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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AWS AI Services Overview | 26% | - Identify appropriate AWS AI services for given scenarios
|
| Topic 2: AI/ML Fundamentals | 24% | - Explain the fundamental concepts of AI and ML
|
| Topic 3: Responsible AI | 20% | - Implement responsible AI best practices
|
| Topic 4: AI Application Development | 30% | - Implement AI applications using AWS services
|
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NEW QUESTION # 174
A company is building an application that needs to generate synthetic data that is based on existing data.
Which type of model can the company use to meet this requirement?
Answer: D
NEW QUESTION # 175
An online media streaming company wants to give its customers the ability to perform natural language-based image search and filtering. The company needs a vector database that can help with similarity searches and nearest neighbor queries.
Which AWS service meets these requirements?
Answer: A
Explanation:
The correct answer is D because Amazon OpenSearch Service supports k-Nearest Neighbor (k-NN) search and vector similarity search, which are required for semantic search tasks, such as matching natural language queries to image embeddings.
From AWS documentation:
"Amazon OpenSearch Service supports k-NN search, which allows you to run efficient similarity searches on large-scale datasets using vector embeddings generated by models. This enables applications like natural language-based image search and personalized recommendations." In this use case, image data can be encoded into vectors using foundation models (e.g., via Amazon Bedrock or SageMaker), and OpenSearch Service can index and retrieve results based on vector similarity.
Explanation of other options:
A). Amazon Comprehend is for text-based NLP tasks and does not provide vector similarity or search functionality.
B). Amazon Personalize is for user-item recommendations and personalization, not vector-based semantic search.
C). Amazon Polly is a text-to-speech service and not related to image search or vector databases.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon OpenSearch Service Documentation - k-NN and Vector Search
* AWS ML Specialty Study Guide - Semantic Search and Vector Indexing
* AWS Generative AI Best Practices - Embeddings and Vector Databases
NEW QUESTION # 176
Which option is an example of unsupervised learning?
Answer: A
Explanation:
Comprehensive and Detailed
Unsupervised learning involves discovering hidden patterns without labeled data. Example: clustering.
Image recognition (B) is supervised learning.
House price prediction (C) is regression (supervised).
NEW QUESTION # 177
A research group wants to test different generative AI models to create research papers. The research group has defined a prompt and needs a method to assess the models' output. The research group wants to use a team of scientists to perform the output assessments.
Which solution will meet these requirements?
Answer: B
Explanation:
The correct answer is C because Amazon Bedrock's model evaluation feature allows users to compare outputs from different foundation models using human evaluation or automatic metrics. It enables the creation of structured evaluations where human reviewers (in this case, scientists) can assess model responses based on custom criteria like relevance, coherence, or accuracy.
From AWS documentation:
"Amazon Bedrock provides model evaluation capabilities that support both automatic and human evaluation. You can define custom evaluation prompts and collect assessments from reviewers to compare foundation model outputs for tasks such as summarization, text generation, and more." This solution is ideal for research workflows requiring domain experts to provide feedback on LLM-generated content.
Explanation of other options:
A . Amazon Personalize is used for building recommendation systems, not for evaluating model output.
B . Amazon Rekognition is used for analyzing images and videos (e.g., moderation, facial recognition), not textual output.
D . Amazon Comprehend provides NLP services like sentiment analysis, but sentiment is not sufficient for full quality evaluation of research paper generation.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Model Evaluation Overview
AWS Generative AI Best Practices
AWS ML Specialty Study Guide - Evaluation and Feedback Loops in LLMs
NEW QUESTION # 178
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?
Answer: A
Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
* Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
* Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
* Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.
* Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner References:
* Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.
NEW QUESTION # 179
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