Sample MLA-C01 Exam - MLA-C01 Latest Exam Format

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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: ML Solution Monitoring, Maintenance, and Security24%- Monitor model and data quality
  • 1. Performance monitoring and alerting
  • 2. Model drift detection: data drift, concept drift
  • 3. Amazon SageMaker Model Monitor
- Optimize and maintain workloads
  • 1. Logging, auditing, and troubleshooting
  • 2. Cost optimization and resource management
  • 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
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. 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
Topic 3: ML Model Development26%- 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
- 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. Training options: built-in algorithms, custom containers, frameworks
  • 2. Distributed training and managed services
  • 3. Hyperparameter optimization: Amazon SageMaker Automatic Model Tuning
Topic 4: Data Preparation for Machine Learning28%- Ingest and store data
  • 1. Data storage options: object storage, data lakes, databases
  • 2. Data formats: Parquet, JSON, CSV, ORC, Avro, RecordIO
  • 3. Data ingestion services: Amazon Kinesis, AWS Glue, Amazon S3, Amazon Athena
- Ensure data integrity and prepare for modeling
  • 1. Data validation, quality checks, and profiling
  • 2. Data splitting: train/validation/test sets
  • 3. Feature store usage: Amazon SageMaker Feature Store
- 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

>> Sample MLA-C01 Exam <<

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q188-Q193):

NEW QUESTION # 188
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?

Answer: C

Explanation:
Option A is correct because the issue is that the lead ML engineer's local notebook environment does not yet contain the latest merged changes from the shared Git repository. In Git, the operation used to fetch remote updates and merge them into the local branch is git pull . AWS documentation for SageMaker notebook collaboration explains that Git repositories can be associated with SageMaker notebook instances so distributed teams can share notebooks and collaborate in a source-control environment.
AWS documentation is even more explicit in the SageMaker Git operations guide: to fetch notebooks committed by other users , you should do a pull from the project repository . That is exactly the situation described in the question. Other team members have already merged their notebook changes into the repository, and the lead engineer needs the latest notebook updates in the local SageMaker notebook environment. Therefore, running !git pull origin master is the correct command among the available choices.
The other options do not solve the problem. git commit records local changes in the user's own repository history; it does not download new remote content. git push origin master uploads local commits to the remote repository; it is used when you want to send your own changes outward, not retrieve teammates' updates. git branch only lists or manages branches and does not synchronize notebook content. Because the requirement is to see the latest merged notebook made by others , the necessary operation is a pull from the remote repository. So the best verified answer is A .


NEW QUESTION # 189
A company has a binary classification model in production. An ML engineer needs to develop a new version of the model.
The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements.
Which metric should the ML engineer use for the model recalibration?

Answer: C

Explanation:
Accuracy measures the proportion of correctly predicted labels (both positive and negative) out of the total predictions. It is the appropriate metric when the goal is to maximize the correct predictions of both positive and negative labels. However, it assumes that the classes are balanced; if the classes are imbalanced, other metrics like precision, recall, or specificity may be more relevant depending on the specific needs.


NEW QUESTION # 190
An ML engineer notices class imbalance in an image classification training job.
What should the ML engineer do to resolve this issue?

Answer: C


NEW QUESTION # 191
A company uses Amazon SageMaker for its ML process. A compliance audit discovers that an Amazon S3 bucket for training data uses server-side encryption with S3 managed keys (SSE- S3).
The company requires customer managed keys. An ML engineer changes the S3 bucket to use server-side encryption with AWS KMS keys (SSE-KMS). The ML engineer makes no other configuration changes.
After the change to the encryption settings, SageMaker training jobs start to fail with AccessDenied errors.
What should the ML engineer do to resolve this problem?

Answer: A


NEW QUESTION # 192
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account # in the same Region.
Which solution will meet this requirement with the LEAST development effort?

Answer: B

Explanation:
Amazon Comprehend provides the ImportModel API operation, which allows you to copy a custom model between AWS accounts. By creating a resource-based IAM policy on the model in Account A, you can grant Account B the necessary permissions to access and import the model. This approach requires minimal development effort and is the AWS-recommended method for sharing custom models across accounts.


NEW QUESTION # 193
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