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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.
Topic 2	Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.
Topic 3	ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.
Topic 4	ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q73-Q78):

NEW QUESTION # 73
A company must install a custom script on any newly created Amazon SageMaker notebook instances.
Which solution will meet this requirement with the LEAST operational overhead?

A. Create a lifecycle configuration script to install the custom script when a new SageMaker notebook is created. Attach the lifecycle configuration to every new SageMaker notebook as part of the creation steps.
B. Create a custom package index repository. Use AWS CodeArtifact to manage the installation of the custom script. Set up AWS PrivateLink endpoints to connect CodeArtifact to the SageMaker instance. Install the script.
C. Create a custom Amazon Elastic Container Registry (Amazon ECR) image that contains the custom script. Push the ECR image to a Docker registry. Attach the Docker image to a SageMaker Studio domain. Select the kernel to run as part of the SageMaker notebook.
D. Store the custom script in Amazon S3. Create an AWS Lambda function to install the custom script on new SageMaker notebooks. Configure Amazon EventBridge to invoke the Lambda function when a new SageMaker notebook is initialized.

Answer: A

NEW QUESTION # 74
An ML engineer is designing an AI-powered traffic management system. The system must use near real-time inference to predict congestion and prevent collisions.
The system must also use batch processing to perform historical analysis of predictions over several hours to improve the model. The inference endpoints must scale automatically to meet demand.
Which combination of solutions will meet these requirements? (Select TWO.)

A. Use Amazon EC2 Auto Scaling to host containers for batch analysis.
B. Use AWS Lambda with reserved concurrency and SnapStart to connect to SageMaker endpoints.
C. Use AWS Lambda for historical analysis.
D. Use Amazon SageMaker real-time inference endpoints with automatic scaling based on ConcurrentInvocationsPerInstance.
E. Use an Amazon SageMaker Processing job for batch historical analysis. Schedule the job with Amazon EventBridge.

Answer: D,E

Explanation:
For near real-time predictions, AWS documentation recommends Amazon SageMaker real-time inference endpoints. These endpoints support automatic scaling based on metrics such as ConcurrentInvocationsPerInstance, ensuring low latency and high availability during traffic spikes.
For long-running historical analysis, SageMaker Processing jobs are the appropriate solution. Processing jobs are designed for batch workloads, can run for hours, and integrate cleanly with SageMaker pipelines.
Scheduling them with Amazon EventBridge provides a fully managed, scalable, and serverless solution.
AWS Lambda is unsuitable for multi-hour workloads. EC2 Auto Scaling adds unnecessary infrastructure management overhead.
Therefore, Options A and C together meet all requirements and align with AWS best practices.

NEW QUESTION # 75
A company uses Amazon SageMaker AI to create ML models. The data scientists need fine-grained control of ML workflows, DAG visualization, experiment history, and model governance for auditing and compliance.
Which solution will meet these requirements?

A. Use SageMaker Pipelines with SageMaker Studio and SageMaker ML Lineage Tracking.
B. Use AWS CodePipeline with SageMaker Experiments.
C. Use AWS CodePipeline with SageMaker Studio and SageMaker ML Lineage Tracking.
D. Use SageMaker Pipelines with SageMaker Experiments.

Answer: A

Explanation:
Amazon SageMaker Pipelines provides native orchestration of ML workflows with fine-grained control, DAG-based visualization, and seamless integration with SageMaker Studio. AWS documentation explicitly states that Pipelines is designed for end-to-end ML workflow automation and visualization.
SageMaker ML Lineage Tracking records relationships between datasets, models, training jobs, and endpoints, enabling full auditability and governance, which is essential for compliance.
SageMaker Experiments tracks experiment metrics but does not provide lineage-level governance.
CodePipeline is a general CI/CD service and lacks ML-specific DAG visualization and lineage tracking.
AWS best practices recommend combining SageMaker Pipelines + SageMaker Studio + ML Lineage Tracking for enterprise-grade ML workflow management.
Therefore, Option C is the correct and AWS-verified solution.

NEW QUESTION # 76
A company shares Amazon SageMaker Studio notebooks that are accessible through a VPN.
The company must enforce access controls to prevent malicious actors from exploiting presigned URLs to access the notebooks.
Which solution will meet these requirements?

A. Set up Studio client VPC validation by using the aws:sourceVpc IAM policy condition.
B. Set up Studio client IP validation by using the aws:sourceIp IAM policy condition.
C. Set up Studio client user endpoint validation by using the aws:PrincipalTag IAM policy condition.
D. Set up Studio client role endpoint validation by using the aws:PrimaryTag IAM policy condition.

Answer: B

NEW QUESTION # 77
An ML engineer is setting up a CI/CD pipeline for an ML workflow in Amazon SageMaker AI. The pipeline must automatically retrain, test, and deploy a model whenever new data is uploaded to an Amazon S3 bucket.
New data files are approximately 10 GB in size. The ML engineer also needs to track model versions for auditing.
Which solution will meet these requirements?

A. Use AWS Lambda and Amazon EventBridge to retrain and deploy the model and track versions via logs.
B. Use SageMaker Pipelines with the SageMaker Model Registry to orchestrate model training and version tracking.
C. Manually retrain and deploy the model using SageMaker notebook instances and track versions with AWS CloudTrail.
D. Use AWS CodePipeline, Amazon S3, and AWS CodeBuild to retrain and deploy the model automatically and track model versions.

Answer: B

Explanation:
AWS documentation identifies SageMaker Pipelines as the native CI/CD service for ML workflows. Pipelines allow engineers to define automated steps for data processing, training, evaluation, and deployment, making them ideal for retraining models when new data arrives in Amazon S3.
For version tracking and auditing, SageMaker Model Registry is explicitly designed to manage model versions, metadata, approval status, and deployment history. This satisfies regulatory and audit requirements without custom tooling.
AWS Lambda is not suitable for handling large datasets (10 GB), and CodeBuild is not ML-aware and lacks built-in model governance. Manual notebook workflows do not meet CI/CD or automation requirements.
AWS best practices strongly recommend SageMaker Pipelines combined with the Model Registry for scalable, auditable, and production-grade ML CI/CD pipelines.
Therefore, Option B is the correct and AWS-verified solution.

NEW QUESTION # 78
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