AWS Machine Learning MLA-C01 - Mock Tests 390 Questions 2025

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.

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:

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 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

Validity: 31 Days

Starts: 09/20/2025 12:00 AM PDT (GMT -7)

Expires: 10/21/2025 12:00 PM PDT (GMT -7)

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

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

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

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

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

Domain 2: ML Model Development – 26%

Domain 3: Deployment & Orchestration of ML Workflows – 22%

Domain 4: ML Solution Monitoring, Maintenance, & Security – 24%

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

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.