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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: ML Solution Monitoring, Maintenance, and Security24%- Optimize and maintain workloads
  • 1. Cost optimization and resource management
  • 2. Logging, auditing, and troubleshooting
  • 3. Model retraining and update strategies
- 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
- Monitor model and data quality
  • 1. Amazon SageMaker Model Monitor
  • 2. Performance monitoring and alerting
  • 3. Model drift detection: data drift, concept drift
Topic 2: Deployment and Orchestration of ML Workflows22%- Configure deployment for scalability and availability
  • 1. Auto-scaling, load balancing, and high availability
  • 2. Infrastructure as code: AWS CloudFormation, Terraform
  • 3. A/B testing and canary deployment
- 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. ML pipelines: Amazon SageMaker Pipelines
  • 2. CI/CD integration: AWS CodePipeline, AWS CodeBuild
  • 3. Workflow automation and event-driven processing
Topic 3: Data Preparation for Machine Learning28%- 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
- 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
- Transform data and perform feature engineering
  • 1. Tools: Amazon SageMaker Processing, AWS Glue DataBrew, Pandas, PySpark
  • 2. Feature selection, transformation, and scaling
  • 3. Data cleaning, normalization, and encoding
Topic 4: ML Model Development26%- Select appropriate modeling approach
  • 1. Use cases and service recommendations
  • 2. Problem type: classification, regression, clustering, forecasting, NLP, computer vision
  • 3. Algorithm selection: traditional ML, deep learning, pre-built models
- 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
- 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 (Q93-Q98):

NEW QUESTION # 93
A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. The team's leaders need to accelerate the training process.
What can a machine learning specialist do to address this concern?

Answer: C

Explanation:
Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances.


NEW QUESTION # 94
A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.
A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.
Which solution will meet these requirements with the LEAST implementation effort?

Answer: C

Explanation:
Dynamic data maskingallows you to control how sensitive data is presented to users at query time, without modifying or storing transformed versions of the source data. Amazon Redshift supports dynamic data masking, which can be implemented with minimal effort. This solution ensures that the data scientistcan access the required information while sensitive data remains protected, meeting the requirements efficiently and with the least implementation effort.


NEW QUESTION # 95
An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
Amazon Comprehend provides pre-built functionality for key phrase extraction and can identify meaningful keywords from documents with minimal setup or operational overhead. It eliminates the need for manual preprocessing, stemming, or stop-word removal and does not require custom model development or infrastructure management. This makes it the most efficient and low-maintenance solution for the task.


NEW QUESTION # 96
A company is developing a new online application to gather information from customers. An ML engineer has developed a new ML model that will determine a score for each customer. The model will use the score to determine which product to display to the customer. The ML engineer needs to minimize response-time latency for the model. How should the ML engineer deploy the application in Amazon SageMaker to meet these requirements?

Answer: B

Explanation:
To minimize response-time latency, the ML model should be deployed to a real-time inference endpoint in Amazon SageMaker. This provides low-latency predictions by keeping the model loaded and ready to handle incoming requests, which is critical for an online application serving customers in real time.


NEW QUESTION # 97
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?

Answer: A


NEW QUESTION # 98
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