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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	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 3	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 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 (Q89-Q94):

NEW QUESTION # 89
A company has an existing Amazon SageMaker AI model (v1) on a production endpoint. The company develops a new model version (v2) and needs to test v2 in production before substituting v2 for v1.
The company needs to minimize the risk of v2 generating incorrect output in production and must prevent any disruption of production traffic during the change.
Which solution will meet these requirements?

A. Deploy v2 into a shadow variant that samples 100% of the inference requests. Collect all output in Amazon S3. If v2 performs as expected, promote v2 to production.
B. Create a second production variant for v2. Assign 1% of the traffic to v2 and 99% to v1. Collect all output of v2 in Amazon S3. If v2 performs as expected, switch all traffic to v2.
C. Create a second production variant for v2. Assign 10% of the traffic to v2 and 90% to v1. Collect all output of v2 in Amazon S3. If v2 performs as expected, switch all traffic to v2.
D. Deploy v2 to a new endpoint. Turn on data capture for the production endpoint. Send 100% of the input data to v2.

Answer: A

Explanation:
AWS recommends SageMaker shadow testing as the safest way to validate a new model version using real production traffic without impacting production responses. A shadow variant receives a copy of inference requests but does not return predictions to end users. This completely eliminates the risk of exposing incorrect predictions.
Options A and B are canary deployments. While useful, they still allow v2 to return responses to real users, which violates the requirement to prevent disruption. Option C sends 100% of traffic to v2 externally and is risky.
Shadow variants are explicitly designed to minimize risk while using real data, making them the AWS best practice for pre-production validation.
Therefore, Option D is correct.

NEW QUESTION # 90
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. Store users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.
B. Configure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.
C. Configure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.
D. Use Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.

Answer: D

NEW QUESTION # 91
A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow. The AWS Glue jobs can run on a schedule or can be launched manually.
The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development.
An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Callback steps in SageMaker Pipelines to start the AWS Glue workflow and to stop the pipelines until the AWS Glue jobs finish running.
B. Use Amazon EventBridge to invoke the pipelines and the AWS Glue jobs in the desired order.
C. Use AWS Step Functions for orchestration of the pipelines and the AWS Glue jobs.
D. Use processing steps in SageMaker Pipelines. Configure inputs that point to the Amazon Resource Names (ARNs) of the AWS Glue jobs.

Answer: A

Explanation:
Callback steps in Amazon SageMaker Pipelines allow you to integrate external processes, such as AWS Glue jobs, into the pipeline workflow. By using a Callback step, the SageMaker pipeline can trigger the AWS Glue workflow and pause execution until the Glue jobs complete. This approach provides seamless integration with minimal operational overhead, as it directly ties the pipeline's execution flow to the completion of the AWS Glue jobs without requiring additional orchestration tools or complex setups.

NEW QUESTION # 92
A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker endpoint. The ML engineer needs to explain to company stakeholders how the model makes predictions.
Which solution will provide an explanation for the model's predictions?

A. Use SageMaker Model Monitor on the deployed model.
B. Show the distribution of inferences from A/# testing in Amazon CloudWatch.
C. Add a shadow endpoint. Analyze prediction differences on samples.
D. Use SageMaker Clarify on the deployed model.

Answer: D

Explanation:
SageMaker Clarify is designed to provide explainability for ML models. It can analyze feature importance and explain how input features influence the model's predictions. By using Clarify with the deployed SageMaker model, the ML engineer can generate insights and present them to stakeholders to explain the sentiment analysis predictions effectively.

NEW QUESTION # 93
A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.
During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly.
What could be the reason for the reduced F1 score?

A. Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.
B. The original baseline data had a data quality issue of missing values.
C. Concept drift occurred in the underlying customer data that was used for predictions.
D. The model was not sufficiently complex to capture all the patterns in the original baseline data.

Answer: C

Explanation:
* Problem Description:
* The F1 score, which is a balance of precision and recall, has decreased significantly. This indicates the model's predictions are no longer aligned with the real-world data distribution.
* Why Concept Drift?
* Concept driftoccurs when the statistical properties of the target variable or features change over time. For example, customer behaviors or subscription cancellation patterns may have shifted, leading to reduced model accuracy.
* Signs of Concept Drift:
* Deviation in performance metrics (e.g., F1 score) over time.
* Declining prediction accuracy for certain groups or scenarios.
* Solution:
* Monitor for drift using tools like SageMaker Model Monitor.
* Regularly retrain the model with updated data to account for the drift.
* Why Not Other Options?:
* B: Model complexity is unrelated if the model initially performed well.
* C: Data quality issues would have been detected during baseline analysis.
* D: Incorrect ground truth labels would have resulted in a consistently poor baseline.
Conclusion: The decrease in F1 score is most likely due toconcept driftin the customer data, requiring retraining of the model with new data.

NEW QUESTION # 94
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