-solving.
Performance Analytics: Receive insights into strengths and weaknesses across AI domains.
Practical, Real-World Application: Reinforce learning through applied scenarios, case studies, and problem-solving questions.

Exam Details

Exam Body: Amazon Web Services (AWS)
Exam Name: AWS Certified Machine Learning Engineer – Associate (AIF-C01)
Prerequisite Certification: None
Recommended Experience: Up to 6 months of exposure to AI/ML technologies on AWS
Exam Format: Multiple Choice, Multiple Response, Ordering, Matching, and Case Study questions
Certification Validity: Three years (requires recertification)
Number of Questions: 65 (50 scored + 15 unscored)
Passing Score: 700 (on a scaled score of 100-1000)
Exam Duration: 130 minutes
Language: English
Exam Availability: Online proctored exam or at Pearson VUE test centers

Subscription Coupon

Coupon Code: 512E7A2DCE7416215EBE
Validity: 31 Days
Starts: 09/20/2025 12:00 AM PDT (GMT -7)
Expires: 10/21/2025 12:00 PM PDT (GMT -7)

Detailed Syllabus and Topic Weightage

The AWS Certified Machine Learning Engineer – Associate exam validates a candidate’s ability to build, operationalize, deploy, and maintain ML solutions and pipelines using the AWS Cloud. The syllabus is divided into 4 Domains, with question distribution reflecting the topic weightage.

Domain 1: Data Preparation for Machine Learning (ML) (28%)

Explain data ingestion mechanisms and storage options for different data formats (Parquet, JSON, CSV, ORC, Avro, RecordIO)
Identify appropriate AWS data sources (Amazon S3, EFS, FSx) and streaming services (Kinesis, Kafka) for various use cases
Transform data using AWS tools (AWS Glue, Glue DataBrew, SageMaker Data Wrangler) and perform feature engineering
Apply data cleaning techniques (outlier detection, missing data imputation, deduplication) and encoding methods (one-hot, label encoding)
Ensure data integrity by validating quality, addressing class imbalance, and mitigating bias using SageMaker Clarify
Implement data security measures including encryption, classification, anonymization, and compliance with PII/PHI requirements

Domain 2: ML Model Development (26%)

Choose modeling approaches by assessing business problems, data availability, and solution feasibility
Select appropriate ML algorithms, SageMaker built-in algorithms, and AWS AI services for specific use cases
Train models using SageMaker capabilities, script mode with supported frameworks, and custom datasets for fine-tuning
Apply hyperparameter tuning techniques using SageMaker Automatic Model Tuning (random search, Bayesian optimization)
Prevent model overfitting, underfitting, and catastrophic forgetting using regularization techniques and feature selection
Analyze model performance using evaluation metrics (accuracy, precision, recall, F1, RMSE, AUC-ROC) and debugging tools
Manage model versions for repeatability and audits using SageMaker Model Registry

Domain 3: Deployment and Orchestration of ML Workflows (22%)

Select deployment infrastructure based on performance, cost, and latency requirements
Choose appropriate deployment targets (SageMaker endpoints, Kubernetes, ECS, EKS, Lambda) and strategies (real-time, batch)
Create infrastructure using IaC options (CloudFormation, AWS CDK) and configure auto-scaling policies
Build and maintain containers using ECR, EKS, ECS, and bring your own container (BYOC) with SageMaker
Set up CI/CD pipelines using AWS Code services (CodePipeline, CodeBuild, CodeDeploy) and version control systems
Configure training and inference jobs using orchestration tools (SageMaker Pipelines, EventBridge, Step Functions)
Implement deployment strategies (blue/green, canary) and automated testing in CI/CD pipelines

Domain 4: ML Solution Monitoring, Maintenance, and Security (24%)

Monitor model inference to detect drift, data quality issues, and performance degradation using SageMaker Model Monitor
Monitor workflows to detect anomalies in data processing and model inference
Optimize infrastructure costs by selecting appropriate purchasing options (Spot, On-Demand, Reserved Instances)
Configure monitoring tools (CloudWatch, X-Ray) and set up dashboards for performance metrics
Secure AWS resources by configuring IAM roles, policies, and least privilege access to ML artifacts
Implement network security controls using VPCs, subnets, and security groups for ML systems
Monitor and audit ML systems using CloudTrail, ensure compliance, and troubleshoot security issues

In-Scope AWS Services

Candidates should be familiar with the use cases for the following AWS services:

