The Tester's Handbook: MLA-C01 Online Test Engine

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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Deployment and Orchestration of ML Workflows22%- Automate and orchestrate ML pipelines
  • 1. ML pipelines: Amazon SageMaker Pipelines
  • 2. CI/CD integration: AWS CodePipeline, AWS CodeBuild
  • 3. Workflow automation and event-driven processing
- 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
- Choose deployment infrastructure and pattern
  • 1. Real-time inference, batch transform, serverless, edge deployment
  • 2. Infrastructure: Amazon SageMaker endpoints, AWS Lambda, Amazon ECS, Amazon EKS
  • 3. Model packaging and versioning: Amazon SageMaker Model Registry
Topic 2: ML Solution Monitoring, Maintenance, and Security24%- Monitor model and data quality
  • 1. Model drift detection: data drift, concept drift
  • 2. Performance monitoring and alerting
  • 3. Amazon SageMaker Model Monitor
- 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
- Optimize and maintain workloads
  • 1. Model retraining and update strategies
  • 2. Cost optimization and resource management
  • 3. Logging, auditing, and troubleshooting
Topic 3: ML Model Development26%- 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
- Select appropriate modeling approach
  • 1. Algorithm selection: traditional ML, deep learning, pre-built models
  • 2. Problem type: classification, regression, clustering, forecasting, NLP, computer vision
  • 3. Use cases and service recommendations
- Train, tune, and refine models
  • 1. Distributed training and managed services
  • 2. Training options: built-in algorithms, custom containers, frameworks
  • 3. Hyperparameter optimization: Amazon SageMaker Automatic Model Tuning
Topic 4: Data Preparation for Machine Learning28%- 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. Data validation, quality checks, and profiling
  • 3. Feature store usage: Amazon SageMaker Feature Store
- Ingest and store data
  • 1. Data formats: Parquet, JSON, CSV, ORC, Avro, RecordIO
  • 2. Data storage options: object storage, data lakes, databases
  • 3. Data ingestion services: Amazon Kinesis, AWS Glue, Amazon S3, Amazon Athena

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q122-Q127):

NEW QUESTION # 122
A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups.
The company uses a single AWS account and stores all the training data in Amazon S3 buckets.
All ML model training occurs in Amazon SageMaker.
Which solution will provide the ML engineers with the appropriate access?

Answer: B


NEW QUESTION # 123
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 # 124
A company is using Amazon SageMaker to develop ML models. The company stores sensitive training data in an Amazon S3 bucket. The model training must have network isolation from the internet.
Which solution will meet this requirement?

Answer: D


NEW QUESTION # 125
A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

Answer: C


NEW QUESTION # 126
An ML engineer needs to use an ML model to predict the price of apartments in a specific location.
Which metric should the ML engineer use to evaluate the model's performance?

Answer: B


NEW QUESTION # 127
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