MLA-C01 Deutsche, MLA-C01 Originale Fragen

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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. Model drift detection: data drift, concept drift
  • 2. Performance monitoring and alerting
  • 3. Amazon SageMaker Model Monitor
- 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. Compliance, governance, and data privacy
  • 2. Data encryption: at rest and in transit
  • 3. Access control: IAM roles, policies, permissions
Topic 2: ML Model Development26%- Evaluate and analyze model performance
  • 1. Bias detection and mitigation
  • 2. Metrics: accuracy, precision, recall, F1, RMSE, MAE, confusion matrix
  • 3. Model explainability: Amazon SageMaker Clarify
- Select appropriate modeling approach
  • 1. Problem type: classification, regression, clustering, forecasting, NLP, computer vision
  • 2. Use cases and service recommendations
  • 3. Algorithm selection: traditional ML, deep learning, pre-built models
- Train, tune, and refine models
  • 1. Hyperparameter optimization: Amazon SageMaker Automatic Model Tuning
  • 2. Distributed training and managed services
  • 3. Training options: built-in algorithms, custom containers, frameworks
Topic 3: Data Preparation for Machine Learning28%- 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
- 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
- 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
Topic 4: Deployment and Orchestration of ML Workflows22%- Automate and orchestrate ML pipelines
  • 1. ML pipelines: Amazon SageMaker Pipelines
  • 2. Workflow automation and event-driven processing
  • 3. CI/CD integration: AWS CodePipeline, AWS CodeBuild
- 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

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Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Prüfungsfragen mit Lösungen (Q15-Q20):

15. Frage
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
* An S3 event notification invokes the pipeline when new data is uploaded.
* S3 Lifecycle rule invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
* The pipeline deploys the model to SageMaker Model Registry.

Antwort:

Begründung:

Explanation:
Step 1: An S3 event notification invokes the pipeline when new data is uploaded.Step 2: SageMaker retrains the model by using the data in the S3 bucket.Step 3: The pipeline deploys the model to a SageMaker endpoint.

* Step 1: An S3 Event Notification Invokes the Pipeline When New Data is Uploaded
* Why?The CI/CD pipeline should be triggered automatically whenever new training data is uploaded to Amazon S3. S3 event notifications can be configured to send events to AWS services like Lambda, which can then invoke AWS CodePipeline.
* How?Configure the S3 bucket to send event notifications (e.g., s3:ObjectCreated:*) to AWS Lambda, which in turn triggers the CodePipeline.
* Step 2: SageMaker Retrains the Model by Using the Data in the S3 Bucket
* Why?The uploaded data is used to retrain the ML model to incorporate new information and maintain performance. This step is critical to updating the model with fresh data.
* How?Define a SageMaker training step in the CI/CD pipeline, which reads the training data from the S3 bucket and retrains the model.
* Step 3: The Pipeline Deploys the Model to a SageMaker Endpoint
* Why?Once retrained, the updated model must be deployed to a SageMaker endpoint to make it available for real-time inference.
* How?Add a deployment step in the CI/CD pipeline, which automates the creation or update of the SageMaker endpoint with the retrained model.
Order Summary:
* An S3 event notification invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
This configuration ensures an automated, efficient, and scalable CI/CD pipeline for continuous retraining and deployment of the ML model in Amazon SageMaker.


16. Frage
An ML engineer at a credit card company built and deployed an ML model by using Amazon SageMaker AI.
The model was trained on transaction data that contained very few fraudulent transactions. After deployment, the model is underperforming.
What should the ML engineer do to improve the model's performance?

Antwort: A

Begründung:
This is a classic class imbalance problem, where fraudulent transactions (minority class) are severely underrepresented. AWS documentation for SageMaker Data Wrangler recommends SMOTE (Synthetic Minority Oversampling Technique) as an effective approach for improving model performance in such scenarios.
SMOTE generates synthetic minority samples by interpolating between existing minority class examples.
This improves the model's ability to learn decision boundaries without simply duplicating data, which can cause overfitting.
Random undersampling removes valuable majority class data, reducing overall model robustness. Random oversampling duplicates data and increases overfitting risk. Changing algorithms does not address the root cause.
AWS best practices highlight SMOTE as the preferred technique for fraud detection and other highly imbalanced datasets.
Therefore, Option C is the correct and AWS-verified answer.


17. Frage
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed- circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model's accuracy in the LEAST amount of time?

Antwort: C

Begründung:
The model is underperforming in production due to variations in image quality from different cameras. Using the corrupt image transform with the impulse noise option in SageMaker Data Wrangler simulates real-world noise and variations in the training dataset. This approach helps the model become more robust to inconsistencies in image quality, improving its accuracy in production without the need to collect and process new data, thereby saving time.


18. Frage
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?

Antwort: A


19. Frage
A company has trained an ML model that is packaged in a container. The company will integrate the model with an existing Python web application. The company needs to host the model on AWS by using Kubernetes.
The company does not want to manage the control plane and must provision the resources in a repeatable manner. The infrastructure must be provisioned by using Python.
Which solution will meet these requirements?

Antwort: A

Begründung:
Option C is correct because the company needs Kubernetes hosting, does not want to manage the control plane, wants repeatable infrastructure provisioning, and requires that provisioning be done by using Python.
Amazon EKS is AWS's managed Kubernetes service, so it satisfies the requirement to avoid managing the Kubernetes control plane directly. The AWS CDK documentation also confirms that Python is a fully supported client language for defining infrastructure as code.
AWS CDK is the best fit because it lets engineers define cloud infrastructure programmatically in Python and deploy it in a repeatable way. The AWS CDK EKS construct library specifically supports defining Amazon EKS clusters and related Kubernetes resources. This makes it a strong match for infrastructure that must be reproducible and expressed in code rather than provisioned manually. Since the model is already packaged in a container, storing the image in Amazon ECR and then deploying it to Amazon EKS follows the normal AWS container workflow.
The other options are less suitable. Option A requires setting up and managing a Kubernetes cluster on EC2, which violates the requirement to avoid control-plane management. Option B uses the AWS CLI, but the question specifically requires infrastructure provisioning by using Python, not command-line provisioning.
Option D uses CloudFormation, which is repeatable infrastructure as code, but the question explicitly says the infrastructure must be provisioned by using Python. AWS CDK uniquely satisfies both the IaC and Python requirements while using managed Kubernetes with EKS.
Therefore, the best verified AWS-docs answer is C.


20. Frage
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