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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
Topic 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.
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	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	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 (Q191-Q196):

NEW QUESTION # 191
A company wants to use large language models (LLMs) supported by Amazon Bedrock to develop a chat interface for internal technical documentation.
The documentation consists of dozens of text files totaling several megabytes and is updated frequently.
Which solution will meet these requirements MOST cost-effectively?

A. Fine-tune an LLM in Amazon Bedrock with the documentation.
B. Use Amazon Bedrock guardrails to integrate documentation.
C. Train a new LLM in Amazon Bedrock using the documentation.
D. Upload the documentation to an Amazon Bedrock knowledge base and use it as context during inference.

Answer: D

Explanation:
AWS recommends Retrieval Augmented Generation (RAG) using Amazon Bedrock knowledge bases as the most cost-effective solution for incorporating frequently updated documents into LLM-powered applications.
A Bedrock knowledge base allows the company to store documents in Amazon S3, index them using vector embeddings, and retrieve relevant context dynamically at inference time. This approach eliminates the need for retraining or fine-tuning the model when documents change.
Training or fine-tuning an LLM is expensive, time-consuming, and unnecessary for frequently changing data.
Bedrock guardrails are designed for safety and policy enforcement, not knowledge integration.
AWS documentation explicitly states that knowledge bases are the preferred method for dynamic, updatable enterprise content in chat applications.
Therefore, Option D is the correct and most cost-effective solution.

NEW QUESTION # 192
A company uses Amazon SageMaker for its ML process. A compliance audit discovers that an Amazon S3 bucket for training data uses server-side encryption with S3 managed keys (SSE- S3).
The company requires customer managed keys. An ML engineer changes the S3 bucket to use server-side encryption with AWS KMS keys (SSE-KMS). The ML engineer makes no other configuration changes.
After the change to the encryption settings, SageMaker training jobs start to fail with AccessDenied errors.
What should the ML engineer do to resolve this problem?

A. Update the IAM policy that is attached to the execution role for the training jobs. Include the kms:Encrypt and kms:Decrypt permissions.
B. Update the IAM policy that is attached to the execution role for the training jobs. Include the s3:ListBucket and s3:GetObject permissions.
C. Update the IAM policy that is attached to the user that created the training jobs. Include the kms:CreateGrant permission.
D. Update the S3 bucket policy that is attached to the S3 bucket. Set the value of the aws:SecureTransport condition key to True.

Answer: A

NEW QUESTION # 193
A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories.
Which solution will meet these requirements?

A. Configure ECR cross-account replication for each existing ECR repository. Ensure that each model is visible in each AWS account.
B. Use the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon ECR. Create a new AWS account. In the new account, use the SageMaker Model Registry as the central catalog. Attach a cross-account resource policy to each model group in the initial AWS accounts.
C. Use an AWS Glue Data Catalog to store the models. Run an AWS Glue crawler to migrate the models from the ECR repositories to the Data Catalog. Configure cross-account access to the Data Catalog.
D. Create a new AWS account with a new ECR repository as the central catalog. Configure ECR cross-account replication between the initial ECR repositories and the central catalog.

Answer: B

NEW QUESTION # 194
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?

A. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
B. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
C. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
D. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.

Answer: C

NEW QUESTION # 195
A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain.
Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address.
Which update to the network configuration will meet this requirement?

A. Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
B. Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.
C. Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.
D. Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.

Answer: C

Explanation:
Network ACLs (Access Control Lists) operate at the subnet level and allow for rules to explicitly deny traffic from specific IP addresses. By creating an inbound rule in the network ACL to deny traffic from the suspicious IP address, the company can block traffic to the Amazon SageMaker domain from that IP. This approach works because network ACLs are evaluated before traffic reaches the security groups, making them effective for blocking traffic at the subnet level.

NEW QUESTION # 196
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