MLA-C01證照指南 - MLA-C01證照信息

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Amazon MLA-C01 考試大綱：

主題簡介
主題 1
  • ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.
主題 2
  • Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.
主題 3
  • Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI
  • CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.
主題 4
  • ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.

>> MLA-C01證照指南 <<

最新更新的MLA-C01證照指南及資格考試領導者和免費PDFAmazon AWS Certified Machine Learning Engineer - Associate

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最新的 AWS Certified Associate MLA-C01 免費考試真題 (Q15-Q20):

問題 #15
A company needs an AWS solution that will automatically create versions of ML models as the models are created.
Which solution will meet this requirement?

答案：A


問題 #16
A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.
A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.
Which solution will meet these requirements with the LEAST implementation effort?

答案：B

解題說明：
Dynamic data maskingallows you to control how sensitive data is presented to users at query time, without modifying or storing transformed versions of the source data. Amazon Redshift supports dynamic data masking, which can be implemented with minimal effort. This solution ensures that the data scientistcan access the required information while sensitive data remains protected, meeting the requirements efficiently and with the least implementation effort.


問題 #17
An ML engineer needs to deploy a trained model that is based on a genetic algorithm. The algorithm solves a complex problem and can take several minutes to generate predictions.
When the model is deployed, the model needs to access large amounts of data to process requests. The requests can involve as much as 100 MB of data.
Which deployment solution will meet these requirements with the LEAST operational overhead?

答案：A

解題說明：
SageMaker Asynchronous Inference is designed for models with long processing times and large payloads. It can handle input data up to 1 GB and avoids holding open connections during long inference runs, reducing operational overhead compared to managing EC2 or ECS infrastructure.
This makes it the best fit for the genetic algorithm model that takes minutes and processes large requests.


問題 #18
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.

答案：

解題說明：

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.


問題 #19
An ML engineer is using an Amazon SageMaker AI shadow test to evaluate a new model that is hosted on a SageMaker AI endpoint. The shadow test requires significant GPU resources for high performance. The production variant currently runs on a less powerful instance type.
The ML engineer needs to configure the shadow test to use a higher performance instance type for a shadow variant. The solution must not affect the instance type of the production variant.
Which solution will meet these requirements?

答案：D

解題說明：
Amazon SageMaker AI shadow testing enables ML engineers to evaluate new model versions by sending a copy of live production traffic to a shadow variant without affecting production inference responses. AWS documentation specifies that shadow variants are configured separately from production variants and can use different instance types, including higher-performance GPU instances.
The correct approach is to create a new endpoint configuration using the CreateEndpointConfig API. This configuration includes the existing production variant and a separate ShadowProductionVariants list. The shadow variant can be assigned a larger instance type to meet GPU performance requirements while leaving the production variant unchanged. After creating the configuration, the engineer deploys it using the CreateEndpoint action.
Option A is incorrect because production variant configurations cannot be directly modified to include shadow variants. Option B is incorrect because shadow variants are not defined as standard production variants; defining two production variants would route traffic differently and could affect production behavior. Option C introduces unnecessary complexity and deviates from SageMaker's built-in shadow testing functionality.
AWS explicitly documents that shadow variants are designed to isolate testing resources, support different instance types, and ensure zero impact on production inference. Therefore, Option D is the correct and AWS- recommended solution.


問題 #20
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MLA-C01證照信息: https://www.vcesoft.com/MLA-C01-pdf.html

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