MLA-C01題庫更新資訊 - MLA-C01信息資訊

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Amazon MLA-C01 Exam Overview:

Certification Vendor:Amazon Web Services (AWS)
Exam Name:AWS Certified Machine Learning Engineer - Associate
Exam Number:MLA-C01
Exam Price:150 USD
Real Exam Qty:85
Available Languages:Simplified Chinese, Traditional Chinese, Korean, Japanese, English
Certificate Validity Period:3 years
Exam Duration:170 minutes
Related Certifications:AWS Certified Solutions Architect - Associate
AWS Certified Data Engineer - Associate
AWS Certified Developer - Associate
Passing Score:1000-2000 scaled score (passing score approximately 720)
Exam Format:Multiple choice, Multiple response
Sample Questions:Amazon MLA-C01 Sample Questions
Exam Way:Online proctored (Pearson VUE) or in-person testing center
Pre Condition:Recommended: 2+ years of ML engineering experience, familiarity with AWS ML services
Official Syllabus URL:https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

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Amazon MLA-C01 考試大綱：

主題簡介
主題 1
  • 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 2
  • 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 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.

最新的 AWS Certified Associate MLA-C01 免費考試真題 (Q69-Q74):

問題 #69
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.
The ML engineer needs a scalable solution that minimizes costs when the model is not in use.
The solution also must maintain the model's capacity to respond to requests during times of peak usage.
Which solution will meet these requirements?

答案：D


問題 #70
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?

答案：C

解題說明：
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.


問題 #71
A company has developed a new ML model. The company requires online model validation on
10% of the traffic before the company fully releases the model in production. The company uses an Amazon SageMaker endpoint behind an Application Load Balancer (ALB) to serve the model.
Which solution will set up the required online validation with the LEAST operational overhead?

答案：B


問題 #72
A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference.
Consumers are reporting delays in receiving the inference results.
An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs.
Which solution will meet these requirements?

答案：A


問題 #73
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：A


問題 #74
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