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Amazon MLA-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q183-Q188):

NEW QUESTION # 183
A company runs Amazon SageMaker ML models that use accelerated instances. The models require real-time responses. Each model has different scaling requirements. The company must not allow a cold start for the models.
Which solution will meet these requirements?

A. Create an Amazon S3 bucket. Store all the model artifacts in the S3 bucket. Create a SageMaker multi-model endpoint. Point the endpoint to the S3 bucket. Create an auto scaling policy for the endpoint. Set the parameter for the minimum number of copies to at least 1.
B. Create a SageMaker Asynchronous Inference endpoint for each model. Create an auto scaling policy for each endpoint.
C. Create a SageMaker endpoint. Create an inference component for each model. In the inference component settings, specify the newly created endpoint. Create an auto scaling policy for each inference component. Set the parameter for the minimum number of copies to at least 1.
D. Create a SageMaker Serverless Inference endpoint for each model. Use provisioned concurrency for the endpoints.

Answer: C

NEW QUESTION # 184
A company has deployed a model to predict the churn rate for its games by using Amazon SageMaker Studio.
After the model is deployed, the company must monitor the model performance for data drift and inspect the report. Select and order the correct steps from the following list to model monitor actions. Select each step one time. (Select and order THREE.) .
Check the analysis results on the SageMaker Studio console. .
Create a Shapley Additive Explanations (SHAP) baseline for the model by using Amazon SageMaker Clarify.
Schedule an hourly model explainability monitor.

Answer:

Explanation:

Explanation:
Step 1:
Create a Shapley Additive Explanations (SHAP) baseline for the model by using Amazon SageMaker Clarify.
Step 2:
Schedule an hourly model explainability monitor.
Step 3:
Check the analysis results on the SageMaker Studio console.
When monitoring a deployed model for data drift and explainability, AWS prescribes a specific workflow using SageMaker Clarify and SageMaker Model Monitor:
Create a SHAP baseline (Step 1)Before any monitoring can occur, SageMaker Clarify must establish a baseline explainability configuration. This baseline captures the reference SHAP values for feature importance using training or baseline data. Model Monitor uses this baseline to compare future inferences and detect drift in feature attributions.
Schedule the model explainability monitor (Step 2)After the baseline is created, an explainability monitoring schedule must be configured (hourly in this case). The monitor periodically analyzes inference data, compares it against the SHAP baseline, and generates reports that highlight drift or anomalies in feature contributions.
Inspect results in SageMaker Studio (Step 3)Once monitoring jobs run, SageMaker stores the analysis results in Amazon S3 and surfaces them in the SageMaker Studio console, where engineers can review metrics, violations, and visual reports.
This sequence is mandatory because:
A monitor cannot run without a baseline
Results cannot be reviewed until the monitor executes

NEW QUESTION # 185
A company uses the Amazon SageMaker AI Object2Vec algorithm to train an ML model. The model performs well on training data but underperforms after deployment. The company wants to avoid overfitting the model and maintain the model's ability to generalize.
Which solution will meet these requirements?

A. Increase the mini_batch_size hyperparameter.
B. Decrease the dropout rate.
C. Increase the number of epochs.
D. Decrease the early_stopping_patience hyperparameter.

Answer: D

Explanation:
The described behavior-strong performance on training data but poor performance in production-is a classic sign of overfitting. AWS documentation for Amazon SageMaker Object2Vec highlights early stopping as a key regularization technique to prevent models from learning noise in the training data.
The early_stopping_patience hyperparameter controls how many additional epochs the training job will run after the validation loss stops improving. Decreasing this value causes training to stop earlier, reducing the chance that the model overfits to the training dataset.
Option B increases batch size, which may improve training efficiency but does not directly address overfitting. Option C decreases the dropout rate, which actually increases overfitting risk, since dropout is a regularization mechanism. Option D increases epochs, which further worsens overfitting.
AWS best practices emphasize early stopping combined with validation metrics as one of the most effective ways to maintain generalization performance in neural embedding models such as Object2Vec.
Therefore, Option A is the correct and AWS-aligned solution.

NEW QUESTION # 186
A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow. The AWS Glue jobs can run on a schedule or can be launched manually.
The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development.
An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Step Functions for orchestration of the pipelines and the AWS Glue jobs.
B. Use Amazon EventBridge to invoke the pipelines and the AWS Glue jobs in the desired order.
C. Use Callback steps in SageMaker Pipelines to start the AWS Glue workflow and to stop the pipelines until the AWS Glue jobs finish running.
D. Use processing steps in SageMaker Pipelines. Configure inputs that point to the Amazon Resource Names (ARNs) of the AWS Glue jobs.

Answer: C

Explanation:
Callback steps in Amazon SageMaker Pipelines allow you to integrate external processes, such as AWS Glue jobs, into the pipeline workflow. By using a Callback step, the SageMaker pipeline can trigger the AWS Glue workflow and pause execution until the Glue jobs complete. This approach provides seamless integration with minimal operational overhead, as it directly ties the pipeline's execution flow to the completion of the AWS Glue jobs without requiring additional orchestration tools or complex setups.

NEW QUESTION # 187
A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories.
Which solution will meet these requirements?

A. Use the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon ECR. Create a new AWS account. In the new account, use the SageMaker Model Registry as the central catalog. Attach a cross-account resource policy to each model group in the initial AWS accounts.
B. Configure ECR cross-account replication for each existing ECR repository. Ensure that each model is visible in each AWS account.
C. Use an AWS Glue Data Catalog to store the models. Run an AWS Glue crawler to migrate the models from the ECR repositories to the Data Catalog. Configure cross-account access to the Data Catalog.
D. Create a new AWS account with a new ECR repository as the central catalog. Configure ECR cross- account replication between the initial ECR repositories and the central catalog.

Answer: A

Explanation:
The Amazon SageMaker Model Registry is designed to manage and catalog ML models, including those hosted in Amazon ECR. By creating a model group for each model in the SageMaker Model Registry and setting up cross-account resource policies, the company can establish a central catalog in a new AWS account.
This allows all models from the initial accounts to be accessible in a unified, centralized manner for better organization, management, and governance. This solution leverages existing AWS services and ensures scalability and minimal operational overhead.

NEW QUESTION # 188
......

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