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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 (Q214-Q219):

NEW QUESTION # 214
An ML engineer is training a simple neural network model. The model's performance improves initially and then degrades after a certain number of epochs.
Which solutions will mitigate this problem? (Select TWO.)

Answer: C,D

Explanation:
The described behavior indicates overfitting, where the model starts to memorize training data instead of generalizing.
Early stopping halts training when validation performance stops improving, preventing the model from overfitting further. AWS documentation recommends early stopping as a primary regularization technique.
Dropout randomly disables neurons during training, forcing the model to learn robust representations and reducing reliance on specific neurons. Increasing dropout is a well-established method for improving generalization.
Increasing layers or neurons increases model capacity and worsens overfitting. Model bias is unrelated to epoch-based degradation.
Therefore, Options A and B are correct.


NEW QUESTION # 215
A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.
Which solution will meet this requirement?

Answer: D

Explanation:
Amazon Q Business provides built-in guardrails to control and constrain model responses. One such control is the ability to define blocked phrases, which explicitly prevents specified terms from appearing in generated responses.
Configuring the competitor's name as a blocked phrase guarantees that Amazon Q Business will never include that name in responses, fully meeting the requirement. This approach is deterministic and does not rely on ranking or retrieval behavior.
Option B is incorrect because retrievers control what documents are fetched, not how responses are generated.
Option C adds unnecessary complexity and does not guarantee exclusion in generated answers. Option D only deprioritizes content and does not prevent it from appearing.
Therefore, blocking the competitor's name is the correct solution.


NEW QUESTION # 216
A company is developing an ML model to predict customer satisfaction. The company needs to use survey feedback and the past satisfaction level of customers to predict the future satisfaction level of customers.
The dataset includes a column named Feedback that contains long text responses. The dataset also includes a column named Satisfaction Level that contains three distinct values for past customer satisfaction: High, Medium, and Low. The company must apply encoding methods to transform the data in each column.
Which solution will meet these requirements?

Answer: A


NEW QUESTION # 217
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
* Feature splitting
* Logarithmic transformation
* One-hot encoding
* Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)

Answer:

Explanation:

Explanation:
City (name): One-hot encoding
Type_year (type of home and year the home was built): Feature splitting Size of the building (square feet or square meters): Standardized distribution City (name): One-hot encoding Why? The " City " is a categorical feature (non-numeric), so one-hot encoding is used to transform it into a numeric format. This encoding creates binary columns for each unique category (e.g., cities like " New York " or " Los Angeles " ), which the model can interpret.
Type_year (type of home and year the home was built): Feature splitting Why? " Type_year " combines two pieces of information into one column, which could confuse the model.
Feature splitting separates this column into two distinct features: " Type of home " and " Year built, " enabling the model to process each feature independently.
Size of the building (square feet or square meters): Standardized distribution Why? Size is a continuous numerical variable, and standardization (scaling the feature to have a mean of 0 and a standard deviation of 1) ensures that the model treats it fairly compared to other features, avoiding bias from differences in feature scale.
By applying these feature engineering techniques, the ML engineer can ensure that the input data is correctly formatted and optimized for the model to make accurate predictions.


NEW QUESTION # 218
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?

Answer: D

Explanation:
The key requirement is semantic search over text documents that already reside in Amazon S3. AWS provides Amazon Kendra, a fully managed service specifically designed for semantic and natural language search across unstructured text.
Amazon Kendra natively supports S3 connectors, which can ingest documents directly from an S3 bucket, automatically process the text, generate embeddings, and index the content for semantic retrieval. This removes the need for the company to manage embedding generation, vector storage, or similarity search infrastructure. Queries can be expressed in natural language, making Kendra well suited for RAG-style applications.
Option A and B require building and maintaining a custom embedding pipeline and do not provide a true vector similarity search engine using SQL. SageMaker Feature Store is not intended to function as a vector database for semantic search.
Option D is incorrect because Amazon Textract is an OCR service for extracting text from scanned documents and images; it does not support semantic search.
Therefore, ingesting documents using the Amazon Kendra S3 connector and querying Kendra is the correct and AWS-recommended solution.


NEW QUESTION # 219
......

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