퍼펙트한MLA-C01최신덤프자료덤프공부문제

PassTIP는 유일하게 여러분이 원하는Amazon인증MLA-C01시험관련자료를 해결해드릴 수 잇는 사이트입니다. 여러분이 다른 사이트에서도 관련덤프자료를 보셨을경우 페이지 아래를 보면 자료출처는 당연히 PassTIP 일 것입니다. PassTIP의 자료만의 제일 전면적이고 또 최신 업데이트일 것입니다.

네트워크 전성기에 있는 지금 인터넷에서Amazon 인증MLA-C01시험자료를 많이 검색할수 있습니다. 하지만 왜PassTIP덤프자료만을 믿어야 할가요? PassTIP덤프자료는 실제시험문제의 모든 유형에 근거하여 예상문제를 묶어둔 문제은행입니다.시험적중율이 거의 100%에 달하여Amazon 인증MLA-C01시험을 한방에 통과하도록 도와드립니다.

>> MLA-C01최신 덤프자료 <<

MLA-C01적중율 높은 인증덤프, MLA-C01최신 인증시험정보

Amazon 인증 MLA-C01시험에 도전해보려고 결정하셨다면 PassTIP덤프공부가이드를추천해드립니다. PassTIP덤프는 고객님께서 필요한것이 무엇인지 너무나도 잘 알고 있답니다. PassTIP의 Amazon 인증 MLA-C01덤프는Amazon 인증 MLA-C01시험을 쉽게 만듭니다.

Amazon MLA-C01 시험요강:

주제	소개
주제 1	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.
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	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.

최신 AWS Certified Associate MLA-C01 무료샘플문제 (Q48-Q53):

질문 # 48
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.
The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.
Which algorithm should the ML engineer use to meet this requirement?

A. LightGBM
B. Linear learner
C. #-means clustering
D. Neural Topic Model (NTM)

정답：B

설명：
Why Linear Learner?
* SageMaker'sLinear Learneralgorithm is well-suited for binary classification problems such as fraud detection. It handles class imbalance effectively by incorporating built-in options forweight balancing across classes.
* Linear Learner can capture patterns in the data while being computationally efficient.
Key Features of Linear Learner:
* Automatically weights minority and majority classes.
* Supports both classification and regression tasks.
* Handles interdependencies among features effectively through gradient optimization.
Steps to Implement:
* Use the SageMaker Python SDK to set up a training job with the Linear Learner algorithm.
* Configure the hyperparameters to enable balanced class weights.
* Train the model with the balanced dataset created using SageMaker Data Wrangler.

질문 # 49
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company needs to use the central model registry to manage different versions of models in the application.
Which action will meet this requirement with the LEAST operational overhead?

A. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
B. Use the SageMaker Model Registry and model groups to catalogthe models.
C. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
D. Use the SageMaker Model Registry and unique tags for each model version.

정답：B

설명：
Amazon SageMaker Model Registry is a feature designed to manage machine learning (ML) models throughout their lifecycle. It allows users to catalog, version, and deploy models systematically, ensuring efficient model governance and management.
Key Features of SageMaker Model Registry:
* Centralized Cataloging: Organizes models intoModel Groups, each containing multiple versions.
* Version Control: Maintains a history of model iterations, making it easier to track changes.
* Metadata Association: Attach metadata such as training metrics and performance evaluations to models.
* Approval Status Management: Allows setting statuses like PendingManualApproval or Approved to ensure only vetted models are deployed.
* Seamless Deployment: Direct integration with SageMaker deployment capabilities for real-time inference or batch processing.
Implementation Steps:
* Create a Model Group: Organize related models into groups to simplify management and versioning.
* Register Model Versions: Each model iteration is registered as a version within a specific Model Group.
* Set Approval Status: Assign approval statuses to models before deploying them to ensure quality control.
* Deploy the Model: Use SageMaker endpoints for deployment once the model is approved.
Benefits:
* Centralized Management: Provides a unified platform to manage models efficiently.
* Streamlined Deployment: Facilitates smooth transitions from development to production.
* Governance and Compliance: Supports metadata association and approval processes.
By leveraging the SageMaker Model Registry, the company can ensure organized management of models, version control, and efficient deployment workflows with minimal operational overhead.
References:
* AWS Documentation: SageMaker Model Registry
* AWS Blog: Model Registry Features and Usage

질문 # 50
An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host.
Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

A. AWS::SageMaker::Pipeline
B. AWS::SageMaker::Model
C. AWS::SageMaker::NotebookInstance
D. AWS::SageMaker::Endpoint

정답：B

설명：
The AWS::SageMaker::Model resource in AWS CloudFormation is used to create an ML model in Amazon SageMaker. This model can then be hosted on an endpoint by using the AWS::SageMaker::Endpoint resource. The model resource defines the container or algorithm to use for hosting and the S3 location of the model artifacts.

질문 # 51
A company has historical data that shows whether customers needed long-term support from company staff.
The company needs to develop an ML model to predict whether new customers will require long-term support.
Which modeling approach should the company use to meet this requirement?

A. Linear regression
B. Anomaly detection
C. Logistic regression
D. Semantic segmentation

정답：C

설명：
Logistic regression is a suitable modeling approach for this requirement because it is designed for binary classification problems, such as predicting whether a customer will require long-term support ("yes" or "no").
It calculates the probability of a particular class and is widely used for tasks like this where the outcome is categorical.

질문 # 52
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions.
Which solution will meet this requirement?

A. Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.
B. Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.
C. Apply statistics from a well-known dataset to normalize the production samples.
D. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.

정답：D

설명：
To ensure consistency between training and inference, themin-max normalization statistics (min and max values)calculated during training must be retained and applied to normalize production inference data. Using the same statistics ensures that the model receives data in the same scale and distribution as it did during training, avoiding discrepancies that could degrade model performance. Calculating new statistics from production data would lead to inconsistent normalization and affect predictions.

질문 # 53
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PassTIP 는 완전히 여러분이 인증시험준비와 안전이 시험패스를 위한 완벽한 덤프제공사이트입니다.우리 PassTIP의 덤프들은 응시자에 따라 ,시험 ,시험방법에 따라 제품의 완성도도 다릅니다.그 말은 즉 알 맞춤 자료입니다.여러분은 PassTIP의 알맞춤 덤프들로 아주 간단하고 편안하게 패스할 수 있습니다.많은 Amazon인증관연 응시자들은 모두 우리PassTIP가 제공하는 MLA-C01문제와 답 덤프로 자격증 취득을 했습니다.때문에 우리PassTIP또한 업계에서 아주 좋은 이미지를 가지고 잇습니다

MLA-C01적중율 높은 인증덤프: https://www.passtip.net/MLA-C01-pass-exam.html