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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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
  • 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q93-Q98):

NEW QUESTION # 93
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: C

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 # 94
A company uses an ML model to recommend videos to users. The model is deployed on Amazon SageMaker AI. The model performed well initially after deployment, but the model's performance has degraded over time.
Which solution can the company use to identify model drift in the future?

Answer: B

Explanation:
AWS recommends Amazon SageMaker Model Monitor for detecting data drift and model drift in deployed models. Model Monitor works by comparing live inference data against a baseline, which must first be created from the training dataset.
AWS documentation clearly specifies the required order:
* Create a baseline using training data statistics
* Create a monitoring schedule to compare incoming data against the baseline Option A reverses this order and is therefore incorrect. Option C is incorrect because SageMaker Clarify focuses on bias and explainability, not ongoing drift detection. Option D is reactive and does not provide continuous monitoring.
Model Monitor integrates with Amazon CloudWatch, enabling automated alerts and downstream retraining workflows. This proactive approach allows companies to detect degradation early and maintain model quality.
Therefore, Option B is the correct and AWS-verified answer.


NEW QUESTION # 95
A company is planning to use Amazon SageMaker to make classification ratings that are based on images.
The company has 6 ## of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker.
An ML engineer must make the training data accessible for ML models that are in the SageMaker environment.
Which solution will meet these requirements?

Answer: B

Explanation:
Amazon FSx for NetApp ONTAP allows mounting the file system as a network-attached storage (NAS) volume. Since the FSx for ONTAP file system and SageMaker instance are in the same VPC, you can directly mount the file system to the SageMaker instance. This approach ensures efficient access to the 6 TB of training data without the need to duplicate or transfer the data, meeting the requirements with minimal complexity and operational overhead.


NEW QUESTION # 96
A government agency is conducting a national census to assess program needs by area and city. The census form collects approximately 500 responses from each citizen. The agency needs to analyze the data to extract meaningful insights. The agency wants to reduce the dimensions of the high-dimensional data to uncover hidden patterns.
Which solution will meet these requirements?

Answer: B

Explanation:
The primary requirement is dimensionality reduction on high-dimensional structured data to uncover hidden patterns. Principal Component Analysis (PCA) is a linear dimensionality reduction technique specifically designed for this purpose and is available as a built-in algorithm in Amazon SageMaker.
PCA transforms the original features into a smaller set of orthogonal components that preserve the maximum possible variance. This makes PCA ideal for large tabular datasets such as census data, where hundreds of correlated variables are common.
t-SNE (Option B) is mainly used for visualization in very low dimensions (2D or 3D) and does not scale well for large datasets or production analysis. k-means (Option C) is a clustering algorithm, not a dimensionality reduction method. Random Cut Forest (Option D) is used for anomaly detection.
Therefore, PCA is the correct and AWS-recommended solution.


NEW QUESTION # 97
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


NEW QUESTION # 98
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