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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 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.
Topic 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.
Topic 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.
Topic 4
  • 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q71-Q76):

NEW QUESTION # 71
A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.
A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.
Which solution will meet these requirements with the LEAST implementation effort?

Answer: C


NEW QUESTION # 72
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed- circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model's accuracy in the LEAST amount of time?

Answer: D

Explanation:
The model is underperforming in production due to variations in image quality from different cameras. Using the corrupt image transform with the impulse noise option in SageMaker Data Wrangler simulates real-world noise and variations in the training dataset. This approach helps the model become more robust to inconsistencies in image quality, improving its accuracy in production without the need to collect and process new data, thereby saving time.


NEW QUESTION # 73
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training.
After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?

Answer: B

Explanation:
To minimize communication overhead during distributed training:
1. Same VPC Subnet: Ensures low-latency communication between training instances by keeping the network traffic within a single subnet.
2. Same AWS Region and Availability Zone: Reduces network latency further because cross-AZ communication incurs additional latency and costs.
3. Data in the Same Region and AZ: Ensures that the training data is accessed with minimal latency, improving performance during training.
This configuration optimizes communication efficiency and minimizes overhead.


NEW QUESTION # 74
A music streaming company constantly streams song ratings from an application to an Amazon S3 bucket.
The company wants to use the ratings as an input for training and inference of an Amazon SageMaker AI model.
The company has an AWS Glue Data Catalog that is configured with the S3 bucket as the source. An ML engineer needs to implement a solution to create a repository for this data. The solution must ensure that the data stays synchronized during batch training and real-time inference.
Which solution will meet these requirements?

Answer: A

Explanation:
Option A is correct because Amazon SageMaker Feature Store is the AWS service designed to act as a centralized repository for ML features that are used consistently across training and inference . AWS documentation states that SageMaker Feature Store simplifies how you create, store, share, and manage features for data exploration, model training, and model inference. This directly matches the requirement to create a repository for streamed song ratings that will be used in both batch training and real-time inference.
The most important requirement in the question is that the data must stay synchronized between batch training and real-time inference . AWS documents explain that Feature Store provides both an offline store and an online store . The offline store is used for historical data, model training, and batch inference, while the online store is a low-latency, high-availability store intended for real-time lookup during inference. This dual-store design is exactly why Feature Store is used to maintain feature consistency across training and serving workflows. AWS Well-Architected guidance also explicitly says Feature Store provides online storage for real-time inference and offline storage for model training and batch inference.
The other options do not solve the full problem. Athena CTAS can organize query results but does not provide a synchronized feature repository for online and offline ML use. Lake Formation governs access to data lakes but is not a feature repository for training and inference consistency. Data Wrangler Generate Data Insights is for analysis and preparation, not synchronized feature serving. Therefore, the best AWS- documented answer is A .


NEW QUESTION # 75
A company is building a real-time data processing pipeline for an ecommerce application. The application generates a high volume of clickstream data that must be ingested, processed, and visualized in near real time. The company needs a solution that supports SQL for data processing and Jupyter notebooks for interactive analysis.
Which solution will meet these requirements?

Answer: C


NEW QUESTION # 76
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