MLA-C01関連資格知識、MLA-C01関連復習問題集

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Amazon MLA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: ML Model Development	26%	- Train, tune, and refine models 1. Hyperparameter optimization: Amazon SageMaker Automatic Model Tuning 2. Training options: built-in algorithms, custom containers, frameworks 3. Distributed training and managed services - 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 - Select appropriate modeling approach 1. Use cases and service recommendations 2. Algorithm selection: traditional ML, deep learning, pre-built models 3. Problem type: classification, regression, clustering, forecasting, NLP, computer vision
Topic 2: Data Preparation for Machine Learning	28%	- 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. Data cleaning, normalization, and encoding 2. Tools: Amazon SageMaker Processing, AWS Glue DataBrew, Pandas, PySpark 3. Feature selection, transformation, and scaling - 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
Topic 3: ML Solution Monitoring, Maintenance, and Security	24%	- 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. Data encryption: at rest and in transit 2. Access control: IAM roles, policies, permissions 3. Compliance, governance, and data privacy - Monitor model and data quality 1. Amazon SageMaker Model Monitor 2. Model drift detection: data drift, concept drift 3. Performance monitoring and alerting
Topic 4: Deployment and Orchestration of ML Workflows	22%	- Automate and orchestrate ML pipelines 1. Workflow automation and event-driven processing 2. CI/CD integration: AWS CodePipeline, AWS CodeBuild 3. ML pipelines: Amazon SageMaker Pipelines - 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. Model packaging and versioning: Amazon SageMaker Model Registry 3. Real-time inference, batch transform, serverless, edge deployment

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Amazon AWS Certified Machine Learning Engineer - Associate 認定 MLA-C01 試験問題 (Q117-Q122):

質問 # 117
A company needs to combine data from multiple sources. The company must use Amazon Redshift Serverless to query an AWS Glue Data Catalog database and underlying data that is stored in an Amazon S3 bucket.
Select and order the correct steps from the following list to meet these requirements. Select each step one time or not at all. (Select and order three.)
* Attach the IAM role to the Redshift cluster.
* Attach the IAM role to the Redshift namespace.
* Create an external database in Amazon Redshift to point to the Data Catalog schema.
* Create an external schema in Amazon Redshift to point to the Data Catalog database.
* Create an IAM role for Amazon Redshift to use to access only the S3 bucket that contains underlying data.
* Create an IAM role for Amazon Redshift to use to access the Data Catalog and the S3 bucket that contains underlying data.

正解：

解説：

Explanation:
Step 1
Create an IAM role for Amazon Redshift to use to access the Data Catalog and the S3 bucket that contains underlying data.
This role must include:
Permissions for AWS Glue Data Catalog (e.g., glue:GetDatabase, glue:GetTables) Permissions for the Amazon S3 bucket that stores the underlying data Step 2 Attach the IAM role to the Redshift namespace.
Redshift Serverless uses a namespace, not a cluster, so the role must be associated with the namespace to allow Redshift to assume it when querying external data.
Step 3
Create an external schema in Amazon Redshift to point to the Data Catalog database.
The external schema maps Redshift to the Glue Data Catalog database so Redshift can query the tables stored in S3.

質問 # 118
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company is experimenting with consecutive training jobs.
How can the company MINIMIZE infrastructure startup times for these jobs?

A. Use SageMaker Training Compiler.
B. Use Managed Spot Training.
C. Use the SageMaker distributed data parallelism (SMDDP) library.
D. Use SageMaker managed warm pools.

正解：D

解説：
When running consecutive training jobs in Amazon SageMaker, infrastructure provisioning can introduce latency, as each job typically requires the allocation and setup of compute resources. To minimize this startup time and enhance efficiency, Amazon SageMaker offers Managed Warm Pools.
Key Features of Managed Warm Pools:
* Reduced Latency: Reusing existing infrastructure significantly reduces startup time for training jobs.
* Configurable Retention Period: Allows retention of resources after training jobs complete, defined by the KeepAlivePeriodInSeconds parameter.
* Automatic Matching: Subsequent jobs with matching configurations (e.g., instance type) can reuse retained infrastructure.
Implementation Steps:
* Request Warm Pool Quota Increase: Increase the default resource quota for warm pools through AWS Service Quotas.
* Configure Training Jobs:
* Set KeepAlivePeriodInSeconds for the first training job to retain resources.
* Ensure subsequent jobs match the retained pool's configuration to enable reuse.
* Monitor Warm Pool Usage: Track warm pool status through the SageMaker console or API to confirm resource reuse.
Considerations:
* Billing: Resources in warm pools are billable during the retention period.
* Matching Requirements: Jobs must have consistent configurations to use warm pools effectively.
Alternative Options:
* Managed Spot Training: Reduces costs by using spare capacity but doesn't address startup latency.
* SageMaker Training Compiler: Optimizes training time but not infrastructure setup.
* SageMaker Distributed Data Parallelism Library: Enhances training efficiency but doesn't reduce setup time.
By using Managed Warm Pools, the company can significantly reduce startup latency for consecutive training jobs, ensuring faster experimentation cycles with minimal operational overhead.
AWS Documentation: Managed Warm Pools
AWS Blog: Reduce ML Model Training Job Startup Time

質問 # 119
A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%.
What should the ML engineer do to minimize bias due to missing values?

A. Replace each missing value by the mean or median across non-missing values in the same column.
B. Replace each missing value by the mean or median across non-missing values in same row.
C. For each feature, approximate the missing values using supervised learning based on other features.
D. Delete observations that contain missing values because these represent less than 5% of the data.

正解：C

解説：
Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C. Supervised learning applied to the imputation of missing values is an active field of research.

質問 # 120
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company needs to run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application.
Which action will meet this requirement?

A. Use SageMaker notebooks to compare the bias.
B. Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.
C. Use AWS Glue Data Quality to monitor bias.
D. Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.

正解：B

質問 # 121
A company needs to perform feature engineering, aggregation, and data preparation. After the features are produced, the company must implement a solution on AWS to process and store the features. Which solution will meet these requirements?

A. Use Amazon SageMaker Model Monitor to automatically ingest and transform the data. Create an Amazon S3 bucket to store the features in JSON format.
B. Use Amazon Managed Service for Apache Flink to transform the data and to ingest the data directly into Amazon SageMaker Feature Store. Use Feature Store to manage and store the features.
C. Use an Amazon SageMaker batch transform job to analyze, transform, and ingest the data.Create an Amazon DynamoDB table to store the features.
D. Use Amazon SageMaker Feature Processing to process and ingest the data. Use SageMaker Feature Store to manage and store the features.

正解：D

解説：
Amazon SageMaker Feature Processing (via processing jobs) is used to perform feature engineering and data preparation. The engineered features can then be ingested into SageMaker Feature Store, which is a purpose-built service to manage and store ML features for reuse across training and inference. This combination directly addresses the company's requirements.

質問 # 122
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