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Amazon MLA-C01 Exam Syllabus Topics:

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

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q233-Q238):

NEW QUESTION # 233
An ML engineering team is spread across multiple locations. When the lead ML engineer opens an Amazon SageMaker AI notebook, the ML engineer does not see the latest merged notebook made by other team members from a Git repository.
The lead ML engineer must see the latest SageMaker AI notebook updates.
Which solution will meet this requirement?

A. Run the !git commit command.
B. Run the !git branch command.
C. Run the !git pull origin master command.
D. Run the !git push origin master command.

Answer: C

NEW QUESTION # 234
A medical company needs to store clinical data. The data includes personally identifiable information (PII) and protected health information (PHI).
An ML engineer needs to implement a solution to ensure that the PII and PHI are not used to train ML models.
Which solution will meet these requirements?

A. Upload the clinical data to an Amazon Redshift database. Use built-in SQL stored procedures to automatically classify and mask the PII and PHI before the data is used for model training.
B. Use Amazon Comprehend to detect and mask the PII before the data is used for model training.
Use Amazon Comprehend Medical to detect and mask the PHI before the data is used for model training.
C. Create an AWS Lambda function to encrypt the PII and PHI. Program the Lambda function to save the encrypted data to an Amazon S3 bucket for model training.
D. Store the clinical data in Amazon S3 buckets. Use AWS Glue DataBrew to mask the PII and PHI before the data is used for model training.

Answer: B

NEW QUESTION # 235
A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.
Which solution on AWS will detect duplicates in the dataset with the LEAST code development?

A. Use Amazon QuickSight ML Insights to build a custom deduplication model.
B. Use the AWS Glue FindMatches transform to detect duplicates.
C. Use Amazon SageMaker Data Wrangler to pre-process and detect duplicates.
D. Use Amazon Mechanical Turk jobs to detect duplicates.

Answer: B

Explanation:
Scenario:The dataset contains duplicate records that need to be detected with minimal code development.
Why FindMatches in AWS Glue?
* Purpose-Built for Deduplication:The FindMatches transform in AWS Glue is specifically designed to identify duplicate records in structured or semi-structured datasets.
* Machine Learning-Based:It uses ML to identify duplicates based on configurable thresholds and provides flexibility for tuning accuracy.
* Low Code Overhead:Minimal development effort is required as Glue provides an interactive console for configuring and running FindMatches transforms.
Steps to Implement:
* Prepare the Data:Upload the unstructured dataset to an S3 bucket and define a schema if needed.
* Create a Glue Job:
* Use the AWS Glue Studio to create a job and select the FindMatches transform.
* Specify key attributes for deduplication.
* Run and Evaluate:Execute the Glue job, and review the results for duplicates.
* Resolve Duplicates:Export results to an S3 bucket or process them as needed.
References:
* AWS Glue FindMatches Documentation
* FindMatches Transform Example

NEW QUESTION # 236
A company has an ML model that needs to run one time each night to predict stock values. The model input is
3 MB of data that is collected during the current day. The model produces the predictions for the next day.
The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?

A. Use a multi-model serverless endpoint. Enable caching.
B. Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.
C. Use a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.
D. Use an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.

Answer: B

Explanation:
A serverless inference endpoint in Amazon SageMaker is ideal for use cases where the model is invoked infrequently, such as running one time each night. It eliminates the cost of idle resources when the model is not in use. Setting the MaxConcurrency parameter to 1 ensures cost-efficiency while supporting the required single nightly invocation. This solution minimizes costs and matches the requirement to process a small amount of data quickly.

NEW QUESTION # 237
A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS.
Which solution will meet these requirements with the LEAST development effort?

A. Use SageMaker AI script mode and premade images for ML frameworks.
B. Purchase similar production models through AWS Marketplace.
C. Build a container on AWS that includes custom packages and a choice of ML frameworks.
D. Use SageMaker AI built-in algorithms to train the proprietary datasets.

Answer: A

Explanation:
The company already has custom Python training scripts, proprietary datasets, and uses PyTorch, with significant domain-specific logic embedded in the model. The goal is to migrate these workloads to AWS with the least development effort.
According to AWS documentation, Amazon SageMaker AI script mode is explicitly designed for this scenario. Script mode allows customers to bring their existing training scripts with minimal or no code changes and run them using prebuilt SageMaker framework containers, including PyTorch. This approach eliminates the need to redesign models or rewrite training logic while still benefiting from SageMaker's managed infrastructure, scalability, monitoring, and security.
Option A is incorrect because SageMaker built-in algorithms require adapting data formats and training logic to AWS-provided implementations, which would increase development effort and may not support proprietary domain logic.
Option C is also incorrect because building and maintaining a custom container requires additional effort for containerization, dependency management, security updates, and lifecycle maintenance-making it more complex than necessary.
Option D is not viable because purchasing models from AWS Marketplace would not support the company's proprietary datasets or unique domain knowledge embedded in existing models.
Therefore, using SageMaker AI script mode with prebuilt PyTorch containers is the fastest, most efficient, and AWS-recommended migration path that minimizes development effort while preserving existing workflows.

NEW QUESTION # 238
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