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Amazon MLA-C01 Exam Syllabus Topics:

Topic
Details

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

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 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q222-Q227):

NEW QUESTION # 222
A company wants to provide services to help other businesses label images. The company wants its labeling specialists to complete human labeling tasks on AWS. How should the company register the labeling specialists to receive tasks on AWS?

A. Use AWS Data Exchange.
B. Create and use Amazon Mechanical Turk entities in an Amazon SageMaker human loop.
C. Create and use an internal workforce in Amazon SageMaker Ground Truth.
D. Use the Amazon Mechanical Turk website.

Answer: C

Explanation:
To enable labeling specialists within the company to perform tasks, the correct solution is to create and use an internal workforce in Amazon SageMaker Ground Truth. This allows the company to securely register and manage its own labeling team to receive and complete human labeling tasks.

NEW QUESTION # 223
A company needs to develop an ML model. The model must identify an item in an image and must provide the location of the item.
Which Amazon SageMaker algorithm will meet these requirements?

A. K-nearest neighbors (k-NN)
B. XGBoost
C. Object detection
D. Image classification

Answer: C

NEW QUESTION # 224
A company is planning to create an internal-only chat interface to help employees handle customer queries. Currently, the employees need to refer to a massive knowledge base of internal documents to address customer issues. The new solution must be serverless. Which combination of steps will meet these requirements?

A. Use Amazon EC2 instances with Amazon API Gateway to invoke the model API.
B. Set up Amazon Bedrock with the Anthropic Claude foundation model.
C. Use an Amazon S3 bucket to store vector database dumps and embeddings.
D. Set up Amazon SageMaker JumpStart with the Llama foundation model.
E. Use Amazon RDS for MySQL to store vector database dumps and embeddings.
F. Use AWS Lambda functions with Amazon API Gateway to invoke the model API.

Answer: B,C,F

Explanation:
To build a serverless internal chat interface, you can use Amazon Bedrock with a foundation model like Claude, invoke the model API through AWS Lambda with Amazon API Gateway, and store embeddings in a vector database format using Amazon S3. This avoids server management, ensures scalability, and leverages serverless components end-to-end.

NEW QUESTION # 225
A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.
The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?

A. Create an Amazon EventBridge rule that has an event pattern that matches the S3 upload.
Configure the pipeline as the target of the rule.
B. Create an AWS Lambda function that scans the S3 bucket. Program the Lambda function to initiate the pipeline when new data is uploaded.
C. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded.
D. Create an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate training.

Answer: A

NEW QUESTION # 226
A company is using Amazon EMR. The company has a large dataset in Amazon S3 that needs to be ingested into Amazon SageMaker Feature Store. The dataset contains historical data and real-time streaming data.
The company must ensure that the Feature Store online store is updated with the most recent data as soon as the data becomes available. The company also must maintain a complete Feature Store offline store for batch processing.
Which solution will meet these requirements?

A. Use the PutRecord API in Feature Store Runtime to ingest all the data into the offline store.
B. Use the PutRecord API in Feature Store Runtime to ingest all the data into the online store.
C. Use the Feature Store Spark connector to ingest the data as Spark DataFrames with the online store and offline store enabled.
D. Use the Feature Store Spark connector to ingest the data as Spark DataFrames with only the online store enabled.

Answer: C

Explanation:
The SageMaker Feature Store Spark connector allows ingestion of large-scale data from Amazon EMR into Feature Store as Spark DataFrames. Enabling both the online store ensures real-time updates for the latest data, while the offline store maintains the full historical dataset for batch analytics. This setup meets both low-latency and historical processing requirements.

NEW QUESTION # 227
......

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