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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
  • 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
  • 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 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 (Q125-Q130):

NEW QUESTION # 125
A company launches a feature that predicts home prices. An ML engineer trained a regression model using the SageMaker AI XGBoost algorithm. The model performs well on training data but underperforms on real- world validation data.
Which solution will improve the validation score with the LEAST implementation effort?

Answer: B

Explanation:
This scenario indicates overfitting. AWS documentation for XGBoost recommends increasing the L2 regularization parameter (lambda) to reduce overfitting and improve generalization.
Increasing num_round worsens overfitting. Changing evaluation metrics does not change model behavior.
Collecting more data is effective but requires significant effort.
Regularization is a low-effort, high-impact fix.
Therefore, Option D is correct.


NEW QUESTION # 126
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training. After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?

Answer: A


NEW QUESTION # 127
A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.
A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.
Which solution will meet these requirements with the LEAST implementation effort?

Answer: D


NEW QUESTION # 128
An ML engineer uses one ML framework to train multiple ML models. The ML engineer needs to optimize inference costs and host the models on Amazon SageMaker AI.
Which solution will meet these requirements MOST cost-effectively?

Answer: D

Explanation:
Amazon SageMaker multi-model endpoints (MME) are designed to host multiple models behind a single endpoint, dynamically loading models into memory on demand. AWS documentation explicitly recommends MME as the most cost-effective solution when multiple models share the same ML framework and inference container.
With MME, SageMaker loads models from Amazon S3 only when they are invoked and unloads idle models automatically. This dramatically reduces the number of instances required and avoids paying for always-on resources for infrequently used models.
Multi-container endpoints are intended for inference pipelines or ensembles and require all containers to be loaded at startup, which increases cost. Deploying separate endpoints for each model results in the highest cost due to duplicated infrastructure.
AWS best practices clearly position multi-model endpoints as the optimal choice for reducing inference costs when hosting many models with similar runtime requirements.
Therefore, Option B is the correct and AWS-verified solution.


NEW QUESTION # 129
An ML engineer is deploying a generative AI model-based customer support agent that uses Amazon SageMaker AI for inference. The customer support agent must respond to customer questions about topics such as shipping policies, refund processes, and account management. The generative AI model generates one token at a time.
Customers report dissatisfaction with how long the customer support agent takes to generate lengthy responses to questions. The ML engineer must apply an inference optimization technique to improve the performance of the customer support agent.
Which solution will meet this requirement?

Answer: D

Explanation:
Option B is correct because AWS documentation states that speculative decoding is specifically a technique to speed up the decoding process of large language models. The question highlights that the model generates one token at a time and that users are unhappy with the time needed to generate lengthy responses. That is exactly the stage of inference targeted by speculative decoding. AWS explains that this method improves latency without compromising the quality of the generated text.
According to AWS, speculative decoding works by using a smaller, faster draft model to generate candidate tokens first. The larger target model then verifies those tokens. AWS further explains that the draft model can generate multiple candidate tokens quickly, and the target model evaluates them in parallel, which speeds up the final response. This directly addresses the problem in the scenario: the customer support agent is slow not because of startup time, but because long answers require many decoding steps.
The other options are not the best fit. Compilation reduces deployment time and auto-scaling latency through ahead-of-time compilation, but AWS documents it primarily as helping model deployment and hardware optimization rather than token-by-token response generation. Quantization reduces hardware requirements and can lower cost, but the docs do not present it as the most direct answer to slow sequential decoding. Fast model loading improves how quickly the model loads onto instances, which helps startup and scale-out time, not long response generation after the model is already serving traffic. Therefore, the best AWS-documented answer is B.


NEW QUESTION # 130
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