MLA-C01최신덤프문제모음집 - MLA-C01시험대비최신덤프문제

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인재도 많고 경쟁도 치열한 이 사회에서 IT업계 인재들은 인기가 아주 많습니다.하지만 팽팽한 경쟁률도 무시할 수 없습니다.많은 IT인재들도 어려운 인증시험을 패스하여 자기만의 자리를 지켜야만 합니다.우리 Fast2test에서는 마침 전문적으로 이러한 IT인사들에게 편리하게 시험을 패스할수 있도록 유용한 자료들을 제공하고 있습니다. Amazon 인증MLA-C01인증은 아주 중요한 인증시험중의 하나입니다. Fast2test의Amazon 인증MLA-C01로 시험을 한방에 정복하세요.

Amazon MLA-C01 시험요강:

주제소개
주제 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.
주제 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.
주제 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.
주제 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.

>> MLA-C01최신 덤프문제모음집 <<

MLA-C01최신 덤프문제모음집 완벽한 시험공부자료

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최신 AWS Certified Associate MLA-C01 무료샘플문제 (Q59-Q64):

질문 # 59
A company uses ML models to predict whether transactions are fraudulent. The company needs to identify as many fraudulent transactions as possible. Which evaluation metric should the company use to evaluate the models to meet this requirement?

정답：B

설명：
Option D is correct because the company's primary goal is to identify as many fraudulent transactions as possible . In AWS documentation, recall is defined as TP / (TP + FN), where TP is true positives and FN is false negatives. Recall measures how well a model finds all actual positive cases. In a fraud-detection setting, the "positive" class is the fraudulent transaction, so maximizing recall means minimizing missed fraud cases.
AWS documentation also explains the business tradeoff clearly: a use case that needs to correctly predict as many positive examples as possible should prioritize high recall , even if that means accepting some additional false positives and therefore only moderate precision. That matches this question exactly, because the requirement is not to be most selective or most balanced overall; it is specifically to catch the largest possible number of fraudulent transactions.
The other metrics are less aligned to the stated goal. Precision focuses on how many predicted fraud cases are actually fraud, which is most important when false positives are very costly. F1 score balances precision and recall, but the question does not ask for balance; it asks for finding as many fraudulent transactions as possible. AUC is useful for overall ranking and threshold- independent model discrimination, but it is not the most direct metric for optimizing missed-fraud detection in this scenario. Based on AWS metric definitions, when the cost of missing true positives is the main concern, recall is the best evaluation metric. Therefore, the best verified answer is D .


질문 # 60
A company has an ML model that needs to run one time each night to predict stock values. The model input is
3 MB of data that is collected during the current day. The model produces the predictions for the next day.
The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?

정답：D

설명：
A serverless inference endpoint in Amazon SageMaker is ideal for use cases where the model is invoked infrequently, such as running one time each night. It eliminates the cost of idle resources when the model is not in use. Setting the MaxConcurrency parameter to 1 ensures cost-efficiency while supporting the required single nightly invocation. This solution minimizes costs and matches the requirement to process a small amount of data quickly.


질문 # 61
An ML engineer needs to train a supervised deep learning model. The available dataset is a large number of unlabeled images that only employees should access. The ML engineer needs to implement a solution that labels the dataset with the highest possible accuracy. Which combination of steps should the ML engineer take to meet these requirements? (Choose two.)

정답：D,E

설명：
To achieve the highest labeling accuracy with controlled employee-only access, the ML engineer should use Amazon SageMaker Ground Truth to define the annotation job and then assign it to a private workforce of employees for labeling and review. This ensures high-quality, secure labeling restricted to authorized personnel.


질문 # 62
A company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. The solution also must provide a notification when the number of API call events breaches a threshold.
Which solution will meet these requirements?

정답：A


질문 # 63
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine- grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?

정답：B

설명：
SageMaker Pipelines provides a directed acyclic graph (DAG) view for managing and visualizing ML workflows with fine-grained control. It integrates seamlessly with SageMaker Studio, offering an intuitive interface for workflow orchestration.
SageMaker ML Lineage Tracking keeps a running history of experiments and tracks the lineage of datasets, models, and training jobs. This feature supports model governance, auditing, and compliance verification requirements.


질문 # 64
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