MLA-C01題庫資訊 & MLA-C01信息資訊

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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: ML Solution Monitoring, Maintenance, and Security24%- 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. Amazon SageMaker Model Monitor
  • 3. Model drift detection: data drift, concept drift
- Optimize and maintain workloads
  • 1. Logging, auditing, and troubleshooting
  • 2. Cost optimization and resource management
  • 3. Model retraining and update strategies
Topic 2: Data Preparation for Machine Learning28%- Ingest and store data
  • 1. Data formats: Parquet, JSON, CSV, ORC, Avro, RecordIO
  • 2. Data ingestion services: Amazon Kinesis, AWS Glue, Amazon S3, Amazon Athena
  • 3. Data storage options: object storage, data lakes, databases
- Ensure data integrity and prepare for modeling
  • 1. Data splitting: train/validation/test sets
  • 2. Data validation, quality checks, and profiling
  • 3. Feature store usage: Amazon SageMaker Feature Store
- Transform data and perform feature engineering
  • 1. Data cleaning, normalization, and encoding
  • 2. Feature selection, transformation, and scaling
  • 3. Tools: Amazon SageMaker Processing, AWS Glue DataBrew, Pandas, PySpark
Topic 3: Deployment and Orchestration of ML Workflows22%- 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
- Configure deployment for scalability and availability
  • 1. A/B testing and canary deployment
  • 2. Infrastructure as code: AWS CloudFormation, Terraform
  • 3. Auto-scaling, load balancing, and high availability
- 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
Topic 4: ML Model Development26%- 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. Hyperparameter optimization: Amazon SageMaker Automatic Model Tuning
  • 2. Training options: built-in algorithms, custom containers, frameworks
  • 3. Distributed training and managed services
- Select appropriate modeling approach
  • 1. Use cases and service recommendations
  • 2. Problem type: classification, regression, clustering, forecasting, NLP, computer vision
  • 3. Algorithm selection: traditional ML, deep learning, pre-built models

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最新的 AWS Certified Associate MLA-C01 免費考試真題 (Q51-Q56):

問題 #51
An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models. The ML engineer must receive alerts when changes in data quality occur.
Which solution will meet these requirements?

答案：B


問題 #52
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?

答案：B


問題 #53
A company stores time-series data about user clicks in an Amazon S3 bucket. The raw data consists of millions of rows of user activity every day. ML engineers access the data to develop their ML models.
The ML engineers need to generate daily reports and analyze click trends over the past 3 days by using Amazon Athena. The company must retain the data for 30 days before archiving the data.
Which solution will provide the HIGHEST performance for data retrieval?

答案：A

解題說明：
Partitioning the time-series data by date prefix in the S3 bucket significantly improves query performance in Amazon Athena by reducing the amount of data that needs to be scanned during queries. This allows the ML engineers to efficiently analyze trends over specific time periods, such as the past 3 days. Applying S3 Lifecycle policies to archive partitions older than 30 days to S3 Glacier FlexibleRetrieval ensures cost- effective data retention and storage management while maintaining high performance for recent data retrieval.


問題 #54
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
* Feature splitting
* Logarithmic transformation
* One-hot encoding
* Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)

答案：

解題說明：

Explanation:
* City (name):One-hot encoding
* Type_year (type of home and year the home was built):Feature splitting
* Size of the building (square feet or square meters):Standardized distribution
* City (name): One-hot encoding
* Why?The "City" is a categorical feature (non-numeric), so one-hot encoding is used to transform it into a numeric format. This encoding creates binary columns for eachunique category (e.g., cities like "New York" or "Los Angeles"), which the model can interpret.
* Type_year (type of home and year the home was built): Feature splitting
* Why?"Type_year" combines two pieces of information into one column, which could confuse the model. Feature splitting separates this column into two distinct features: "Type of home" and
"Year built," enabling the model to process each feature independently.
* Size of the building (square feet or square meters): Standardized distribution
* Why?Size is a continuous numerical variable, and standardization (scaling the feature to have a mean of 0 and a standard deviation of 1) ensures that the model treats it fairly compared to other features, avoiding bias from differences in feature scale.
By applying these feature engineering techniques, the ML engineer can ensure that the input data is correctly formatted and optimized for the model to make accurate predictions.


問題 #55
An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim consists of a few sentences in English, many of which contain complex related information. Management would like to use Amazon SageMaker built-in algorithms to design a machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not.
Which approach should be used to extract features from the claims to be used as inputs for the downstream supervised task?

答案：D

解題說明：
Amazon SageMaker Object2Vec generalizes the Word2Vec embedding technique for words to more complex objects, such as sentences and paragraphs. Since the supervised learning task is at the level of whole claims, for which there are labels, and no labels are available at the word level, Object2Vec needs be used instead of Word2Vec.


問題 #56
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