AI/ML Core: Amazon SageMaker (all components), Amazon Bedrock, Amazon Augmented AI (A2I), SageMaker Ground Truth
AI Services: Amazon Comprehend, Amazon Lex, Amazon Polly, Amazon Rekognition, Amazon Transcribe, Amazon Translate, Amazon Kendra, Amazon Textract
Analytics & Data Processing: Amazon Athena, AWS Glue, AWS Glue DataBrew, Amazon EMR, Amazon Kinesis, Amazon OpenSearch Service, Amazon Redshift
Compute & Containers: Amazon EC2, AWS Lambda, Amazon ECR, Amazon ECS, Amazon EKS, AWS Batch
Developer & Orchestration: AWS CodePipeline, AWS CodeBuild, AWS CodeDeploy, AWS CloudFormation, AWS CDK, AWS Step Functions, Amazon EventBridge
Management & Monitoring: Amazon CloudWatch, AWS CloudTrail, AWS X-Ray, AWS Systems Manager, AWS Compute Optimizer
Security & Identity: AWS IAM, AWS KMS, Amazon Macie, AWS Secrets Manager, Amazon VPC
Storage & Database: Amazon S3, Amazon EBS, Amazon EFS, Amazon FSx, Amazon RDS, Amazon DynamoDB

AWS Certified Machine Learning Engineer – Associate – Domain Weightage

Domain 1: Data Preparation for ML – 28%
Domain 2: ML Model Development – 26%
Domain 3: Deployment & Orchestration of ML Workflows – 22%
Domain 4: ML Solution Monitoring, Maintenance, & Security – 24%

Sample Practice Questions

Question 1

A global e-commerce company operates a recommendation system serving millions of users. The system experiences performance degradation, increased costs, and occasional bias in recommendations. The ML team must optimize the entire solution while ensuring fairness, security, and cost efficiency. The current architecture uses SageMaker endpoints on large GPU instances, processes data daily with AWS Glue, stores features in S3, and lacks comprehensive monitoring.

Question:
Which combination of actions addresses all maintenance and optimization requirements?

Options:

A: Migrate to Lambda, use EC2 for training, disable logging
B: Use only CPU instances, manual scaling, quarterly audits
C: Continue current setup without changes
D: Implement SageMaker Model Monitor and Clarify for drift and bias detection, use Inference Recommender to optimize instance types, enable multi-model endpoints to reduce costs, configure CloudWatch alarms for performance metrics, implement VPC isolation with least-privilege IAM roles, enable CloudTrail and Config for audit compliance, use Cost Explorer with tagging for cost allocation, establish A/B testing for model variants

Answer: D

Explanation:

A: Lambda is unsuitable for large inference workloads due to execution time and memory limits. EC2 requires manual management, and disabling logging removes visibility and compliance tracking.
B: CPU-only setups may underperform for deep learning models, and manual scaling increases operational overhead. Quarterly audits are too infrequent for proactive compliance.
C: The current system already shows inefficiencies and lacks monitoring, so maintaining the status quo won’t resolve issues.
D: This end-to-end optimization covers all areas: Model Monitor and Clarify ensure bias and drift detection; Inference Recommender optimizes instance types; multi-model endpoints reduce cost; CloudWatch enhances observability; VPC and IAM strengthen security; CloudTrail and Config provide compliance tracking; Cost Explorer supports cost allocation; A/B testing validates performance improvements.

Domain: ML Solution Monitoring, Maintenance, and Security
Question Type: Case-Study

Question 2

Task: Match the data cleaning technique to the scenario:

Use median imputation for missing values in skewed distributions
Apply IQR method for outlier detection
Implement deduplication using composite keys
Merge datasets using inner join

Question:
Which AWS services enable validation and quality checks on data before model training?

Options:

A: Amazon SageMaker Studio
B: AWS Glue Data Quality
C: Amazon CloudWatch
D: AWS Glue DataBrew

Answer: B, D

Explanation:

A: SageMaker Studio is an ML IDE and doesn’t provide built-in data validation rules.
B: AWS Glue Data Quality supports automated validation, completeness checks, and data profiling before pipeline execution, making it ideal for pre-training validation.
C: CloudWatch focuses on infrastructure and application metrics, not dataset validation.
D: Glue DataBrew visually profiles and cleans data, detecting missing values, skewness, and anomalies — ensuring datasets meet model input standards.

Domain: Data Preparation for ML
Question Type: Service-Based

Question 3

Question:
What is the primary benefit of using Amazon SageMaker Feature Store for managing machine learning features?

Options:

A: Automated hyperparameter tuning
B: Real-time model deployment
C: Version control for training scripts
D: Centralized feature repository with online and offline stores

Answer: D

Explanation:

A: Hyperparameter tuning is handled by SageMaker Automatic Model Tuning, not Feature Store.
B: Model deployment is performed via SageMaker endpoints, not Feature Store.
C: Script versioning is managed externally (e.g., with Git).
D: Feature Store provides a unified feature repository with online (low-latency) and offline (batch) stores, ensuring consistent features for training and inference.

Domain: Data Preparation for ML
Question Type: Concept

Question 4

Task: Order the steps for ingesting streaming data into AWS for ML processing:

Configure Kinesis Data Stream
Set up S3 bucket
Create Data Firehose delivery stream
Define data transformation Lambda

Options:

A: 2, 3, 1, 4
B: 1, 3, 4, 2
C: 3, 1, 2, 4
D: 4, 1, 3, 2

Answer: B

Explanation:

A: Setting up S3 first doesn’t establish the streaming pipeline flow.
B: Correct order begins by configuring the Kinesis Data Stream, then setting up Data Firehose for delivery, defining transformation logic with Lambda, and finally creating the S3 bucket for storage.
C: Starting with Firehose before its data source causes dependency issues.
D: Defining transformations before streams exist disrupts the logical flow of data ingestion.

