A company has set up a translation tool to help its customer service team handle issues from customers around the world. The company wants to evaluate the performance of the translation tool. The company sets up a parallel data process that compares the responses from the tool to responses from actual humans. Both sets of responses are generated on the same set of documents.
Which strategy should the company use to evaluate the translation tool?
A. Use the Bilingual Evaluation Understudy (BLEU) score to estimate the absolute translation quality of the two methods.
B. Use the Bilingual Evaluation Understudy (BLEU) score to estimate the relative translation quality of the two methods.
C. Use the BERTScore to estimate the absolute translation quality of the two methods.
D. Use the BERTScore to estimate the relative translation quality of the two methods.
Show Answer
Correct Answer: B
Explanation: BLEU is a standard machine translation evaluation metric that compares system output against one or more reference translations using n-gram overlap. It is primarily used to compare the relative performance of translation systems on the same test set rather than to provide an absolute measure of translation quality. BERTScore is a semantic similarity metric and often correlates better with human judgment, but the classic evaluation strategy described is BLEU for relative comparison.
Question 225
An AI practitioner wants to generate more diverse and more creative outputs from a large language model (LLM).
How should the AI practitioner adjust the inference parameter?
A. Increase the temperature value.
B. Decrease the Top K value.
C. Increase the response length.
D. Decrease the prompt length.
Show Answer
Correct Answer: A
Explanation: Increasing the temperature increases randomness in token sampling, leading to more diverse and creative outputs. Decreasing Top K generally restricts the candidate token set and tends to reduce diversity. Response length and prompt length do not directly control creativity.
Question 226
A company wants to control employee access to publicly available foundation models (FMs).
Which solution meets these requirements?
A. Analyze cost and usage reports in AWS Cost Explorer.
B. Download AWS security and compliance documents from AWS Artifact.
C. Configure Amazon SageMaker JumpStart to restrict discoverable FMs.
D. Build a hybrid search solution by using Amazon OpenSearch Service.
Show Answer
Correct Answer: C
Explanation: Amazon SageMaker JumpStart provides controlled access to publicly available foundation models and supports restricting which foundation models are discoverable and usable through configuration and IAM permissions. The other options do not control employee access to foundation models: Cost Explorer is for cost analysis, AWS Artifact provides compliance documents, and OpenSearch Service is for search solutions.
Question 227
An ecommerce company wants to group customers based on their purchase history and preferences to personalize the user experience of the company's application.
Which ML technique should the company use?
A. Classification
B. Clustering
C. Regression
D. Content generation
Show Answer
Correct Answer: B
Explanation: Clustering is the appropriate machine learning technique for grouping customers with similar purchase histories and preferences without requiring labeled outcomes. This unsupervised learning approach is commonly used for customer segmentation to enable personalization. Classification predicts predefined labels, regression predicts continuous values, and content generation creates new content rather than grouping data.
Question 228
An animation company wants to provide subtitles for its content.
Which AWS service meets this requirement?
A. Amazon Comprehend
B. Amazon Polly
C. Amazon Transcribe
D. Amazon Translate
Show Answer
Correct Answer: C
Explanation: Amazon Transcribe is the AWS speech-to-text service that converts spoken audio into text, making it suitable for generating captions and subtitles for video content. Amazon Comprehend analyzes text, Amazon Polly converts text to speech, and Amazon Translate translates text between languages.
Question 229
A company uses a third-party model on Amazon Bedrock to analyze confidential documents. The company is concerned about data privacy.
Which statement describes how Amazon Bedrock protects data privacy?
A. User inputs and model outputs are anonymized and shared with third-party model providers.
B. User inputs and model outputs are not shared with any third-party model providers.
C. User inputs are kept confidential, but model outputs are shared with third-party model providers.
D. User inputs and model outputs are redacted before the inputs and outputs are shared with third-party model providers.
Show Answer
Correct Answer: B
Explanation: Amazon Bedrock is designed so that prompts (inputs) and generated outputs are not shared with third-party model providers. By default, customer content is not used to train the underlying foundation models unless the customer explicitly opts into such programs. This protects the privacy of confidential data during inference.
Question 230
A grocery store wants to create a chatbot to help customers find products in the store. The chatbot must check the inventory in real time and provide the product location in the store.
Which prompt engineering technique should the store use to build the chatbot?
A. Zero-shot prompting
B. Few-shot prompting
C. Least-to-most prompting
D. Reasoning and acting (ReAct) prompting
Show Answer
Correct Answer: D
Explanation: ReAct (Reasoning and Acting) prompting is designed for tasks that require both reasoning about a user's request and taking actions such as calling external tools or APIs. A grocery chatbot that must query real-time inventory and then return the product's in-store location fits this pattern. Zero-shot, few-shot, and least-to-most prompting do not inherently provide a mechanism for interacting with live external systems.
Question 231
A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions about their loans. The bank wants to ensure that the model does not reveal any private customer data.
Which solution meets these requirements?
A. Use Amazon Bedrock Guardrails.
B. Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM.
C. Increase the Top-K parameter of the LLM.
D. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM.
Show Answer
Correct Answer: B
Explanation: Removing or anonymizing personally identifiable information (PII) before fine-tuning prevents the model from learning sensitive customer data in the first place. Guardrails help constrain or filter model inputs and outputs at inference time, but they do not eliminate the risk of memorization during fine-tuning. Increasing Top-K is unrelated to privacy, and encrypting data in Amazon S3 protects data at rest rather than preventing the model from exposing learned information.
Question 232
A company wants to use a large language model (LLM) to generate product descriptions. The company wants to give the model example descriptions that follow a format.
Which prompt engineering technique will generate descriptions that match the format?
A. Zero-shot prompting
B. Chain-of-thought prompting
C. One-shot prompting
D. Few-shot prompting
Show Answer
Correct Answer: D
Explanation: Few-shot prompting provides multiple example product descriptions so the model can infer and reproduce the desired format and style. Zero-shot provides no examples, one-shot provides only a single example, and chain-of-thought is intended to elicit reasoning rather than enforce output formatting.
Question 233
A company is making a chatbot. The chatbot uses Amazon Lex and Amazon OpenSearch Service. The chatbot uses the company's private data to answer questions. The company needs to convert the data into a vector representation before storing the data in a database.
Which type of foundation model (FM) meets these requirements?
A. Text completion model
B. Instruction following model
C. Text embeddings model
D. Image generation model
Show Answer
Correct Answer: C
Explanation: A text embeddings model converts text into dense vector representations that capture semantic meaning. These embeddings are stored in a vector database or Amazon OpenSearch Service vector index to support semantic search and retrieval for chatbot/RAG applications.
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