MLA-C01 Examsfragen, MLA-C01 Zertifizierungsfragen

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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
ML Solution Monitoring, Maintenance, and Security24%- Optimize and maintain workloads
  • 1. Logging, auditing, and troubleshooting
  • 2. Model retraining and update strategies
  • 3. Cost optimization and resource management
- Secure ML solutions and resources
  • 1. Compliance, governance, and data privacy
  • 2. Access control: IAM roles, policies, permissions
  • 3. Data encryption: at rest and in transit
- Monitor model and data quality
  • 1. Performance monitoring and alerting
  • 2. Model drift detection: data drift, concept drift
  • 3. Amazon SageMaker Model Monitor
Data Preparation for Machine Learning28%- Ensure data integrity and prepare for modeling
  • 1. Feature store usage: Amazon SageMaker Feature Store
  • 2. Data splitting: train/validation/test sets
  • 3. Data validation, quality checks, and profiling
- 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
- 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
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. 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
ML Model Development26%- Evaluate and analyze model performance
  • 1. Model explainability: Amazon SageMaker Clarify
  • 2. Bias detection and mitigation
  • 3. Metrics: accuracy, precision, recall, F1, RMSE, MAE, confusion matrix
- 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
- 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

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MLA-C01 Zertifizierungsfragen & MLA-C01 Exam Fragen

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Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Prüfungsfragen mit Lösungen (Q39-Q44):

39. Frage
A company's dataset for prediction analytics contains duplicate records, missing data, and unusually extreme high or low values. The company needs a solution to resolve the data quality issues quickly. The solution must maintain data integrity and have the LEAST operational overhead.
Which solution will meet these requirements?

Antwort: D

Begründung:
AWS Glue DataBrew is designed specifically for no-code and low-code data preparation, making it the fastest and lowest-overhead solution for resolving common data quality issues. DataBrew provides built-in transformations for deduplication, missing value imputation, and outlier handling while preserving data integrity.
Option A uses standard statistical techniques such as median imputation and value normalization, which are widely accepted and maintain the distribution of the data. DataBrew jobs are fully managed and do not require infrastructure setup or maintenance.
Option B deletes records, which can lead to data loss and does not preserve integrity. Option C introduces unnecessary infrastructure complexity and uses poor data imputation practices. Option D provides advanced capabilities but requires more configuration and ML expertise, increasing operational overhead.
AWS documentation clearly positions DataBrew as the preferred solution for quick, reliable data cleaning with minimal effort.
Therefore, Option A is the correct answer.


40. Frage
A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.
The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.
Which change to the architecture will meet these requirements?

Antwort: D


41. Frage
An ML engineer decides to use Amazon SageMaker AI automated model tuning (AMT) for hyperparameter optimization (HPO). The ML engineer requires a tuning strategy that uses regression to slowly and sequentially select the next set of hyperparameters based on previous runs. The strategy must work across small hyperparameter ranges.
Which solution will meet these requirements?

Antwort: D

Begründung:
Amazon SageMaker Automated Model Tuning supports several hyperparameter search strategies. Bayesian optimization is explicitly designed to model the relationship between hyperparameters and objective metrics using regression techniques. Based on results from previous training jobs, Bayesian optimization predicts which hyperparameter combinations are most likely to improve model performance and evaluates those next.
AWS documentation highlights Bayesian optimization as the preferred strategy when the hyperparameter search space is small to medium and when training jobs are expensive. Because the algorithm learns from prior runs, it avoids wasting resources on unpromising configurations and converges efficiently.
Grid search exhaustively evaluates all combinations and becomes inefficient even with moderately sized search spaces. Random search does not use information from prior runs and is less efficient. Hyperband focuses on aggressive early stopping and resource allocation, not regression-based sequential selection.
Therefore, Option C is the correct and AWS-verified solution.


42. Frage
A company is exploring generative AI and wants to add a new product feature. An ML engineer is making API calls from existing Amazon EC2 instances to Amazon Bedrock.
The EC2 instances are in a private subnet and must remain private during the implementation. The EC2 instances have a security group that allows access to all IP addresses in the private subnet.
What should the ML engineer do to establish a connection between the EC2 instances and Amazon Bedrock?

Antwort: D

Begründung:
Amazon Bedrock is a fully managed AWS service and does not reside inside customer VPCs. To allow private connectivity from EC2 instances in a private subnet without using the public internet, AWS documentation recommends AWS PrivateLink.
By creating an interface VPC endpoint for Amazon Bedrock, traffic between the EC2 instances and Bedrock remains entirely within the AWS network. This approach preserves the private subnet configuration and meets strict security requirements.
Security groups alone cannot establish connectivity to a managed service endpoint. Amazon Bedrock cannot be deployed into a customer subnet, making Option C invalid. AWS Direct Connect is designed for on- premises connectivity, not for accessing AWS managed services from within a VPC.
AWS explicitly documents that PrivateLink is the correct mechanism for private access to Amazon Bedrock.
Therefore, Option B is the correct and AWS-verified answer.


43. Frage
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize production inference data in the same way before passing the data to the model.
Which solution will meet this requirement?

Antwort: B

Begründung:
AWS ML best practices state that data preprocessing applied during training must be applied identically during inference. For min-max normalization, this requires reusing the minimum and maximum values calculated from the training dataset.
If production data is normalized using different statistics, the feature distributions will differ from what the model learned, leading to degraded prediction accuracy. AWS documentation explicitly warns against recomputing normalization parameters on inference data.
Options A, C, and D introduce data leakage or inconsistent feature scaling. Option B ensures consistency between training and inference pipelines and preserves model integrity.
Therefore, Option B is the correct and AWS-aligned solution.


44. Frage
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