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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 (Q212-Q217):

NEW QUESTION # 212
A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.
Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

A. Spot Instances
B. On-Demand Instances
C. Dedicated Instances
D. Reserved Instances

Answer: A

NEW QUESTION # 213
An ML engineer is training an ML model to identify medical patients for disease screening. The tabular dataset for training contains 50,000 patient records: 1,000 with the disease and 49,000 without the disease.
The ML engineer splits the dataset into a training dataset, a validation dataset, and a test dataset.
What should the ML engineer do to transform the data and make the data suitable for training?

A. Apply k-means clustering to undersample the minority class in the test dataset.
B. Apply principal component analysis (PCA) to oversample the minority class in the training dataset.
C. Randomly oversample the majority class in the validation dataset.
D. Apply Synthetic Minority Oversampling Technique (SMOTE) to generate new synthetic samples of the minority class in the training dataset.

Answer: D

Explanation:
This dataset shows severe class imbalance, with only 2% of records representing patients with the disease.
AWS ML best practices recommend correcting imbalance only in the training dataset, while keeping validation and test sets representative of real-world distributions.
Synthetic Minority Oversampling Technique (SMOTE) generates synthetic samples of the minority class by interpolating between existing minority examples. This improves the model's ability to learn disease-related patterns without discarding data.
PCA is a dimensionality reduction method, not an oversampling technique. Oversampling the majority class worsens imbalance. Altering the test dataset would invalidate evaluation results.
Therefore, applying SMOTE to the training dataset is the correct approach.

NEW QUESTION # 214
A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models.
When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance.
The company needs to centralize management of the team ' s permissions.
Which solution will meet this requirement?

A. Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user.
Configure each notebook instance to use the IAM user.
B. Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.
C. Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.
D. Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role with the group.Associate the group with each notebook instance that the team uses.

Answer: B

Explanation:
Managing permissions for multiple Amazon SageMaker notebook instances can become complex when handled individually. To centralize and streamline permission management, AWS recommends creating a single IAM role with the necessary permissions and attaching this role to each notebook instance used by the data science team.
Steps to Implement the Solution:
Create a Single IAM Role with Necessary Permissions:
Define an IAM role that encompasses all permissions required by the data scientists for their tasks. This includes permissions for SageMaker operations and any other AWS services they interact with.
AWS provides managed policies like AmazonSageMakerFullAccess that can be attached to the role to grant comprehensive SageMaker permissions.(IAM Policies for SageMaker) Attach the IAM Role to Each Notebook Instance:
When creating or updating a SageMaker notebook instance, specify the IAM role created in the previous step.
This ensures that all notebook instances operate under a consistent set of permissions.
In the SageMaker console, during the notebook instance setup, you can choose an existing IAM role to associate with the instance.(Creating SageMaker Workspaces) Benefits of This Approach:
Centralized Permission Management:By using a single IAM role, you simplify the process of updating permissions. Changes to the role ' s policies automatically propagate to all associated notebook instances, ensuring consistent access control.
Adherence to Best Practices:AWS recommends using IAM roles to manage permissions for applications running on services like SageMaker. This approach avoids the need to manage individual user permissions separately.(IAM Best Practices for SageMaker) Alternative Options and Their Drawbacks:
Option B: Creating a single IAM group and adding data scientists to it does not directly associate the group with notebook instances. IAM groups are used to manage user permissions, not to assign roles to AWS resources like notebook instances.
Option C: Using a single IAM user with the AdministratorAccess policy is not recommended due to security risks associated with granting broad permissions and the challenges in managing shared user credentials.
Option D: Associating an IAM group with a role and then with notebook instances is not a valid approach, as IAM groups cannot be directly associated with AWS resources.
Conclusion: Option A is the most effective solution to centralize and manage permissions for SageMaker notebook instances, aligning with AWS best practices for IAM role management.
References:
AWS Documentation: IAM Policies for SageMaker
AWS Documentation: Creating SageMaker Workspaces
AWS Documentation: IAM Best Practices for SageMaker

NEW QUESTION # 215
An ML engineer needs to deploy a trained model based on a genetic algorithm. Predictions can take several minutes, and requests can include up to 100 MB of data.
Which deployment solution will meet these requirements with the LEAST operational overhead?

A. Deploy to a SageMaker AI Asynchronous Inference endpoint.
B. Deploy on EC2 Auto Scaling behind an ALB.
C. Deploy to a SageMaker AI real-time endpoint.
D. Deploy to Amazon ECS on EC2.

Answer: A

Explanation:
SageMaker Asynchronous Inference is designed for long-running inference workloads and large payloads (up to 1 GB). Requests are queued, processed asynchronously, and results are written to Amazon S3.
Real-time endpoints have payload and timeout limits. EC2 and ECS require infrastructure management, increasing operational overhead.
AWS documentation explicitly recommends asynchronous inference for workloads with large inputs and long execution times.
Therefore, Option C is the correct and most efficient solution.

NEW QUESTION # 216
A company is uploading thousands of PDF policy documents into Amazon S3 and Amazon Bedrock Knowledge Bases. Each document contains structured sections. Users often search for a small section but need the full section context. The company wants accurate section-level search with automatic context retrieval and minimal custom coding.
Which chunking strategy meets these requirements?

A. Semantic
B. Fixed-size
C. Hierarchical
D. Maximum tokens

Answer: C

Explanation:
AWS Bedrock Knowledge Bases support multiple chunking strategies to optimize retrieval quality.
Hierarchical chunking is specifically designed for structured documents such as PDFs with headings, sections, and subsections.
Hierarchical chunking allows fine-grained retrieval at the subsection level while automatically preserving parent section context. This ensures that when a small portion is retrieved, the surrounding section is also provided to the foundation model for better understanding.
Fixed-size and maximum-token chunking can split content arbitrarily, breaking semantic and structural boundaries. Semantic chunking focuses on meaning but does not guarantee structured context preservation without additional logic.
AWS documentation highlights hierarchical chunking as the preferred strategy when documents are structured and contextual integrity is required.
Therefore, Option A is the correct and AWS-aligned solution.

NEW QUESTION # 217
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