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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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 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
  • 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q196-Q201):

NEW QUESTION # 196
A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker endpoint. The ML engineer needs to explain to company stakeholders how the model makes predictions.
Which solution will provide an explanation for the model's predictions?

Answer: D

Explanation:
SageMaker Clarify is designed to provide explainability for ML models. It can analyze feature importance and explain how input features influence the model's predictions. By using Clarify with the deployed SageMaker model, the ML engineer can generate insights and present them to stakeholders to explain the sentiment analysis predictions effectively.


NEW QUESTION # 197
A company wants to evaluate a new ML model architecture to understand its performance before deploying the model to production. The company wants to use Amazon SageMaker AI shadow testing.
The company needs to analyze the performance metrics of the shadow model and the production model without affecting the existing production endpoint. The analysis must use real-time inference requests.
Select and order the correct steps to implement shadow testing and compare the model variants in SageMaker AI. Select each step one time or not at all (Select and order Three)

Answer:

Explanation:

Explanation:
Step 1: Update the existing endpoint with a shadow variant. Pick a suitable duration. Start the shadow test.
Step 2: Create a shadow test.
Step 3: Use the SageMaker shadow testing dashboard to analyze the performance differences between the variants.
This is the correct order because SageMaker shadow testing can run against an existing production endpoint.
The production variant continues to receive and respond to inference requests, while the shadow variant receives replicated real-time requests but does not return responses to users. That satisfies the requirement to analyze the new model with real inference traffic without affecting the production endpoint. AWS also states that the SageMaker shadow testing dashboard provides side-by-side comparison metrics for the production and shadow variants, such as latency and error rate.
Do not choose "Create a new endpoint that includes the production model and the shadow model," because the question explicitly says the analysis must not affect the existing production endpoint. Also, do not use the Amazon SageMaker Model Monitoring dashboard here; the specific feature for comparing variants in this scenario is the SageMaker shadow testing dashboard.


NEW QUESTION # 198
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account # in the same Region.
Which solution will meet this requirement with the LEAST development effort?

Answer: A

Explanation:
Amazon Comprehend provides the ImportModel API operation, which allows you to copy a custom model between AWS accounts. By creating a resource-based IAM policy on the model in Account A, you can grant Account B the necessary permissions to access and import the model. This approach requires minimal development effort and is the AWS-recommended method for sharing custom models across accounts.


NEW QUESTION # 199
An ML engineer is developing a classification model. The ML engineer needs to use custom libraries in processing jobs, training jobs, and pipelines in Amazon SageMaker. Which solution will provide this functionality with the LEAST implementation effort?

Answer: D

Explanation:
Building a custom Docker container with the required libraries and hosting it in Amazon ECR allows SageMaker jobs, training, and pipelines to consistently use the same environment. This approach minimizes manual setup, ensures portability, and provides the least ongoing implementation effort compared to repeatedly installing or managing libraries separately.


NEW QUESTION # 200
Hotspot Question
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
- An S3 event notification invokes the pipeline when new data is
uploaded.
- S3 Lifecycle rule invokes the pipeline when new data is uploaded.
- SageMaker retrains the model by using the data in the S3 bucket.
- The pipeline deploys the model to a SageMaker endpoint.
- The pipeline deploys the model to SageMaker Model Registry.

Answer:

Explanation:


NEW QUESTION # 201
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