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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
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	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 3	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 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 (Q125-Q130):

NEW QUESTION # 125
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?

A. Use a custom Amazon SageMaker AI notebook to run a custom script to generate embeddings. Use SageMaker Feature Store to store the embeddings. Use SQL queries to perform the semantic searches.
B. Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra. Query Amazon Kendra to perform the semantic searches.
C. Use an AWS Batch job to process the files and generate embeddings. Use AWS Glue to store the embeddings. Use SQL queries to perform the semantic searches.
D. Use an Amazon Textract asynchronous job to ingest the documents from the S3 bucket. Query Amazon Textract to perform the semantic searches.

Answer: B

Explanation:
The key requirement is semantic search over text documents that already reside in Amazon S3. AWS provides Amazon Kendra, a fully managed service specifically designed for semantic and natural language search across unstructured text.
Amazon Kendra natively supports S3 connectors, which can ingest documents directly from an S3 bucket, automatically process the text, generate embeddings, and index the content for semantic retrieval. This removes the need for the company to manage embedding generation, vector storage, or similarity search infrastructure. Queries can be expressed in natural language, making Kendra well suited for RAG-style applications.
Option A and B require building and maintaining a custom embedding pipeline and do not provide a true vector similarity search engine using SQL. SageMaker Feature Store is not intended to function as a vector database for semantic search.
Option D is incorrect because Amazon Textract is an OCR service for extracting text from scanned documents and images; it does not support semantic search.
Therefore, ingesting documents using the Amazon Kendra S3 connector and querying Kendra is the correct and AWS-recommended solution.

NEW QUESTION # 126
An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.
The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Configure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.
B. Configure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.
C. Use Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.
D. Store users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.

Answer: C

Explanation:
AWS Lake Formation provides fine-grained access control and simplifies data governance for data lakes. By configuring Lake Formation tags to map ML engineers to their specific campaigns, you can restrict access to both structured and unstructured data in the data lake. This method is operationally efficient, as it centralizes access control management within Lake Formation and ensures consistency across Amazon Athena and S3 bucket access without requiring manual updates to policies or DynamoDB-based custom logic.

NEW QUESTION # 127
A company ingests sales transaction data using Amazon Data Firehose into Amazon OpenSearch Service. The Firehose buffer interval is set to 60 seconds.
The company needs sub-second latency for a real-time OpenSearch dashboard.
Which architectural change will meet this requirement?

A. Replace Firehose with AWS DataSync and enhanced fan-out consumers.
B. Increase the Firehose buffer interval to 120 seconds.
C. Replace Firehose with Amazon SQS.
D. Use zero buffering in the Firehose stream and tune the PutRecordBatch batch size.

Answer: D

Explanation:
Amazon Data Firehose supports near real-time delivery by configuring the buffer interval and buffer size.
AWS documentation states that setting the buffer interval to the minimum (as low as 1 second) enables low- latency ingestion.
By using zero or minimal buffering and tuning PutRecordBatch, data is delivered to OpenSearch almost immediately, enabling sub-second dashboard updates.
DataSync and SQS are not designed for real-time streaming analytics. Increasing the buffer interval worsens latency.
AWS explicitly recommends Firehose with minimal buffering for real-time OpenSearch ingestion.
Therefore, Option A is the correct and AWS-verified solution.

NEW QUESTION # 128
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.
B. Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.
C. Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.
D. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.

Answer: C

NEW QUESTION # 129
A company has developed a new ML model. The company requires online model validation on
10% of the traffic before the company fully releases the model in production. The company uses an Amazon SageMaker endpoint behind an Application Load Balancer (ALB) to serve the model.
Which solution will set up the required online validation with the LEAST operational overhead?

A. Use production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 0.1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
B. Configure the ALB to route 10% of the traffic to the new model at the existing SageMaker endpoint. Monitor the number of invocations by using AWS CloudTrail.
C. Create a new SageMaker endpoint. Use production variants to add the new model to the new endpoint. Monitor the number of invocations by using Amazon CloudWatch.
D. Use production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.

Answer: A

NEW QUESTION # 130
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