MLA-C01学習資料、MLA-C01資格関連題

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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Data Preparation for Machine Learning28%- Ingest and store data
  • 1. Data storage options: object storage, data lakes, databases
  • 2. Data ingestion services: Amazon Kinesis, AWS Glue, Amazon S3, Amazon Athena
  • 3. Data formats: Parquet, JSON, CSV, ORC, Avro, RecordIO
- 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. Feature selection, transformation, and scaling
  • 3. Data cleaning, normalization, and encoding
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
- Automate and orchestrate ML pipelines
  • 1. Workflow automation and event-driven processing
  • 2. ML pipelines: Amazon SageMaker Pipelines
  • 3. CI/CD integration: AWS CodePipeline, AWS CodeBuild
- Choose deployment infrastructure and pattern
  • 1. Model packaging and versioning: Amazon SageMaker Model Registry
  • 2. Real-time inference, batch transform, serverless, edge deployment
  • 3. Infrastructure: Amazon SageMaker endpoints, AWS Lambda, Amazon ECS, Amazon EKS
ML Solution Monitoring, Maintenance, and Security24%- Monitor model and data quality
  • 1. Amazon SageMaker Model Monitor
  • 2. Performance monitoring and alerting
  • 3. Model drift detection: data drift, concept drift
- Secure ML solutions and resources
  • 1. Access control: IAM roles, policies, permissions
  • 2. Data encryption: at rest and in transit
  • 3. Compliance, governance, and data privacy
- Optimize and maintain workloads
  • 1. Logging, auditing, and troubleshooting
  • 2. Model retraining and update strategies
  • 3. Cost optimization and resource management
ML Model Development26%- 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
- 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
- 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

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MLA-C01試験の準備方法｜一番優秀なMLA-C01学習資料試験｜高品質なAWS Certified Machine Learning Engineer - Associate資格関連題

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Amazon AWS Certified Machine Learning Engineer - Associate 認定 MLA-C01 試験問題 (Q127-Q132):

質問 # 127
An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a re-training job if any data drift is detected.
How should the ML engineer set up the pipeline to meet this requirement?

正解：D

解説：
AWS provides Amazon SageMaker Model Monitor as a native solution for detecting data drift and model quality issues in production ML pipelines. Model Monitor continuously analyzes incoming inference data and compares it with baseline training data to identify schema drift, feature distribution drift, and data quality anomalies.
When drift thresholds are violated, Model Monitor generates CloudWatch metrics and alerts. These alerts can directly trigger an AWS Lambda function, which can then programmatically initiate a SageMaker retraining job or start a SageMaker Pipeline execution. This design is explicitly documented by AWS as the recommended architecture for automated retraining workflows.
Option A is incorrect because AWS Glue is a data integration service and does not provide ML-specific drift detection capabilities.
Option B is incorrect because Apache Flink is designed for stream processing, not ML data drift detection.
Option D is incorrect because Amazon QuickSight anomaly detection is intended for business intelligence metrics, not ML feature drift.
Therefore, using SageMaker Model Monitor with AWS Lambda automation is the correct, AWS-native solution for drift-driven retraining.


質問 # 128
An ML engineer is using a training job to fine-tune a deep learning model in Amazon SageMaker Studio. The ML engineer previously used the same pre-trained model with a similar dataset. The ML engineer expects vanishing gradient, underutilized GPU, and overfitting problems.
The ML engineer needs to implement a solution to detect these issues and to react in predefined ways when the issues occur. The solution also must provide comprehensive real-time metrics during the training.
Which solution will meet these requirements with the LEAST operational overhead?

正解：B


質問 # 129
An ML engineer wants to use, prepare, and load data from Amazon S3 for analytics. The ML engineer must run an extract, transform, and load (ETL) job to discover the schema of the data and to store the metadata.
Which solution will meet these requirements with the LEAST manual effort?

正解：C

解説：
Option A is correct because AWS Glue is the AWS-native managed ETL service built specifically to discover schema , run ETL jobs , and store metadata in the AWS Glue Data Catalog . AWS documentation states that Glue crawlers can automatically discover and catalog new or updated data sources , and that the Data Catalog automatically captures and manages schema metadata. This directly matches the requirement to run an ETL job on data in Amazon S3, discover the schema, and store the metadata with the least manual effort.
AWS Glue is also the lowest-effort answer because the service is managed and purpose-built for this workflow. The Glue Data Catalog serves as a persistent metadata repository, and AWS documents that crawlers infer schema information and integrate it into the catalog automatically. That means the ML engineer does not need to build custom schema inference logic or manually maintain metadata storage. This is exactly the kind of manual work the question is trying to avoid.
The other options are not as good. SageMaker Data Wrangler is primarily for visual data preparation and feature engineering, not for running a managed ETL-plus-catalog workflow with schema stored in a metadata catalog. Athena with Step Functions would require assembling more custom orchestration and still does not naturally replace the Glue Data Catalog workflow. Launching an EC2 instance introduces the highest operational overhead and does not align with the requirement for least manual effort. Therefore, the best verified AWS-docs answer is A , because AWS Glue combines ETL, schema discovery, and metadata cataloging in one managed service.


質問 # 130
An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a retraining job if any data drift is detected.
How should the ML engineer set up the pipeline to meet this requirement?

正解：A

解説：
AWS recommends Amazon SageMaker Model Monitor as the native service for detecting data drift, model drift, and bias drift in deployed ML models. Model Monitor continuously compares incoming inference data against a baseline dataset captured during training.
When Model Monitor detects drift beyond configured thresholds, it can emit Amazon CloudWatch events.
These events can trigger an AWS Lambda function, which is a common AWS-documented pattern for orchestrating automated workflows such as model retraining.
This Lambda function can then initiate a SageMaker Pipeline execution, starting a retraining job with updated data. This architecture aligns with AWS best practices for building automated, event-driven ML pipelines.
Option A is incorrect because AWS Glue is designed for data cataloging and ETL, not for ML-specific drift detection. Option B is unnecessary and overly complex for this use case. Option D is incorrect because Amazon QuickSight anomaly detection is intended for business intelligence analytics, not ML model monitoring.
AWS documentation explicitly positions SageMaker Model Monitor + Lambda automation as the recommended approach for continuous ML monitoring and retraining.
Therefore, Option C is the correct and AWS-verified answer.


質問 # 131
A government agency is conducting a national census to assess program needs by area and city. The census form collects approximately 500 responses from each citizen. The agency needs to analyze the data to extract meaningful insights. The agency wants to reduce the dimensions of the high-dimensional data to uncover hidden patterns.
Which solution will meet these requirements?

正解：D

解説：
The primary requirement is dimensionality reduction on high-dimensional structured data to uncover hidden patterns. Principal Component Analysis (PCA) is a linear dimensionality reduction technique specifically designed for this purpose and is available as a built-in algorithm in Amazon SageMaker.
PCA transforms the original features into a smaller set of orthogonal components that preserve the maximum possible variance. This makes PCA ideal for large tabular datasets such as census data, where hundreds of correlated variables are common.
t-SNE (Option B) is mainly used for visualization in very low dimensions (2D or 3D) and does not scale well for large datasets or production analysis. k-means (Option C) is a clustering algorithm, not a dimensionality reduction method. Random Cut Forest (Option D) is used for anomaly detection.
Therefore, PCA is the correct and AWS-recommended solution.


質問 # 132
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