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Amazon MLA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
ML Solution Monitoring, Maintenance, and Security	24%	- Monitoring and observability 1. Model drift detection and performance monitoring 2. Infrastructure and data monitoring - Security and governance 1. Compliance and secure ML system design 2. IAM policies and access control
Deployment and Orchestration of ML Workflows	22%	- ML pipeline orchestration 1. CI/CD pipelines for ML workflows 2. Workflow automation using AWS services - Model deployment 1. Real-time and batch inference endpoints 2. Auto scaling and infrastructure configuration
Data Preparation for Machine Learning (ML)	28%	- Data ingestion and collection 1. Batch and streaming ingestion using AWS services (e.g., S3, Kinesis, Glue) 2. Data acquisition from structured and unstructured sources - Data preprocessing and transformation 1. Data validation and quality checks 2. Data cleaning, normalization, and feature engineering
ML Model Development	26%	- Model tuning and evaluation 1. Performance evaluation and metrics interpretation 2. Hyperparameter tuning - Model selection and training 1. Training models using Amazon SageMaker 2. Choosing appropriate ML algorithms

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q171-Q176):

NEW QUESTION # 171
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 # 172
A company has an existing Amazon SageMaker AI model (v1) on a production endpoint. The company develops a new model version (v2) and needs to test v2 in production before substituting v2 for v1.
The company needs to minimize the risk of v2 generating incorrect output in production and must prevent any disruption of production traffic during the change.
Which solution will meet these requirements?

A. Deploy v2 into a shadow variant that samples 100% of the inference requests. Collect all output in Amazon S3. If v2 performs as expected, promote v2 to production.
B. Create a second production variant for v2. Assign 10% of the traffic to v2 and 90% to v1. Collect all output of v2 in Amazon S3. If v2 performs as expected, switch all traffic to v2.
C. Deploy v2 to a new endpoint. Turn on data capture for the production endpoint. Send 100% of the input data to v2.
D. Create a second production variant for v2. Assign 1% of the traffic to v2 and 99% to v1. Collect all output of v2 in Amazon S3. If v2 performs as expected, switch all traffic to v2.

Answer: A

Explanation:
AWS recommends SageMaker shadow testing as the safest way to validate a new model version using real production traffic without impacting production responses. A shadow variant receives a copy of inference requests but does not return predictions to end users. This completely eliminates the risk of exposing incorrect predictions.
Options A and B are canary deployments. While useful, they still allow v2 to return responses to real users, which violates the requirement to prevent disruption. Option C sends 100% of traffic to v2 externally and is risky.
Shadow variants are explicitly designed to minimize risk while using real data, making them the AWS best practice for pre-production validation.
Therefore, Option D is correct.

NEW QUESTION # 173
A company runs training jobs on Amazon SageMaker by using a compute optimized instance.
Demand for training runs will remain constant for the next 55 weeks. The instance needs to run for 35 hours each week. The company needs to reduce its model training costs.
Which solution will meet these requirements?

A. Use the heterogeneous cluster feature of SageMaker Training. Configure the instance_type, instance_count, and instance_groups arguments to run training jobs.
B. Opt in to a SageMaker Savings Plan with a 1-year term and an All Upfront payment. Run a SageMaker Training job on the instance.
C. Use SageMaker Edge Manager for the training. Specify the instance requirement in the edge device configuration. Run the training.
D. Use a serverless endpoint with a provisioned concurrency of 35 hours for each week. Run the training on the endpoint.

Answer: B

NEW QUESTION # 174
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?

A. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
B. Use the SageMaker Model Registry and unique tags for each model version.
C. Use the SageMaker Model Registry and model groups to catalog the models.
D. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.

Answer: C

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 # 175
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?

A. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
B. Create a model group for each category. Move the existing models into these category model groups.
C. Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.
D. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.

Answer: C

NEW QUESTION # 176
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