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Amazon MLA-C01 Exam Overview:

Certification Vendor:	Amazon Web Services (AWS)
Exam Name:	AWS Certified Machine Learning Engineer – Associate (MLA-C01)
Exam Number:	MLA-C01
Exam Format:	Multiple response, Matching, Multiple choice, Ordering
Certificate Validity Period:	3 years
Real Exam Qty:	65 scored questions + 15 unscored questions
Related Certifications:	AWS Certified Data Engineer – Associate AWS Certified DevOps Engineer – Professional AWS Certified AI Practitioner AWS Certified Solutions Architect – Associate
Exam Price:	USD 150
Available Languages:	Korean, Simplified Chinese, Japanese, English
Passing Score:	720/1000
Exam Duration:	130 minutes
Recommended Training:	Amazon SageMaker Documentation AWS Skill Builder - ML Engineer Associate Exam Prep
Exam Registration:	AWS Certification Registration
Sample Questions:	Amazon MLA-C01 Sample Questions
Exam Way:	Online proctored or test center exam
Pre Condition:	Recommended: ~1 year experience with Amazon SageMaker and AWS-based ML or data engineering roles
Official Syllabus URL:	https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

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Amazon MLA-C01 認定試験の出題範囲：

トピック	出題範囲
トピック 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.
トピック 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.
トピック 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.
トピック 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.

Amazon AWS Certified Machine Learning Engineer - Associate 認定 MLA-C01 試験問題 (Q153-Q158):

質問 # 153
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.
Which metric finding should the ML engineer prioritize the MOST when choosing the model?

A. High recall
B. High precision
C. Low recall
D. Low precision

正解：A

解説：
Recall measures the ability of a model to correctly identify all positive cases (true positives) out of all actual positives, minimizing false negatives. Since the cost of false negatives is much higher than falsepositives in this scenario, the ML engineer should prioritize models with high recall to reduce the likelihood of missing positive cases.

質問 # 154
A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field.
For this use case, which is the most effective course of action to address test data samples with missing features?

A. Use an algorithm that handles missing data better than decision trees.
B. Generate synthetic data to fill in the fields that are missing data, and then run through the test set.
C. Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
D. Drop the test samples with missing full review text fields, and then run through the test set.

正解：C

解説：
In this case, a full review summary usually contains the most descriptive phrases of the entire review and is a valid stand-in for the missing full review text field.

質問 # 155
A company is creating an application that will recommend products for customers to purchase.
The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.
Which solution will meet this requirement?

A. Configure the competitor's name as a blocked phrase in Amazon Q Business.
B. Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.
C. Configure an Amazon Q Business retriever to exclude the competitor's name.
D. Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.

正解：A

質問 # 156
A company runs training jobs on Amazon SageMaker by using a compute optimized instance.
Demand for training runs will remain constant for the next 55 weeks. The instance needs to run for 35 hours each week. The company needs to reduce its model training costs.
Which solution will meet these requirements?

A. Use the heterogeneous cluster feature of SageMaker Training. Configure the instance_type, instance_count, and instance_groups arguments to run training jobs.
B. Use SageMaker Edge Manager for the training. Specify the instance requirement in the edge device configuration. Run the training.
C. Opt in to a SageMaker Savings Plan with a 1-year term and an All Upfront payment. Run a SageMaker Training job on the instance.
D. Use a serverless endpoint with a provisioned concurrency of 35 hours for each week. Run the training on the endpoint.

正解：C

質問 # 157
An ML engineer is setting up a CI/CD pipeline for an ML workflow in Amazon SageMaker AI. The pipeline must automatically retrain, test, and deploy a model whenever new data is uploaded to an Amazon S3 bucket.
New data files are approximately 10 GB in size. The ML engineer also needs to track model versions for auditing.
Which solution will meet these requirements?

A. Manually retrain and deploy the model using SageMaker notebook instances and track versions with AWS CloudTrail.
B. Use AWS Lambda and Amazon EventBridge to retrain and deploy the model and track versions via logs.
C. Use SageMaker Pipelines with the SageMaker Model Registry to orchestrate model training and version tracking.
D. Use AWS CodePipeline, Amazon S3, and AWS CodeBuild to retrain and deploy the model automatically and track model versions.

正解：C

解説：
AWS documentation identifies SageMaker Pipelines as the native CI/CD service for ML workflows. Pipelines allow engineers to define automated steps for data processing, training, evaluation, and deployment, making them ideal for retraining models when new data arrives in Amazon S3.
For version tracking and auditing, SageMaker Model Registry is explicitly designed to manage model versions, metadata, approval status, and deployment history. This satisfies regulatory and audit requirements without custom tooling.
AWS Lambda is not suitable for handling large datasets (10 GB), and CodeBuild is not ML-aware and lacks built-in model governance. Manual notebook workflows do not meet CI/CD or automation requirements.
AWS best practices strongly recommend SageMaker Pipelines combined with the Model Registry for scalable, auditable, and production-grade ML CI/CD pipelines.
Therefore, Option B is the correct and AWS-verified solution.

質問 # 158
......

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