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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Preparation for Machine Learning28%- Transform data and perform feature engineering
  • 1. Tools: Amazon SageMaker Processing, AWS Glue DataBrew, Pandas, PySpark
  • 2. Data cleaning, normalization, and encoding
  • 3. Feature selection, transformation, and scaling
- Ensure data integrity and prepare for modeling
  • 1. Data validation, quality checks, and profiling
  • 2. Feature store usage: Amazon SageMaker Feature Store
  • 3. Data splitting: train/validation/test sets
- 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
Topic 2: Deployment and Orchestration of ML Workflows22%- 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
- 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
- Configure deployment for scalability and availability
  • 1. A/B testing and canary deployment
  • 2. Auto-scaling, load balancing, and high availability
  • 3. Infrastructure as code: AWS CloudFormation, Terraform
Topic 3: ML Solution Monitoring, Maintenance, and Security24%- Optimize and maintain workloads
  • 1. Cost optimization and resource management
  • 2. Model retraining and update strategies
  • 3. Logging, auditing, and troubleshooting
- Monitor model and data quality
  • 1. Amazon SageMaker Model Monitor
  • 2. Performance monitoring and alerting
  • 3. Model drift detection: data drift, concept drift
- Secure ML solutions and resources
  • 1. Data encryption: at rest and in transit
  • 2. Access control: IAM roles, policies, permissions
  • 3. Compliance, governance, and data privacy
Topic 4: ML Model Development26%- Select appropriate modeling approach
  • 1. Problem type: classification, regression, clustering, forecasting, NLP, computer vision
  • 2. Algorithm selection: traditional ML, deep learning, pre-built models
  • 3. Use cases and service recommendations
- 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. Bias detection and mitigation
  • 2. Model explainability: Amazon SageMaker Clarify
  • 3. Metrics: accuracy, precision, recall, F1, RMSE, MAE, confusion matrix

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q127-Q132):

NEW QUESTION # 127
A company is developing an internal cost-estimation tool that uses an ML model in Amazon SageMaker AI.
Users upload high-resolution images to the tool.
The model must process each image and predict the cost of the object in the image. The model also must notify the user when processing is complete.
Which solution will meet these requirements?

Answer: A

Explanation:
This use case involves large payloads (high-resolution images), variable processing time, and a requirement to notify users upon completion. AWS documentation recommends Amazon SageMaker asynchronous inference for exactly this scenario.
Asynchronous inference allows clients to submit inference requests without waiting for a response. The request payload is stored in Amazon S3, and the inference result is written back to S3 when processing is complete. This approach is designed for workloads that cannot meet real-time latency requirements and where payload sizes exceed real-time limits.
SageMaker asynchronous inference integrates natively with Amazon SNS to send notifications when inference completes or fails, which directly satisfies the user notification requirement.
Batch transform jobs are intended for offline, scheduled processing of large datasets and do not provide built- in user notification mechanisms. Amazon EFS is not required because SageMaker natively integrates with S3 for inference input and output.
AWS explicitly documents asynchronous inference as the preferred solution for large image inference with completion notifications.
Therefore, Option B is the correct and AWS-verified answer.


NEW QUESTION # 128
A company is using Amazon EMR. The company has a large dataset in Amazon S3 that needs to be ingested into Amazon SageMaker Feature Store. The dataset contains historical data and real-time streaming data.
The company must ensure that the Feature Store online store is updated with the most recent data as soon as the data becomes available. The company also must maintain a complete Feature Store offline store for batch processing.
Which solution will meet these requirements?

Answer: A

Explanation:
The SageMaker Feature Store Spark connector allows ingestion of large-scale data from Amazon EMR into Feature Store as Spark DataFrames. Enabling both the online store ensures real-time updates for the latest data, while the offline store maintains the full historical dataset for batch analytics. This setup meets both low-latency and historical processing requirements.


NEW QUESTION # 129
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?

Answer: C

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 # 130
A customer call center uses Amazon Transcribe to convert hundreds of audio recordings of conversations between customers and support agents to text files. The call center wants to use the text files to train an ML model. To comply with industry regulations, the call center must remove customer names, addresses, and phone numbers from the training text files.
Which solution will meet these requirements with the LEAST development effort?

Answer: D

Explanation:
Option B is correct because AWS Glue provides a built-in Detect PII transform that can detect, mask, or remove personally identifiable information with minimal custom development. AWS documentation says the Detect PII transform can process predefined AWS-managed PII entity types and supports actions such as removing or masking values. The examples in AWS docs explicitly mention sensitive entities such as phone numbers and addresses , which directly match the problem statement.
The question specifically asks for the least development effort . That wording makes AWS Glue Detect PII the strongest answer because it is a native transformation capability rather than a custom code-heavy workflow. AWS also documents fine-grained sensitive data detection features that let you apply actions per entity type, improving usability and reducing the need to build custom parsing and redaction logic yourself.
This is much easier than creating Lambda-based transformation code or custom text-cleaning logic inside another ML preprocessing tool.
The other options are less suitable. Amazon Bedrock Guardrails is not the standard AWS service documented for bulk ETL-style redaction of training text files in this context. S3 Object Lambda would require more custom engineering to inspect and redact each object. SageMaker Data Wrangler custom transformation would also involve extra implementation work compared with using a purpose-built Glue transform. Because the call center already has text output and simply needs regulated fields like names, addresses, and phone numbers removed before training, the AWS-native low-effort solution is AWS Glue Detect PII . Therefore, the best verified answer is B .


NEW QUESTION # 131
An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model.
Select and order the steps from the following list to create and use the features in Feature Store. Each step should be selected one time. (Select and order three.)
* Access the store to build datasets for training.
* Create a feature group.
* Ingest the records.

Answer:

Explanation:

Explanation:

Step 1: Create a feature group.
Step 2: Ingest the records.
Step 3: Access the store to build datasets for training.
Step 1: Create a Feature Group
Why? A feature group is the foundational unit in SageMaker Feature Store, where features are defined, stored, and organized. Creating a feature group specifies the schema (name, data type) for the features and the primary keys for data identification.
How? Use the SageMaker Python SDK or AWS CLI to define the feature group by specifying its name, schema, and S3 storage location for offline access.
Step 2: Ingest the Records
Why? After creating the feature group, the raw data must be ingested into the Feature Store. This step populates the feature group with data, making it available for both real-time and offline use.
How? Use the SageMaker SDK or AWS CLI to batch-ingest historical data or stream new records into the feature group. Ensure the records conform to the feature group schema.
Step 3: Access the Store to Build Datasets for Training
Why? Once the features are stored, they can be accessed to create training datasets. These datasets combine relevant features into a single format for machine learning model training.
How? Use the SageMaker Python SDK to query the offline store or retrieve real-time features using the online store API. The offline store is typically used for batch training, while the online store is used for inference.
Order Summary:
Create a feature group.
Ingest the records.
Access the store to build datasets for training.
This process ensures the features are properly managed, ingested, and accessible for model training using Amazon SageMaker Feature Store.


NEW QUESTION # 132
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