A company wants to develop an AI assistant for employees to query internal data.
Which AWS service will meet this requirement?
A. Amazon Rekognition
B. Amazon Textract
C. Amazon Lex
D. Amazon Q Business
Show Answer
Correct Answer: D
Explanation: The requirement is an AI assistant that allows employees to query internal company data. Amazon Q Business is purpose-built for this use case: it provides a managed, generative AI assistant that securely connects to enterprise data sources (documents, wikis, databases, SaaS apps) and supports natural-language queries with access controls. Rekognition is for image/video analysis, Textract is for document text extraction, and Lex is a chatbot framework that does not natively provide enterprise data retrieval on its own.
Question 51
A company must comply with regulatory standards to develop and use trustworthy AI management solutions.
Which approach will meet this requirement?
A. Optimize model inference time by using high-powered GPUs for faster processing.
B. Ensure that each AI solution is developed only by technical experts. Do not involve other stakeholders.
C. Constrain transparency and user access to each model’s decision-making process.
D. Ensure fairness, transparency, accountability, and security throughout the lifecycle of each AI solution.
Show Answer
Correct Answer: D
Explanation: Regulatory and responsible AI standards require a holistic, lifecycle-based approach that embeds fairness, transparency, accountability, and security into the design, development, deployment, and monitoring of AI systems. This aligns with established frameworks for trustworthy AI. The other options focus on performance, limit stakeholder involvement, or reduce transparency, which conflict with regulatory expectations.
Question 52
HOTSPOT
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Select and order the steps from the following list to correctly describe the ML lifecycle for a new custom model. Select each step one time.
Show Answer
Correct Answer: Step 1: Define the business objective
Step 2: Process the data
Step 3: Develop and train the model
Step 4: Deploy the model
Explanation: The ML lifecycle starts by clarifying the business goal, then preparing and processing data. Next, the model is developed and trained using the prepared data, and finally the trained model is deployed for use.
Question 52
Which statement accurately describes Retrieval Augmented Generation (RAG)?
A. A process that uses large amounts of new data to train large language models (LLMs) to improve LLM performance
B. A process by which large language models (LLMs) reference external authoritative knowledge bases to enhance the relevance and accuracy of LLM responses without re-training
C. A process that limits large language models (LLMs) exclusively to their original training data to improve response speed for business applications without re-training
D. A process that focuses on language translation tasks for businesses that operate in multiple countries
Show Answer
Correct Answer: B
Explanation: Retrieval-Augmented Generation enhances LLM outputs by retrieving relevant information from external knowledge sources at inference time and using it to ground the response, improving accuracy and relevance without retraining the model.
Question 53
A company wants to build an AI assistant to provide responses to user queries. The AI assistant must evaluate specific data sources, query external APIs, generate response options, and compare and prioritize response options.
Which Amazon Bedrock feature or resource will meet these requirements?
A. Prompt Management
B. Response streaming
C. Knowledge Bases
D. Agents
Show Answer
Correct Answer: D
Explanation: Amazon Bedrock Agents are designed to orchestrate multi-step AI workflows. They can evaluate and retrieve information from data sources, call external APIs via action groups, perform reasoning to generate multiple response options, and compare or prioritize those options before returning a final response. The other options do not support this level of autonomous planning and tool invocation.
Question 53
A company wants to generate synthetic data responses for multiple prompts from a large volume of data. The company wants to use an API method to generate the responses. The company does not need to generate the responses immediately.
Which solution meets these requirements with the LEAST development effort?
A. Input the prompts into the model. Generate responses by using real-time inference.
B. Use Amazon Bedrock batch inference. Generate responses asynchronously.
C. Use Amazon Bedrock agents. Build an agent system to process the prompts recursively.
D. Use AWS Lambda functions to automate the task. Submit one prompt after another and store each response.
Show Answer
Correct Answer: B
Explanation: The company needs to generate a large volume of synthetic responses without requiring immediate results, and wants the least development effort. Amazon Bedrock batch inference is specifically designed for asynchronous, high-volume prompt processing using an API, with minimal orchestration or custom code. Real-time inference and Lambda-based loops require more operational effort, and agents are unnecessary for simple batch generation. Therefore, batch inference best fits the requirements.
Question 54
A company is analyzing financial transaction records. The company categorizes the records as either personal or business. The company inserts the categories into the transaction records.
Which data preparation step does this describe?
A. Data encoding
B. Data labeling
C. Data normalization
D. Data balancing
Show Answer
Correct Answer: B
Explanation: The company is assigning categorical tags (personal or business) to existing transaction records. Adding such class labels or annotations to data is the definition of data labeling, commonly used to prepare data for supervised analysis or modeling.
Question 54
Which foundation model (FM) in Amazon Bedrock can be fine-tuned for text, image, and video comprehension?
A. Amazon Nova Pro
B. Amazon Titan Multimodal Embeddings G1
C. Amazon Titan Text G1 - Express
D. Amazon Nova Micro
Show Answer
Correct Answer: A
Explanation: Amazon Nova Pro is a multimodal foundation model in Amazon Bedrock that supports comprehension across text, images, and video, and it can be fine-tuned using multimodal datasets. The Titan Text and Titan Multimodal Embeddings models are not fine-tunable for multimodal comprehension tasks, and Nova Micro is not designed for this capability.
Question 55
A company receives a large amount of unstructured user feedback in text format. The company wants to analyze the sentiment of the user feedback. Which solution will meet these requirements?
A. Use a large language model (LLM) to perform natural language processing (NLP) for sentiment analysis.
B. Use a regression algorithm to classify the feedback based on predefined categories. Then, analyze user sentiment.
C. Use a recommendation engine algorithm to detect user sentiment.
D. Use a time series algorithm to predict user sentiment based on past feedback.
Show Answer
Correct Answer: A
Explanation: The data consists of unstructured text, and the goal is to determine sentiment (for example, positive, negative, or neutral). This is a classic natural language processing task. Large language models and other NLP-based sentiment analysis models are specifically designed to understand free-form text and infer sentiment. Regression, recommendation engines, and time series algorithms are not appropriate for directly analyzing sentiment from unstructured text.
Question 55
A company stores customer data in OpenSearch. The company wants an AI solution to retrieve specific customer information from the stored data. The AI solution must convert queries into data requests and generate CSV files from the results. Then, the AI solution must upload the CSV files to Amazon S3.
Which solution will meet these requirements in the MOST operationally-efficient way?
A. Create an AI agent to perform the required steps.
B. Use a single foundation model (FM) with few-shot prompting.
C. Create a software application without using AI to perform the required steps.
D. Train a decision tree model to generate a solution based on user questions.
Show Answer
Correct Answer: A
Explanation: An AI agent is designed to handle multi-step workflows with reasoning and action execution. It can interpret user queries, translate them into OpenSearch requests, process results into CSV files, and upload those files to Amazon S3 by invoking the required APIs. This approach minimizes custom orchestration code and is more operationally efficient than prompt-only models, non-AI software, or training a separate ML model.
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