更新的MLA-C01認證題庫|高通過率的考試材料|全面覆蓋的MLA-C01：AWS Certified Machine Learning Engineer - Associate

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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
  • 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
  • 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.

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最新的 AWS Certified Associate MLA-C01 免費考試真題 (Q179-Q184):

問題 #179
Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.
Which solution will meet this requirement with the LEAST operational effort?

答案：D

解題說明：
Problem Description:
* The training dataset has a class imbalance, meaning one class (e.g., fraudulent transactions) has fewer samples compared to the majority class (e.g., non-fraudulent transactions). This imbalance affects the model's ability to learn patterns from the minority class.
Why SageMaker Data Wrangler?
* SageMaker Data Wrangler provides a built-in operation called "Balance Data," which includes oversampling and undersampling techniques to address class imbalances.
* Oversampling the minority class replicates samples of the minority class, ensuring the algorithm receives balanced inputs without significant additional operational overhead.
Steps to Implement:
* Import the dataset into SageMaker Data Wrangler.
* Apply the "Balance Data" operation and configure it to oversample the minority class.
* Export the balanced dataset for training.
Advantages:
* Ease of Use: Minimal configuration is required.
* Integrated Workflow: Works seamlessly with the SageMaker ecosystem for preprocessing and model training.
* Time Efficiency: Reduces manual effort compared to external tools or scripts.


問題 #180
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize production inference data in the same way before passing the data to the model.
Which solution will meet this requirement?

答案：C

解題說明：
AWS ML best practices state that data preprocessing applied during training must be applied identically during inference. For min-max normalization, this requires reusing the minimum and maximum values calculated from the training dataset.
If production data is normalized using different statistics, the feature distributions will differ from what the model learned, leading to degraded prediction accuracy. AWS documentation explicitly warns against recomputing normalization parameters on inference data.
Options A, C, and D introduce data leakage or inconsistent feature scaling. Option B ensures consistency between training and inference pipelines and preserves model integrity.
Therefore, Option B is the correct and AWS-aligned solution.


問題 #181
A company has a binary classification model in production. An ML engineer needs to develop a new version of the model.
The new model version must maximize correct predictions of positive labels and negative labels.
The ML engineer must use a metric to recalibrate the model to meet these requirements.
Which metric should the ML engineer use for the model recalibration?

答案：B


問題 #182
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 #183
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?

答案：B

解題說明：
The key requirement is semantic search over text documents that already reside in Amazon S3. AWS provides Amazon Kendra, a fully managed service specifically designed for semantic and natural language search across unstructured text.
Amazon Kendra natively supports S3 connectors, which can ingest documents directly from an S3 bucket, automatically process the text, generate embeddings, and index the content for semantic retrieval. This removes the need for the company to manage embedding generation, vector storage, or similarity search infrastructure. Queries can be expressed in natural language, making Kendra well suited for RAG-style applications.
Option A and B require building and maintaining a custom embedding pipeline and do not provide a true vector similarity search engine using SQL. SageMaker Feature Store is not intended to function as a vector database for semantic search.
Option D is incorrect because Amazon Textract is an OCR service for extracting text from scanned documents and images; it does not support semantic search.
Therefore, ingesting documents using the Amazon Kendra S3 connector and querying Kendra is the correct and AWS-recommended solution.


問題 #184
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