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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
  • 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
  • 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
  • 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 (Q10-Q15):

NEW QUESTION # 10
A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.
The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.
Which change to the architecture will meet these requirements?

Answer: D

Explanation:
Amazon Kinesis Data Firehose allows for near real-time data streaming. Setting thebuffering hintsto zero or a very small value minimizes the buffering delay and ensures that records are delivered to the destination (Amazon OpenSearch Service) as quickly as possible. Additionally, tuning thebatch sizein thePutRecordBatchoperation can further optimize the data ingestion for sub-second latency. This approach minimizes latency while maintaining the operational simplicity of using Firehose.


NEW QUESTION # 11
A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data.
Which technique for feature engineering should the ML engineer use for the model?

Answer: B

Explanation:
One-hot encodingis the appropriate technique for transforming categorical data, such as color information, into a format suitable for input to a neural network. This technique creates a binary vector representation where each unique category (color) is represented as a separate binary column, ensuring that the model does not infer ordinal relationships between categories. This approach preserves the categorical nature of the data and avoids introducing unintended biases.


NEW QUESTION # 12
A company wants to use large language models (LLMs) that are supported by Amazon Bedrock to develop a chat interface for the company ' s internal technical documentation. The company stores the documentation as dozens of text files that are several megabytes in total size. The company updates the text files often.
Which solution will meet these requirements MOST cost-effectively?

Answer: D

Explanation:
Option D is correct because Amazon Bedrock Knowledge Bases are designed for applications that need to answer questions using private documents without retraining or repeatedly fine-tuning a foundation model.
AWS documentation states that with Amazon Bedrock Knowledge Bases, you can build applications enriched by context retrieved from a knowledge base, and that this provides an out-of-the-box RAG solution. AWS also explicitly says that adding a knowledge base increases cost-effectiveness by removing the need to continually train your model to use your private data. That matches this use case very closely.
The question also says the documentation consists of text files that are only several megabytes total and are updated often. A retrieval-based approach is more economical and operationally simpler than creating a new model or repeatedly fine-tuning one whenever the documents change. AWS documentation for Bedrock knowledge bases describes adding data sources and running ingestion jobs to process and index the content, which is exactly the pattern needed for frequently updated internal documentation used by a chat interface.
The other options are not as cost-effective. Creating a new LLM is far beyond the need here. Guardrails help control model behavior and policy enforcement, but they do not serve as a document retrieval layer for internal documentation. Fine-tuning a model on frequently changing text files is usually more expensive and less flexible than using retrieval augmentation. For a modest-sized, frequently updated documentation corpus, the AWS-native and most cost-effective solution is to load the files into an Amazon Bedrock knowledge base and use it to provide context at inference time. Therefore, the best verified answer is D.


NEW QUESTION # 13
An ML engineer is training an XGBoost regression model in Amazon SageMaker AI. The ML engineer conducts several rounds of hyperparameter tuning with random grid search. After these rounds of tuning, the error rate on the test hold-out dataset is much larger than the error rate on the training dataset.
The ML engineer needs to make changes before running the hyperparameter grid search again.
Which changes will improve the model's performance? (Select TWO.)

Answer: A,C

Explanation:
The scenario describes a classic overfitting problem: the XGBoost model performs well on the training dataset but poorly on the test hold-out dataset. According to AWS Machine Learning and XGBoost documentation, overfitting occurs when a model is too complex and learns noise and patterns specific to the training data rather than generalizable relationships.
One effective way to address overfitting is to reduce model complexity. Option B, reducing the number of features, simplifies the hypothesis space and lowers the risk of fitting spurious correlations. Feature reduction is a recommended best practice when the model shows a large generalization gap between training and test error.
Another effective method is to increase regularization. Option D, increasing the L2 regularization parameter (lambda in XGBoost), penalizes large weights and discourages overly complex trees. AWS documentation explicitly notes that L2 regularization helps improve generalization by smoothing model parameters and reducing variance.
Option A would worsen overfitting by increasing complexity. Option C is incorrect because reducing the number of training samples generally increases overfitting risk. Option E would decrease regularization strength and further degrade test performance.
Therefore, reducing feature complexity and increasing L2 regularization are the correct changes.


NEW QUESTION # 14
A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

Answer: A

Explanation:
Amazon Comprehend is a fully managed natural language processing (NLP) service that includes a built-in sentiment analysis feature. It can quickly and efficiently analyze text data to determine whether the sentiment is positive, negative, neutral, or mixed. Using Amazon Comprehend requires minimal setup and provides accurate results without the need to train and deploy custom models, making it the fastest and most efficient solution for this task.


NEW QUESTION # 15
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