A healthcare company is building an AI solution to predict patient readmission within 30 days of patient discharge. The company has trained a model on historical patient data including medical history, demographics, and treatment specifications, to provide readmission predictions in real time.
Which task describes AI model inference in this scenario?
A. Gather historical patient readmission data.
B. Use appropriate metrics and assess model performance.
C. Use data to identify patient patterns and correlations.
D. Use a trained model to predict patient readmission.
Show Answer
Correct Answer: D
Explanation: AI model inference is the phase where a trained model is applied to new, unseen data to generate predictions. In this scenario, inference occurs when the trained model is used in real time to predict whether a patient will be readmitted within 30 days after discharge.
Question 91
A company needs to apply numerical transformations to a set of images to transpose and rotate the images.
Which solution will meet these requirements in the MOST operationally efficient way?
A. Create a deep neural network by using the images as input.
B. Create an AWS Lambda function to perform the transformations.
C. Use an Amazon Bedrock large language model (LLM) with a high temperature.
D. Use AWS Glue Data Quality to make corrections to each image.
Show Answer
Correct Answer: B
Explanation: The task requires simple numerical image transformations (transpose and rotate), not machine learning or data quality analysis. An AWS Lambda function can efficiently perform these operations using standard image-processing libraries, requires no server management, scales automatically, and is cost-effective. The other options introduce unnecessary complexity and are not suited for straightforward image manipulation.
Question 92
A company is working on a large language model (LLM) and noticed that the LLM’s outputs are not as diverse as expected.
Which parameter should the company adjust?
A. Temperature
B. Batch size
C. Learning rate
D. Optimizer type
Show Answer
Correct Answer: A
Explanation: Output diversity during text generation is controlled by the temperature parameter. Increasing temperature increases randomness in token sampling, leading to more varied and creative outputs, while lower temperature makes responses more deterministic. The other options relate to training dynamics, not inference-time diversity.
Question 93
A company wants to identify groups for its customers based on the customers’ demographics and buying patterns.
Which algorithm should the company use to meet this requirement?
A. K-nearest neighbors (k-NN)
B. K-means
C. Decision tree
D. Support vector machine
Show Answer
Correct Answer: B
Explanation: The goal is to group customers based on similarities in demographics and buying patterns without predefined labels, which is an unsupervised learning task. K-means is a classic unsupervised clustering algorithm designed to partition data into groups based on feature similarity. The other options (k-NN, decision tree, SVM) are supervised algorithms requiring labeled data.
Question 94
A company uses Amazon Bedrock to implement a generative AI assistant on a website. The AI assistant helps customers with product recommendations and purchasing decisions.
The company wants to measure the direct impact of the AI assistant on sales performance.
Which metric will meet these requirements?
A. The conversion rate of customers who purchase products after AI assistant interactions.
B. The number of customer interactions with the AI assistant
C. Sentiment analysis scores from customer feedback after AI assistant interactions
D. Natural language understanding accuracy rates
Show Answer
Correct Answer: A
Explanation: The goal is to measure the direct impact of the AI assistant on sales performance. Conversion rate of customers who purchase after interacting with the AI assistant directly links assistant usage to actual sales outcomes. The other metrics measure engagement, sentiment, or technical performance, but not revenue-related impact.
Question 95
A customer service team is developing an application to analyze customer feedback and automatically classify the feedback into different categories. The categories include product quality, customer service, and delivery experience.
Which A1 concept does this scenario present?
A. Computer vision
B. Natural language processing (NLP)
C. Recommendation systems
D. Fraud detection
Show Answer
Correct Answer: B
Explanation: The application analyzes and classifies text-based customer feedback into categories. Understanding and processing human language in text form is the core function of Natural Language Processing (NLP), making option B the correct choice.
Question 96
An education company is building a chatbot whose target audience is teenagers. The company is training a custom large language model (LLM). The company wants the chatbot to speak in the target audience's language style by using creative spelling and shortened words.
Which metric will assess the LLM's performance?
A. F1 score
B. BERTScore
C. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
D. Bilingual Evaluation Understudy (BLEU) score
Show Answer
Correct Answer: B
Explanation: The goal is to assess whether the chatbot’s outputs match a creative, non-standard language style (slang, abbreviations, creative spelling). BERTScore compares generated text to reference text using contextual embeddings, focusing on semantic similarity rather than exact n‑gram overlap, making it robust to stylistic and spelling variations. BLEU and ROUGE rely on surface overlap, and F1 is not suited for free-form text generation quality.
Question 97
A company wants to create a chatbot that answers questions about human resources policies. The company is using a large language model (LLM) and has a large digital documentation base.
Which technique should the company use to optimize the generated responses?
A. Use Retrieval Augmented Generation (RAG).
B. Use few-shot prompting.
C. Set the temperature to 1.
D. Decrease the token size.
Show Answer
Correct Answer: A
Explanation: Retrieval Augmented Generation (RAG) retrieves relevant HR policy documents from the company’s documentation base at query time and uses them to ground the LLM’s response. This improves accuracy, relevance, and factual correctness when answering questions based on large internal knowledge sources.
Question 98
A food service company wants to collect a dataset to predict customer food preferences. The company wants to ensure that the food preferences of all demographics are included in the data.
Which dataset characteristic does this scenario present?
A. Accuracy
B. Diversity
C. Recency bias
D. Reliability
Show Answer
Correct Answer: B
Explanation: The scenario emphasizes including food preferences from all demographics so that the dataset represents a wide range of groups. This is the definition of dataset diversity. Accuracy, reliability, and recency bias do not address representation across demographics.
Question 99
HOTSPOT
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A company is using Amazon SageMaker to develop AI models.
Select the correct SageMaker feature or resource from the following list for each step in the AI model lifecycle workflow. Each SageMaker feature or resource should be selected one time or not at all.
Show Answer
Correct Answer: Managing different versions of the model: SageMaker Model Registry
Using the current model to make predictions: SageMaker Serverless Inference
Explanation: SageMaker Model Registry stores, tracks, and manages versions and approvals of models. SageMaker Serverless Inference deploys a model endpoint to serve real-time predictions without managing servers.
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