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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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 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
  • 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q127-Q132):

NEW QUESTION # 127
A company uses a batching solution to process data analytics each day. The company wants to build an analytics platform to provide near real-time updates. The company wants to use open source technology and does not want to manage or scale the infrastructure.
Which solution will meet these requirements?

Answer: D

Explanation:
The correct answer is A. Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless clusters to process the data.
Amazon MSK Serverless allows organizations to run Apache Kafka workloads without managing or provisioning the underlying infrastructure. Unlike MSK Provisioned clusters (Option B), serverless clusters automatically scale to accommodate variable workloads and handle operational tasks such as patching, scaling, and monitoring. This meets the company's requirement to avoid managing or scaling infrastructure while providing near real-time streaming analytics.
Option C, using Amazon Kinesis Data Streams with Application Auto Scaling, provides a managed streaming solution but is not based on open-source technology. While Kinesis can handle near real-time data, the question specifically emphasizes the company's desire for open-source technology, which points toward Kafka.
Option D, self-hosting Apache Flink on EC2, requires manual management of servers, scaling, patching, and container orchestration. This approach contradicts the requirement of not managing or scaling infrastructure and introduces operational complexity.
MSK Serverless provides seamless integration with open-source Kafka APIs, enabling applications to produce and consume streaming data in real time while AWS handles scaling and availability. It is ideal for analytics pipelines where data ingestion is continuous and variable, and infrastructure overhead must be minimized.
By choosing MSK Serverless, the company can implement near real-time updates for its analytics platform using open-source Kafka without the operational burden of cluster management, fully aligning with AWS best practices for deployment and orchestration of ML workflows in a managed, scalable, and serverless manner.


NEW QUESTION # 128
An ML engineer is analyzing potential biases in a customer dataset before training an ML model. The dataset contains customer age (numeric), product reviews (text), and purchase outcomes (categorical).
Which statistical metrics should the ML engineer use to identify potential biases in the dataset before model training?

Answer: B

Explanation:
Bias detection is a critical step in responsible machine learning and is emphasized in AWS documentation, particularly in Amazon SageMaker Clarify. When analyzing structured datasets that include sensitive or influential attributes such as age, AWS recommends evaluating label distribution fairness and group-based outcome differences before training a model.
The class imbalance metric helps identify whether certain outcomes (for example, purchase vs. no purchase) are overrepresented or underrepresented. Severe imbalance can cause models to favor majority classes, leading to biased predictions. AWS explicitly highlights class imbalance as a key issue to assess during data exploration.
The Difference in Proportions of Labels (DPL) is a fairness metric supported by SageMaker Clarify that measures whether outcome labels are disproportionately distributed across different groups, such as age ranges. DPL compares the proportion of favorable outcomes between groups, making it especially effective for identifying demographic bias in categorical labels.
Options A and D focus on descriptive statistics or correlations, which are useful for data understanding but do not directly measure bias or fairness. Option B partially addresses imbalance and sentiment but sentiment analysis of reviews alone does not quantify demographic bias tied to outcomes.
AWS documentation strongly recommends using group fairness metrics, including DPL, alongside class imbalance checks to identify bias before training. These metrics provide actionable insights into whether a dataset may lead to unfair or skewed model behavior.
Therefore, Option C is the most appropriate and AWS-aligned choice.


NEW QUESTION # 129
Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.
Which algorithm should the ML engineer use to meet this requirement?

Answer: B

Explanation:
Why Linear Learner?
* SageMaker'sLinear Learneralgorithm is well-suited for binary classification problems such as fraud detection. It handles class imbalance effectively by incorporating built-in options forweight balancing across classes.
* Linear Learner can capture patterns in the data while being computationally efficient.
Key Features of Linear Learner:
* Automatically weights minority and majority classes.
* Supports both classification and regression tasks.
* Handles interdependencies among features effectively through gradient optimization.
Steps to Implement:
* Use the SageMaker Python SDK to set up a training job with the Linear Learner algorithm.
* Configure the hyperparameters to enable balanced class weights.
* Train the model with the balanced dataset created using SageMaker Data Wrangler.


NEW QUESTION # 130
A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every 3 months.
Which solution will meet this requirement?

Answer: B

Explanation:
AWS Secrets Manager is designed for securely storing, managing, and automatically rotating secrets, including API tokens. By configuring a Lambda function for custom rotation logic, the solution can automatically rotate the API tokens every 3 months as required. Secrets Manager simplifies secret management and integrates seamlessly with other AWS services, making it the ideal choice for this use case.


NEW QUESTION # 131
A company uses a batching solution to process daily analytics. The company wants to provide near real-time updates, use open-source technology, and avoid managing or scaling infrastructure.
Which solution will meet these requirements?

Answer: A

Explanation:
Amazon MSK Serverless provides a fully managed Apache Kafka-compatible service that automatically handles provisioning, scaling, and capacity management. AWS documentation states that MSK Serverless is designed for customers who want Kafka functionality without managing infrastructure.
Option B requires capacity planning and scaling management. Option C uses proprietary technology rather than open source. Option D requires full infrastructure management.
MSK Serverless delivers near real-time streaming with minimal operational overhead while maintaining compatibility with open-source Kafka tooling.
Therefore, Option A is the correct solution.


NEW QUESTION # 132
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