MLA-C01日本語版と英語版、MLA-C01予想試験

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MLA-C01「AWS Certified Machine Learning Engineer - Associate」試験は簡単ではありません。専門的な知識が必要で、もしあなたはまだこの方面の知識を欠かれば、GoShikenは君に向ける知識を提供いたします。GoShikenの専門家チームは彼らの知識や経験を利用してあなたの知識を広めることを助けています。そしてあなたにMLA-C01試験に関するテスト問題と解答が分析して差し上げるうちにあなたのIT専門知識を固めています。

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	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.
トピック 2	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 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.
トピック 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.

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Amazon AWS Certified Machine Learning Engineer - Associate 認定 MLA-C01 試験問題 (Q125-Q130):

質問 # 125
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?

A. Create a separate SageMaker AI endpoint for the shadow variant that uses the larger instance type.
Create an AWS Lambda function that routes a portion of the traffic to the shadow endpoint. Assign the Lambda function to the original endpoint.
B. Use the CreateEndpointConfig action to define a new configuration. Specify the existing production variant in the configuration and add a separate ShadowProductionVariants list. Specify the larger instance type for the shadow variant. Use the CreateEndpoint action and pass the new configuration to the endpoint.
C. Modify the existing ProductionVariant configuration in the endpoint to include a ShadowProductionVariants list. Specify the larger instance type for the shadow variant.
D. Create a new endpoint configuration with two ProductionVariant definitions. Configure one definition for the existing production variant and one definition for the shadow variant with the larger instance type. Use the UpdateEndpoint action to apply the new configuration.

正解：B

解説：
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.

質問 # 126
An ML engineer is preparing a dataset that contains medical records to train an ML model to predict the likelihood of patients developing diseases.
The dataset contains columns for patient ID, age, medical conditions, test results, and a "Disease" target column.
How should the ML engineer configure the data to train the model?

A. Remove the "Disease" target column.
B. Remove the patient ID column.
C. Remove the medical conditions and test results columns.
D. Remove the age column.

正解：B

解説：
Patient ID is a unique identifier and does not contain predictive information. Including it can cause the model to overfit by memorizing records rather than learning meaningful patterns.
AWS ML best practices recommend removing identifiers that are not causally related to the target variable.
Age, medical conditions, and test results are clinically relevant features and should be retained. The target column must remain for supervised learning.
Therefore, Option A is the correct and AWS-aligned choice.

質問 # 127
A company runs an ML model on Amazon SageMaker. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rules that prohibit the collection of aggregated metadata from training jobs. Which solution will prevent SageMaker from collecting metadata from the training jobs?

A. Reconfigure the training jobs to use only AWS Nitro instances.
B. Encrypt the training data with an AWS Key Management Service (AWS KMS) customer managed key.
C. Ensure that training jobs are running in a private subnet in a custom VPC.
D. Opt out of metadata tracking for any training job that is submitted.

正解：D

解説：
Amazon SageMaker automatically collects training job metadata, but you can opt out of metadata tracking when submitting a training job. This disables collection of aggregated metadata, ensuring compliance with rules that prohibit metadata collection.

質問 # 128
A company runs Amazon SageMaker ML models that use accelerated instances. The models require real-time responses. Each model has different scaling requirements. The company must not allow a cold start for the models.
Which solution will meet these requirements?

A. Create a SageMaker Serverless Inference endpoint for each model. Use provisioned concurrency for the endpoints.
B. Create a SageMaker endpoint. Create an inference component for each model. In the inference component settings, specify the newly created endpoint. Create an auto scaling policy for each inference component. Set the parameter for the minimum number of copies to at least 1.
C. Create a SageMaker Asynchronous Inference endpoint for each model. Create an auto scaling policy for each endpoint.
D. Create an Amazon S3 bucket. Store all the model artifacts in the S3 bucket. Create a SageMaker multi-model endpoint. Point the endpoint to the S3 bucket. Create an auto scaling policy for the endpoint. Set the parameter for the minimum number of copies to at least 1.

正解：B

質問 # 129
A data scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result.
The models should be evaluated based on the following criteria:
1) Must have a recall rate of at least 80%
2) Must have a false positive rate of 10% or less
3) Must minimize business costs
After creating each binary classification model, the data scientist generates the corresponding confusion matrix.
Which confusion matrix represents the model that satisfies the requirements?

A. TN = 99, FP = 1
FN = 21, TP = 79
B. TN = 98, FP = 2
FN = 18, TP = 82
C. TN = 96, FP = 4
FN = 10, TP = 90
D. TN = 91, FP = 9
FN = 22, TP = 78

正解：B

解説：
The following calculations are required:
TP = True Positive
FP = False Positive
FN = False Negative
TN = True Negative
FN = False Negative
Recall = TP / (TP + FN)
False Positive Rate (FPR) = FP / (FP + TN)
Cost = 5 * FP + FN

質問 # 130
......

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