MLA-C01 Practice Exam: AWS Certified Machine Learning Engineer - Associate
SageMaker end to end: prep data, train models, deploy pipelines, keep them honest in production.
What you'll be tested on
- Data Preparation for ML
- Model Development
- Deployment and Orchestration
- Monitoring and Maintenance
Sample MLA-C01 questions
Case Study - A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company needs to use the central model registry to manage different versions of models in the application. Which action will meet this requirement with the LEAST operational overhead?
- Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
- Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
- Use the SageMaker Model Registry and model groups to catalog the models.
- Use the SageMaker Model Registry and unique tags for each model version.
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C — Use the SageMaker Model Registry and model groups to catalog the models.The SageMaker Model Registry is the purpose-built central registry for cataloging models, and model groups are its native organizational unit: each model group contains versioned model packages for a given model. This is fully managed, so it has the least operational overhead. Using Amazon ECR repositories or ECR tags (options A and B) only stores container images, not model metadata, approval status, or versions, and requires you to build your own tracking. Option D is wrong because model groups, not tags, are the intended versioning mechanism in the registry; tags are only supplementary metadata.
Case Study - A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company is experimenting with consecutive training jobs. How can the company MINIMIZE infrastructure startup times for these jobs?
- Use Managed Spot Training.
- Use SageMaker managed warm pools.
- Use SageMaker Training Compiler.
- Use the SageMaker distributed data parallelism (SMDDP) library.
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B — Use SageMaker managed warm pools.SageMaker managed warm pools keep training infrastructure provisioned and initialized after a job finishes, so consecutive training jobs start in seconds instead of waiting minutes for instance provisioning and container startup. Managed Spot Training (A) reduces cost, not startup latency, and can add interruption delays. SageMaker Training Compiler (C) speeds up the training computation itself by optimizing the graph, not infrastructure startup. The SMDDP library (D) accelerates distributed training across multiple GPUs or nodes but does nothing to reduce the time needed to launch training infrastructure.
Case Study - A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints. Which solution will meet this requirement?
- Use SageMaker Experiments to facilitate the approval process during model registration.
- Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
- Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
- Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."
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D — Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."SageMaker Pipelines integrates with the Model Registry approval workflow: a model registration step can register a model version with PendingManualApproval status, and a human (or an automated SDK call after approval) sets the status to Approved before a deployment step proceeds. This gives a manual approval gate so only approved models reach production endpoints. SageMaker Experiments (A) tracks and compares runs but has no approval mechanism. ML Lineage Tracking (B) records relationships between entities for auditability, not approvals. Model Monitor (C) detects drift in production quality, it does not manage deployment approvals.
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FAQ
How many practice questions are in this MLA-C01 bank?
265 questions covering the current MLA-C01 AWS Certified Machine Learning Engineer - Associate syllabus, every one with the correct answer and an explanation.How long is the real MLA-C01 exam?
The official MLA-C01 exam gives you 130 minutes. Our timed exam mode uses the same limit so the pace feels familiar.What does MLA-C01 access cost?
Plans start at $3.99 for 3 months. One payment, no subscription — and far cheaper than retaking the real exam.