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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 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.
Topic 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.
Topic 3
  • 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.
Topic 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q128-Q133):

NEW QUESTION # 128
A company stores time-series data about user clicks in an Amazon S3 bucket. The raw data consists of millions of rows of user activity every day. ML engineers access the data to develop their ML models.
The ML engineers need to generate daily reports and analyze click trends over the past 3 days by using Amazon Athena. The company must retain the data for 30 days before archiving the data.
Which solution will provide the HIGHEST performance for data retrieval?

Answer: A


NEW QUESTION # 129
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?

Answer: B

Explanation:
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 into Model 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.
AWS Documentation: SageMaker Model Registry
AWS Blog: Model Registry Features and Usage


NEW QUESTION # 130
A company wants to build an anomaly detection ML model. The model will use large-scale tabular data that is stored in an Amazon S3 bucket. The company does not have expertise in Python, Spark, or other languages for ML.
An ML engineer needs to transform and prepare the data for ML model training.
Which solution will meet these requirements?

Answer: B

Explanation:
The company lacks programming expertise, so a no-code/low-code solution is required. Amazon SageMaker Canvas includes Data Wrangler's visual interface, which enables users to import, transform, and prepare data using a graphical workflow.
Data Wrangler in Canvas supports common preprocessing tasks-such as handling missing values, normalization, outlier detection, and feature engineering-without writing code. It integrates directly with Amazon S3 and is suitable for large tabular datasets.
Options A, C, and D require coding skills (Spark, SQL, or Python), which violates the stated constraint.
Therefore, using the visual interface of SageMaker Data Wrangler in Canvas is the correct solution.


NEW QUESTION # 131
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?

Answer: D


NEW QUESTION # 132
Hotspot Question
An ML engineer must choose the appropriate Amazon SageMaker algorithm to solve specific AI problems.
Select the correct SageMaker built-in algorithm from the following list for each use case. Each algorithm should be selected one time.
- Random Cut Forest (RCF) algorithm
- Semantic segmentation algorithm
- Sequence-to-Sequence (seq2seq) algorithm

Answer:

Explanation:


NEW QUESTION # 133
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