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Amazon MLA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Processing	22%	- Data preprocessing and feature engineering - Data validation and quality assessment - Handling imbalanced data - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Data ingestion and transformation
Model Development	30%	- Training and validation strategies - Transfer learning and fine-tuning - Distributed training - Hyperparameter optimization - Algorithm selection and model architecture - ML frameworks (SageMaker, built-in algorithms)
Model Deployment and Inference	20%	- A/B testing and shadow mode deployment - SageMaker endpoints configuration - Model deployment strategies (real-time, batch) - Inference optimization (latency, throughput) - Model versioning and rollback
MLOps and Monitoring	28%	- Cost optimization for ML workloads - Incident response and remediation - CI/CD pipelines for ML - Security and access management for ML - Model monitoring and drift detection - Model lineage and reproducibility

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q64-Q69):

NEW QUESTION # 64
A company must install a custom script on any newly created Amazon SageMaker notebook instances.
Which solution will meet this requirement with the LEAST operational overhead?

A. Create a lifecycle configuration script to install the custom script when a new SageMaker notebook is created. Attach the lifecycle configuration to every new SageMaker notebook as part of the creation steps.
B. Create a custom Amazon Elastic Container Registry (Amazon ECR) image that contains the custom script. Push the ECR image to a Docker registry. Attach the Docker image to a SageMaker Studio domain. Select the kernel to run as part of the SageMaker notebook.
C. Create a custom package index repository. Use AWS CodeArtifact to manage the installation of the custom script. Set up AWS PrivateLink endpoints to connect CodeArtifact to the SageMaker instance. Install the script.
D. Store the custom script in Amazon S3. Create an AWS Lambda function to install the custom script on new SageMaker notebooks. Configure Amazon EventBridge to invoke the Lambda function when a new SageMaker notebook is initialized.

Answer: A

NEW QUESTION # 65
An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.
Which instance purchasing option will meet these requirements MOST cost-effectively?

A. Run the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.
B. Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.
C. Run the primary node, core nodes, and task nodes on Spot Instances.
D. Run the primary node, core nodes, and task nodes on On-Demand Instances.

Answer: B

Explanation:
For Amazon EMR, the primary node and core nodes handle the critical functions of the cluster, including data storage (HDFS) and processing. Running them on On-Demand Instances ensures high availability and prevents data loss, as Spot Instances can be interrupted. The task nodes, which handle additionalprocessing but do not store data, can use Spot Instances to reduce costs without compromising the cluster's resilience or data integrity. This configuration balances cost-effectiveness and reliability.

NEW QUESTION # 66
A company plans to deploy an ML model for production inference on an Amazon SageMaker endpoint. The average inference payload size will vary from 100 MB to 300 MB. Inference requests must be processed in 60 minutes or less.
Which SageMaker inference option will meet these requirements?

A. Real-time inference
B. Serverless inference
C. Asynchronous inference
D. Batch transform

Answer: C

NEW QUESTION # 67
An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host.
Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

A. AWS::SageMaker::NotebookInstance
B. AWS::SageMaker::Model
C. AWS::SageMaker::Pipeline
D. AWS::SageMaker::Endpoint

Answer: B

NEW QUESTION # 68
A company wants to improve the sustainability of its ML operations.
Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Ground Truth for data labeling.
B. Use AWS Trainium instances for training.
C. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
D. Use PyTorch or TensorFlow with the distributed training option.
E. Deploy models by using AWS Lambda functions.

Answer: B,C

Explanation:
SageMaker Debuggercan identify when a training job is not converging or is stuck in a non-productive state.
By stopping these jobs early, unnecessary energy and computational resources are conserved, improving sustainability.
AWS Trainiuminstances are purpose-built for ML training and are optimized for energy efficiency and cost- effectiveness. They use less energy per training task compared to general-purpose instances, making them a sustainable choice.

NEW QUESTION # 69
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