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Amazon MLA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Deployment and Orchestration of ML Workflows	22%	- Choose deployment infrastructure and pattern 1. Infrastructure: Amazon SageMaker endpoints, AWS Lambda, Amazon ECS, Amazon EKS 2. Model packaging and versioning: Amazon SageMaker Model Registry 3. Real-time inference, batch transform, serverless, edge deployment - Configure deployment for scalability and availability 1. Infrastructure as code: AWS CloudFormation, Terraform 2. Auto-scaling, load balancing, and high availability 3. A/B testing and canary deployment - Automate and orchestrate ML pipelines 1. CI/CD integration: AWS CodePipeline, AWS CodeBuild 2. Workflow automation and event-driven processing 3. ML pipelines: Amazon SageMaker Pipelines
Data Preparation for Machine Learning	28%	- Transform data and perform feature engineering 1. Feature selection, transformation, and scaling 2. Tools: Amazon SageMaker Processing, AWS Glue DataBrew, Pandas, PySpark 3. Data cleaning, normalization, and encoding - Ingest and store data 1. Data formats: Parquet, JSON, CSV, ORC, Avro, RecordIO 2. Data ingestion services: Amazon Kinesis, AWS Glue, Amazon S3, Amazon Athena 3. Data storage options: object storage, data lakes, databases - Ensure data integrity and prepare for modeling 1. Feature store usage: Amazon SageMaker Feature Store 2. Data validation, quality checks, and profiling 3. Data splitting: train/validation/test sets
ML Solution Monitoring, Maintenance, and Security	24%	- Secure ML solutions and resources 1. Data encryption: at rest and in transit 2. Compliance, governance, and data privacy 3. Access control: IAM roles, policies, permissions - Optimize and maintain workloads 1. Logging, auditing, and troubleshooting 2. Model retraining and update strategies 3. Cost optimization and resource management - Monitor model and data quality 1. Performance monitoring and alerting 2. Model drift detection: data drift, concept drift 3. Amazon SageMaker Model Monitor
ML Model Development	26%	- Train, tune, and refine models 1. Training options: built-in algorithms, custom containers, frameworks 2. Hyperparameter optimization: Amazon SageMaker Automatic Model Tuning 3. Distributed training and managed services - Select appropriate modeling approach 1. Problem type: classification, regression, clustering, forecasting, NLP, computer vision 2. Use cases and service recommendations 3. Algorithm selection: traditional ML, deep learning, pre-built models - Evaluate and analyze model performance 1. Model explainability: Amazon SageMaker Clarify 2. Metrics: accuracy, precision, recall, F1, RMSE, MAE, confusion matrix 3. Bias detection and mitigation

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q74-Q79):

NEW QUESTION # 74
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?

A. Create the S3 bucket policy in Account B. Attach the IAM policy to an IAM role that SageMaker AI uses in Account B.
B. Create the S3 bucket policy in Account A. Attach the IAM policy to an IAM role that SageMaker AI uses in Account A.
C. Create the S3 bucket policy in Account A. Attach the IAM policy to an IAM role that SageMaker AI uses in Account B.
D. Create the S3 bucket policy in Account B. Attach the IAM policy to an IAM role that SageMaker AI uses in Account A.

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 # 75
An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.
The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Use Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.
B. Configure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.
C. Store users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.
D. Configure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.

Answer: A

Explanation:
AWS Lake Formation provides fine-grained access control and simplifies data governance for data lakes. By configuring Lake Formation tags to map ML engineers to their specific campaigns, you can restrict access to both structured and unstructured data in the data lake. This method is operationally efficient, as it centralizes access control management within Lake Formation and ensures consistency across Amazon Athena and S3 bucket access without requiring manual updates to policies or DynamoDB-based custom logic.

NEW QUESTION # 76
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?

A. Manually install the libraries in the SageMaker containers.
B. Run code for the libraries externally on Amazon EC2 instances. Store the results in Amazon S3.Import the results into the SageMaker jobs and pipelines.
C. Create a SageMaker notebook instance to host the jobs. Create an AWS Lambda function to install the libraries on the notebook instance when the notebook instance starts. Configure the SageMaker jobs and pipelines to run on the notebook instance.
D. Build a custom Docker container that includes the required libraries. Host the container in Amazon Elastic Container Registry (Amazon ECR). Use the ECR image in the SageMaker jobs and pipelines.

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 # 77
An ML engineer wants to use, prepare, and load data from Amazon S3 for analytics. The ML engineer must run an extract, transform, and load (ETL) job to discover the schema of the data and to store the metadata.
Which solution will meet these requirements with the LEAST manual effort?

A. Create an Amazon SageMaker Data Wrangler flow to run the ETL job. Use the job to discover the schema and to store the associated metadata in an S3 bucket.
B. Launch an Amazon EC2 instance that includes the scikit-learn library to run the ETL job. Use the job to discover the schema and to store the associated metadata in Amazon Redshift.
C. Use AWS Glue to run the ETL job. Use the job to discover the schema and to store the associated metadata in the AWS Glue Data Catalog.
D. Create an ETL pipeline by using Amazon Athena integrated with AWS Step Functions. Use the pipeline to run the ETL job to discover the schema and to store the associated metadata in an S3 bucket.

Answer: C

Explanation:
Option A is correct because AWS Glue is the AWS-native managed ETL service built specifically to discover schema , run ETL jobs , and store metadata in the AWS Glue Data Catalog . AWS documentation states that Glue crawlers can automatically discover and catalog new or updated data sources , and that the Data Catalog automatically captures and manages schema metadata. This directly matches the requirement to run an ETL job on data in Amazon S3, discover the schema, and store the metadata with the least manual effort.
AWS Glue is also the lowest-effort answer because the service is managed and purpose-built for this workflow. The Glue Data Catalog serves as a persistent metadata repository, and AWS documents that crawlers infer schema information and integrate it into the catalog automatically. That means the ML engineer does not need to build custom schema inference logic or manually maintain metadata storage. This is exactly the kind of manual work the question is trying to avoid.
The other options are not as good. SageMaker Data Wrangler is primarily for visual data preparation and feature engineering, not for running a managed ETL-plus-catalog workflow with schema stored in a metadata catalog. Athena with Step Functions would require assembling more custom orchestration and still does not naturally replace the Glue Data Catalog workflow. Launching an EC2 instance introduces the highest operational overhead and does not align with the requirement for least manual effort. Therefore, the best verified AWS-docs answer is A , because AWS Glue combines ETL, schema discovery, and metadata cataloging in one managed service.

NEW QUESTION # 78
A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company needs to improve training performance.
Which solution will meet these requirements in the LEAST amount of time?

A. Create an Amazon Elastic File System (Amazon EFS) file system. Transfer the existing data to the file system. Adjust the training job to read from the file system.
B. Transfer the data to a new S3 bucket that provides S3 Express One Zone storage. Adjust the training job to use the new S3 bucket.
C. Create an Amazon FSx for Lustre file system. Link the file system to the existing S3 bucket. Adjust the training job to read from the file system.
D. Create an Amazon ElastiCache (Redis OSS) cluster. Link the Redis OSS cluster to the existing S3 bucket. Stream the data from the Redis OSS cluster directly to the training job.

Answer: C

Explanation:
Amazon FSx for Lustre is designed for high-performance workloads like ML training. It provides fast, low- latency access to data by linking directly to the existing S3 bucket and caching frequently accessed files locally. This significantly improves training performance compared to directly accessing millions of files from S3. It requires minimal changes to the training job and avoids the overhead of transferring or restructuring data, making it the fastest and most efficient solution.

NEW QUESTION # 79
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