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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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 3	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 4	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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q95-Q100):

NEW QUESTION # 95
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions.
Which solution will meet this requirement?

A. Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.
B. Apply statistics from a well-known dataset to normalize the production samples.
C. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.
D. Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.

Answer: C

NEW QUESTION # 96
An ML engineer is using a training job to fine-tune a deep learning model in Amazon SageMaker Studio. The ML engineer previously used the same pre-trained model with a similar dataset. The ML engineer expects vanishing gradient, underutilized GPU, and overfitting problems.
The ML engineer needs to implement a solution to detect these issues and to react in predefined ways when the issues occur. The solution also must provide comprehensive real-time metrics during the training.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use TensorBoard to monitor the training job. Publish the findings to an Amazon Simple Notification Service (Amazon SNS) topic. Create an AWS Lambda function to consume the findings and to initiate the predefined actions.
B. Use Amazon CloudWatch default metrics to gain insights about the training job. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
C. Expand the metrics in Amazon CloudWatch to include the gradients in each training step. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
D. Use SageMaker Debugger built-in rules to monitor the training job. Configure the rules to initiate the predefined actions.

Answer: D

NEW QUESTION # 97
An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.
Which solutions will mitigate this problem? (Choose two.)

A. Increase dropout in the layers.
B. Increase the number of layers.
C. Enable early stopping on the model.
D. Investigate and reduce the sources of model bias.
E. Increase the number of neurons.

Answer: A,C

NEW QUESTION # 98
A company is building an enterprise AI platform. The company must catalog models for production, manage model versions, and associate metadata such as training metrics with models. The company needs to eliminate the burden of managing different versions of models.
Which solution will meet these requirements?

A. Use the Amazon SageMaker Model Registry to catalog the models. Create unique tags for each model version. Create key-value pairs to maintain associated metadata.
B. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model. Create unique tags for each model version. Create key-value pairs to maintain associated metadata.
C. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model. Use the repositories to catalog the models and to manage model versions and associated metadata.
D. Use the Amazon SageMaker Model Registry to catalog the models. Create model groups for each model to manage the model versions and to maintain associated metadata.

Answer: D

Explanation:
The correct answer is B. Use the Amazon SageMaker Model Registry to catalog the models. Create model groups for each model to manage the model versions and to maintain associated metadata.
The Amazon SageMaker Model Registry is a managed repository within SageMaker designed specifically for production-grade ML model lifecycle management. It allows organizations to catalog models, track multiple versions of a model, associate rich metadata, and manage deployment workflows in a scalable, controlled manner. Each model can belong to a model group, which acts as a container for all versions of that particular model. Versions can store training metrics, hyperparameters, model artifacts, and other key metadata, enabling reproducibility, auditing, and automated promotion between stages (e.g., Staging # Production).
Option A, while using the Model Registry, relies on manually tagging versions and creating key-value pairs to store metadata. This approach is error-prone, lacks structured versioning, and does not integrate with SageMaker's deployment pipelines.
Options C and D suggest using Amazon ECR repositories. While ECR can store containerized model artifacts, it is not designed for ML-specific metadata, versioning, or automated model stage transitions. Using ECR alone would require custom-built solutions for metadata management, auditing, and version tracking, adding unnecessary operational overhead.
By leveraging the Model Registry with model groups, organizations can automate promotions, apply approval workflows, and track lineage efficiently, fully aligning with AWS best practices for ML model development and production readiness. This ensures compliance, reproducibility, and reduces operational complexity in enterprise AI platforms.
Using the Model Registry and model groups is the standard AWS-recommended approach for enterprise-scale model cataloging and version control, enabling teams to focus on model improvement rather than infrastructure management.

NEW QUESTION # 99
A travel company has trained hundreds of geographic data models to answer customer questions by using Amazon SageMaker AI. Each model uses its own inferencing endpoint, which has become an operational challenge for the company.
The company wants to consolidate the models' inferencing endpoints to reduce operational overhead.
Which solution will meet these requirements?

A. Use SageMaker AI multi-container endpoints. Deploy a single endpoint.
B. Use Amazon SageMaker Studio. Deploy a single-model endpoint.
C. Use SageMaker AI multi-model endpoints. Deploy a single endpoint.
D. Use inference pipelines in SageMaker AI to combine tasks from hundreds of models to 15 models.

Answer: C

Explanation:
AWS provides Amazon SageMaker Multi-Model Endpoints to host multiple trained models behind a single inference endpoint. This feature dynamically loads models into memory based on request traffic, significantly reducing endpoint sprawl and operational overhead.
Multi-model endpoints are ideal when an organization has hundreds or thousands of related models, such as geographic or regional models, that are not all invoked simultaneously. AWS documentation highlights this approach as a best practice for cost optimization and simplified deployment management.
Option B (multi-container endpoints) is intended for chaining a small number of models, not hundreds.
Option C does not address endpoint consolidation. Option D changes the modeling strategy and is not required.
Therefore, using a single SageMaker multi-model endpoint is the correct solution.

NEW QUESTION # 100
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