Domain: Data Preparation for ML
Question Type: Ordering

Preparation Strategy & Study Guidance

Understand the Concepts, Not Just the Questions: Use mock exams to identify weak areas but supplement with the official AWS MLA-C01 guide.
Target 80%+ in Practice Tests: Real exam passing score is 700; high practice scores build confidence.
Review Explanations in Detail: Study why each answer is correct or incorrect to reinforce AWS ML service knowledge.
Simulate Real Exam Conditions: Attempt timed, distraction-free sessions to develop focus and endurance.
Hands-On Application: Reinforce ML knowledge through practical examples like SageMaker workflows, model development, deployment orchestration, monitoring, and CI/CD automation.

Why This Course Is Valuable

Realistic exam simulation aligned with AWS MLA-C01 format, covering diverse question types: multiple-choice, ordering, matching, scenario, case study, and concept-based.
Full syllabus coverage, including data preparation, model development, deployment & orchestration, monitoring & maintenance, and AWS ML services.
In-depth explanations for correct and incorrect answers to improve conceptual understanding.
Timed, scored tests with randomized questions for better preparation.
Designed for IT and non-IT professionals aiming for AWS Certified Machine Learning Engineer – Associate (MLA-C01) certification.
Updated as per the latest 2025 AWS MLA-C01 syllabus and exam objectives.

Top Reasons to Take This Practice Exam

6 full-length mock exams with 390 questions
100% coverage of official AWS MLA-C01 syllabus
Realistic multiple-choice, multiple-response, ordering, scenario, matching, case study, and concept-based questions
Detailed rationales for correct and incorrect answers
Balanced question distribution across foundational, application, and analytical levels
Scenario-based, concept-based, and AWS service-based questions for practical learning
Timed simulations to replicate real exam conditions
Randomized question bank to encourage active learning and prevent memorization
Accessible anywhere, anytime on desktop or mobile devices
Lifetime updates included for syllabus changes

What This Course Includes

6 Full-Length Practice Tests: Simulate real exam conditions to test readiness
Access on Mobile: Study anytime, anywhere on your phone or tablet
Full Lifetime Access: Learn at your own pace with no expiration

Money-Back Guarantee

Your success is our priority. 30-day no-questions-asked refund policy if the course doesn’t meet your expectations.

Who This Course Is For

Professionals preparing for the AWS Certified Machine Learning Engineer – Associate (AIF-C01) exam
IT professionals with limited AI/ML exposure who want to make informed decisions when building/managing AI solutions
Non-IT professionals in marketing, sales, PM, HR, finance, accounting, seeking confidence in AI concepts
Developers, data analysts, and cloud engineers enhancing AWS AI/ML skills
Professionals addressing real-world AI challenges, including bias, explainability, and responsible AI
Career changers aiming to develop expertise in AI applications, AWS services, and solution implementation

What You’ll Learn

Core AI and ML principles, including supervised/unsupervised learning, deep learning, and foundation models
Generative AI concepts, prompt engineering, and AWS AI/ML services like SageMaker, Bedrock, Comprehend, Rekognition, and Transcribe
Practical application of AI/ML workflows, model evaluation, and business use cases on AWS
Guidelines for responsible AI, including bias detection, fairness, explainability (XAI), and human-centered AI design
Hands-on experience with scenario-based, AWS service-based, and concept-based questions
Time management, exam strategies, and practice approaches for AWS Certified Machine Learning Engineer – Associate (AIF-C01) exam
Practical knowledge to confidently pass the AWS AIF-C01 certification and apply AI solutions in real-world business scenarios

Requirements / Prerequisites

Basic understanding of cloud computing or IT fundamentals is helpful but not mandatory
Familiarity with AI/ML concepts, generative AI, or AWS services is beneficial but not required
Computer with internet access for online mock exams
Curiosity to learn AI concepts, AWS AI/ML services, foundation models, and generative AI applications
Willingness to practice and apply knowledge using scenario, ordering, matching, and case-study based questions

Who this course is for:

Cloud professionals preparing for the AWS Certified Machine Learning Engineer – Associate MLA-C01 exam
Data scientists and ML engineers seeking to operationalize ML models on AWS
Developers and DevOps engineers implementing scalable ML pipelines.
Data engineers focusing on AWS-based ingestion, transformation, and feature engineering
AI/ML practitioners preparing for multi-format MLA-C01 exam questions
QA automation testers exploring ML-driven testing and model validation workflows
Cloud architects designing ML infrastructure and MLOps pipelines
Career changers aiming to move into cloud-based ML engineering
Students or professionals wanting complete MLA-C01 domain coverage
Anyone aiming to pass AWS Machine Learning Engineer Associate exam with confidence