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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 (Q47-Q52):

NEW QUESTION # 47
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account # in the same Region.
Which solution will meet this requirement with the LEAST development effort?

A. Use AWS DataSync to replicate the model from Account A to Account B.
B. Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B.
C. Use Amazon S3 to make a copy of the model. Transfer the copy to Account B.
D. Create an AWS Site-to-Site VPN connection between Account A and Account # to transfer the model.

Answer: B

Explanation:
Amazon Comprehend provides the ImportModel API operation, which allows you to copy a custom model between AWS accounts. By creating a resource-based IAM policy on the model in Account A, you can grant Account B the necessary permissions to access and import the model. This approach requires minimal development effort and is the AWS-recommended method for sharing custom models across accounts.

NEW QUESTION # 48
A company has an ML model that uses historical transaction data to predict customer behavior.
An ML engineer is optimizing the model in Amazon SageMaker to enhance the model's predictive accuracy. The ML engineer must examine the input data and the resulting predictions to identify trends that could skew the model's performance across different demographics.
Which solution will provide this level of analysis?

A. Create AWS Glue DataBrew recipes to correct the data based on statistics from the model output.
B. Create AWS Lambda functions to automate data pre-processing and to ensure consistent quality of input data for the model.
C. Use Amazon CloudWatch to monitor network metrics and CPU metrics for resource optimization during model training.
D. Use SageMaker Clarify to evaluate the model and training data for underlying patterns that might affect accuracy.

Answer: D

NEW QUESTION # 49
A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.
Which solution will meet these requirements?

A. Decrease the temperature parameter. Increase the top_k parameter.
B. Increase the temperature parameter and the top_k parameter.
C. Increase the temperature parameter. Decrease the top_k parameter.
D. Decrease the temperature parameter and the top_k parameter.

Answer: D

Explanation:
Thetemperatureparameter controls the randomness in the model's responses. Lowering the temperature makes the model produce more deterministic and consistent answers.
Thetop_kparameter limits the number of tokens considered for generating the next word. Reducing top_k further constrains the model's options, ensuring more predictable responses.
By decreasing both parameters, the responses become more focused and consistent, reducing variability in similar queries.

NEW QUESTION # 50
A company is developing an ML model to forecast future values based on time series data. The dataset includes historical measurements collected at regular intervals and categorical features. The model needs to predict future values based on past patterns and trends.
Which algorithm and hyperparameters should the company use to develop the model?

A. Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm with contamination to set the expected proportion of anomalies.
B. Use the Amazon SageMaker AI XGBoost algorithm. Set the scale_pos_weight hyperparameter to adjust for class imbalance.
C. Use k-means clustering with k to specify the number of clusters.
D. Use the Amazon SageMaker AI DeepAR algorithm with matching context length and prediction length hyperparameters.

Answer: D

Explanation:
The problem is a time series forecasting task with historical data and categorical features. Amazon SageMaker DeepAR is purpose-built for this use case. DeepAR uses recurrent neural networks to learn temporal patterns across multiple related time series and supports categorical covariates.
The context length hyperparameter controls how much historical data the model uses as input, while the prediction length specifies how far into the future the model forecasts. Correctly setting these hyperparameters is critical for capturing trends and seasonality.
XGBoost is a general-purpose tabular algorithm and does not model temporal dependencies natively. k-means is a clustering algorithm. Random Cut Forest is used for anomaly detection, not forecasting.
Therefore, DeepAR with appropriate context and prediction lengths is the correct and AWS-recommended solution.

NEW QUESTION # 51
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company needs to use the central model registry to manage different versions of models in the application.
Which action will meet this requirement with the LEAST operational overhead?

A. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
B. Use the SageMaker Model Registry and model groups to catalogthe models.
C. Use the SageMaker Model Registry and unique tags for each model version.
D. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.

Answer: B

Explanation:
Amazon SageMaker Model Registry is a feature designed to manage machine learning (ML) models throughout their lifecycle. It allows users to catalog, version, and deploy models systematically, ensuring efficient model governance and management.
Key Features of SageMaker Model Registry:
* Centralized Cataloging: Organizes models intoModel Groups, each containing multiple versions.
* Version Control: Maintains a history of model iterations, making it easier to track changes.
* Metadata Association: Attach metadata such as training metrics and performance evaluations to models.
* Approval Status Management: Allows setting statuses like PendingManualApproval or Approved to ensure only vetted models are deployed.
* Seamless Deployment: Direct integration with SageMaker deployment capabilities for real-time inference or batch processing.
Implementation Steps:
* Create a Model Group: Organize related models into groups to simplify management and versioning.
* Register Model Versions: Each model iteration is registered as a version within a specific Model Group.
* Set Approval Status: Assign approval statuses to models before deploying them to ensure quality control.
* Deploy the Model: Use SageMaker endpoints for deployment once the model is approved.
Benefits:
* Centralized Management: Provides a unified platform to manage models efficiently.
* Streamlined Deployment: Facilitates smooth transitions from development to production.
* Governance and Compliance: Supports metadata association and approval processes.
By leveraging the SageMaker Model Registry, the company can ensure organized management of models, version control, and efficient deployment workflows with minimal operational overhead.
References:
* AWS Documentation: SageMaker Model Registry
* AWS Blog: Model Registry Features and Usage

NEW QUESTION # 52
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