全面覆蓋的新版MLA-C01考古題，優秀的學習資料幫助妳輕松通過MLA-C01考試

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Amazon MLA-C01 考試大綱：

主題	簡介
主題 1	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 2	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.
主題 3	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 4	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.

>> 新版MLA-C01考古題 <<

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最新的 AWS Certified Associate MLA-C01 免費考試真題 (Q28-Q33):

問題 #28
An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers. The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML.
Which solution will meet these requirements?

A. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon Q Developer to generate code snippets that will prepare the data.
B. Use Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a single data frame. Use the cleansing and enrichment functionalities to prepare the data.
C. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon SageMaker data labeling to prepare the data.
D. Use Amazon SageMaker Ground Truth to import the datasets and to consolidate them into a single data frame. Use the human-in-the-loop capability to prepare the data.

答案：B

解題說明：
Amazon SageMakerData Wranglerprovides a comprehensive solution for importing, consolidating, and preparing datasets for ML. It offers tools to handle missing values, duplicates, and outliers through its built- incleansingandenrichmentfunctionalities, allowing the ML engineer to efficiently prepare the data in a single environment with minimal manual effort.

問題 #29
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?

A. Use a custom Amazon SageMaker notebook to run a custom script to generate embeddings. Use SageMaker Feature Store to store the embeddings. Use SQL queries to perform the semantic searches.
B. Use an AWS Batch job to process the files and generate embeddings. Use AWS Glue to store the embeddings. Use SQL queries to perform the semantic searches.
C. Use an Amazon Textract asynchronous job to ingest the documents from the S3 bucket. Query Amazon Textract to perform the semantic searches.
D. Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra. Query Amazon Kendra to perform the semantic searches.

答案：D

解題說明：
Amazon Kendrais an AI-powered search service designed for semantic search use cases. It allows ingestion of documents from an Amazon S3 bucket using theAmazon Kendra S3 connector. Once the documents are ingested, Kendra enables semantic searches with its built-in capabilities, removing the need to manually generate embeddings or manage a vector database. This approach is efficient, requires minimal operational effort, and meets the requirements for a Retrieval Augmented Generation (RAG) application.

問題 #30
A company has historical data that shows whether customers needed long-term support from company staff.
The company needs to develop an ML model to predict whether new customers will require long-term support.
Which modeling approach should the company use to meet this requirement?

A. Linear regression
B. Logistic regression
C. Semantic segmentation
D. Anomaly detection

答案：B

解題說明：
Logistic regression is a suitable modeling approach for this requirement because it is designed for binary classification problems, such as predicting whether a customer will require long-term support ("yes" or "no").
It calculates the probability of a particular class and is widely used for tasks like this where the outcome is categorical.

問題 #31
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 must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.
Which solution will meet this requirement?

A. Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
B. Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
C. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."
D. Use SageMaker Experiments to facilitate the approval process during model registration.

答案：C

解題說明：
To implement a manual approval-based workflow ensuring that only approved models are deployed to production endpoints, Amazon SageMaker provides integrated tools such asSageMaker Pipelinesand the SageMaker Model Registry.
SageMaker Pipelinesis a robust service for building, automating, and managing end-to-end machine learning workflows. It facilitates the orchestration of various steps in the ML lifecycle, including data preprocessing, model training, evaluation, and deployment. By integrating with theSageMaker Model Registry, it enables seamless tracking and management of model versions and their approval statuses.
Implementation Steps:
* Define the Pipeline:
* Create a SageMaker Pipeline encompassing steps for data preprocessing, model training, evaluation, and registration of the model in the Model Registry.
* Incorporate aCondition Stepto assess model performance metrics. If the model meets predefined criteria, proceed to the next step; otherwise, halt the process.
* Register the Model:
* Utilize theRegisterModelstep to add the trained model to the Model Registry.
* Set the ModelApprovalStatus parameter to PendingManualApproval during registration. This status indicates that the model awaits manual review before deployment.
* Manual Approval Process:
* Notify the designated approver upon model registration. This can be achieved by integrating Amazon EventBridge to monitor registration events and trigger notifications via AWS Lambda functions.
* The approver reviews the model's performance and, if satisfactory, updates the model's status to Approved using the AWS SDK or through the SageMaker Studio interface.
* Deploy the Approved Model:
* Configure the pipeline to automatically deploy models with an Approved status to the production endpoint. This can be managed by adding deployment steps conditioned on the model's approval status.
Advantages of This Approach:
* Automated Workflow:SageMaker Pipelines streamline the ML workflow, reducing manual interventions and potential errors.
* Governance and Compliance:The manual approval step ensures that only thoroughly evaluated models are deployed, aligning with organizational standards.
* Scalability:The solution supports complex ML workflows, making it adaptable to various project requirements.
By implementing this solution, the company can establish a controlled and efficient process for deploying models, ensuring that only approved versions reach production environments.
References:
* Automate the machine learning model approval process with Amazon SageMaker Model Registry and Amazon SageMaker Pipelines
* Update the Approval Status of a Model - Amazon SageMaker

問題 #32
A company is planning to use Amazon SageMaker to make classification ratings that are based on images.
The company has 6 ## of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker.
An ML engineer must make the training data accessible for ML models that are in the SageMaker environment.
Which solution will meet these requirements?

A. Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
B. Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
C. Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.
D. Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.

答案：C

解題說明：
Amazon FSx for NetApp ONTAP allows mounting the file system as a network-attached storage (NAS) volume. Since the FSx for ONTAP file system and SageMaker instance are in the same VPC, you can directly mount the file system to the SageMaker instance. This approach ensures efficient access to the 6 TB of training data without the need to duplicate or transfer the data, meeting the requirements with minimal complexity and operational overhead.

問題 #33
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