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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
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 (Q46-Q51):

NEW QUESTION # 46
A company is developing an ML model for a customer. The training data is stored in an Amazon S3 bucket in the customer ' s AWS account (Account A). The company runs Amazon SageMaker AI training jobs in a separate AWS account (Account B).
The company defines an S3 bucket policy and an IAM policy to allow reads to the S3 bucket.
Which additional steps will meet the cross-account access requirement?

Answer: C

Explanation:
For cross-account Amazon S3 access, AWS requires two components:
An S3 bucket policy in the owning account (Account A) that grants access to a principal in another account An IAM role policy in the consuming account (Account B) that allows the service to access the bucket Amazon SageMaker training jobs assume an IAM role in the account where the job runs-in this case, Account B. Therefore, the IAM policy must be attached to the SageMaker execution role in Account B.
The S3 bucket policy must reside in Account A because bucket policies are owned and enforced by the bucket owner. This policy explicitly allows the IAM role from Account B to read the training data.
Any other combination fails either because the policy is in the wrong account or because the role is not the one used by SageMaker.
AWS documentation clearly describes this pattern as the correct way to grant cross-account access for SageMaker training jobs.
Therefore, Option B is the correct and AWS-aligned solution.


NEW QUESTION # 47
A company is building an ML model by using Amazon SageMaker, AWS owned libraries, and open source libraries. The company must ensure that SageMaker does not collect metadata about usage and errors during training. Which solution will meet these requirements?

Answer: D

Explanation:
To prevent metadata collection in Amazon SageMaker, you must disable the metadata sharing setting in the SageMaker domain for console jobs and explicitly opt out of metadata collection for each training job submitted through the AWS CLI or SDKs. This ensures that neither usage nor error metadata is collected during training.


NEW QUESTION # 48
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?

Answer: D

Explanation:
Amazon SageMaker Model Registry is a feature designed to manage machine learning (ML) models throughout their lifecycle. It allows users to catalog, version, and deploy models systematically, ensuring efficient model governance and management.
Key Features of SageMaker Model Registry:
Centralized Cataloging: Organizes models into Model Groups, each containing multiple versions.
Version Control: Maintains a history of model iterations, making it easier to track changes.
Metadata Association: Attach metadata such as training metrics and performance evaluations to models.
Approval Status Management: Allows setting statuses like PendingManualApproval or Approved to ensure only vetted models are deployed.
Seamless Deployment: Direct integration with SageMaker deployment capabilities for real-time inference or batch processing.
Implementation Steps:
Create a Model Group: Organize related models into groups to simplify management and versioning.
Register Model Versions: Each model iteration is registered as a version within a specific Model Group.
Set Approval Status: Assign approval statuses to models before deploying them to ensure quality control.
Deploy the Model: Use SageMaker endpoints for deployment once the model is approved.
Benefits:
Centralized Management: Provides a unified platform to manage models efficiently.
Streamlined Deployment: Facilitates smooth transitions from development to production.
Governance and Compliance: Supports metadata association and approval processes.
By leveraging the SageMaker Model Registry, the company can ensure organized management of models, version control, and efficient deployment workflows with minimal operational overhead.
AWS Documentation: SageMaker Model Registry
AWS Blog: Model Registry Features and Usage


NEW QUESTION # 49
An ML engineer is using AWS CodeDeploy to deploy new container versions for inference on Amazon ECS.
The deployment must shift 10% of traffic initially, and the remaining 90% must shift within 10-15 minutes.
Which deployment configuration meets these requirements?

Answer: A

Explanation:
AWS CodeDeploy provides predefined deployment configurations for ECS that support canary and linear traffic shifting. The ECSCanary10Percent15Minutes configuration shifts 10% of traffic initially, waits 15 minutes, and then shifts the remaining traffic.
This matches the exact requirement: a 10% initial shift followed by the remaining 90% within the specified time window.
Lambda deployment configurations are not applicable to ECS. ECSAllAtOnce does not perform gradual traffic shifting.
AWS documentation explicitly defines ECSCanary10Percent15Minutes for controlled, low-risk ECS deployments.
Therefore, Option C is the correct and AWS-verified answer.


NEW QUESTION # 50
A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish.
Which solution will meet these requirements in the LEAST amount of time?

Answer: A

Explanation:
Amazon Transcribeis well-suited for converting audio data into text, including Spanish.
Amazon Translatecan efficiently translate Spanish text into English if needed.
Amazon Bedrock, with theJurassic model, is designed for tasks like text summarization and can handle large language models (LLMs) seamlessly. This combination provides a low-code, managed solution to process audio, video, and text data with minimal time and effort.


NEW QUESTION # 51
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