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NEW QUESTION # 22
A retail company has deployed an ML model to predict whether customers will purchase a product. The dataset is highly imbalanced. Only 5% of customers make purchases. The model shows 95% accuracy.
However, the company reports that the model rarely identifies actual buyers.
Which metric should the company use instead to evaluate the model's performance?
Answer: D
Explanation:
The verified answer is B. F1 score. This is a classic imbalanced classification problem. Only 5% of customers make purchases, so a model can achieve 95% accuracy by predicting "will not purchase" for nearly everyone.
That looks good numerically but fails the business purpose because the model rarely identifies actual buyers.
AWS documentation describes F1 as a balanced measure that takes class balance into account and defines it as the harmonic mean of precision and recall.
AWS machine learning documentation also explains that F1 is commonly used for classification tasks and that a high F1 score indicates the model performs well for both precision and recall. If precision or recall is weak, the F1 score drops, which helps expose problems that accuracy hides.
Option A is incorrect because overall accuracy is already misleading in the scenario. The model's 95% accuracy is probably caused by the majority class: customers who do not buy. Accuracy does not show whether the model identifies the minority class, which is the actual buyer group.
Option C is incorrect because training time for each epoch measures training efficiency, not prediction quality. A model can train quickly and still fail to identify buyers.
Option D is incorrect because cost per inference request is an operational cost metric. It does not evaluate whether the model correctly identifies purchasing customers.
Because the dataset is imbalanced and the model misses the minority class, the company should use F1 score instead of accuracy.
NEW QUESTION # 23
A manufacturing company uses AI to inspect products and find any damages or defects.
Which type of AI application is the company using?
Answer: B
Explanation:
The manufacturing company uses AI to inspect products for damages or defects, which involves analyzing visual data (e.g., images or videos of products). This task falls under computer vision, a type of AI application that enables machines to interpret and understand visual information, such as identifying defects in manufacturing.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Computer vision enables machines to interpret and understand visual data from the world, such as images or videos. Common applications include defect detection in manufacturing, where AI models analyze product images to identify damages or anomalies." (Source: AWS AI Practitioner Learning Path, Module on AI Concepts) Detailed Explanation:
* Option A: Recommendation systemRecommendation systems suggest items or actions based on user preferences (e.g., product recommendations). They are not relevant for inspecting products for defects.
* Option B: Natural language processing (NLP)NLP focuses on processing and understanding text or speech, not visual data like product images. This option is incorrect.
* Option C: Computer visionThis is the correct answer. Computer vision is used for tasks like defect detection in manufacturing by analyzing visual data to identify damages or defects.
* Option D: Image processingWhile image processing involves manipulating images (e.g., filtering, resizing), it is a lower-level technique, not an AI application. Computer vision, which often uses image processing as a component, is the broader AI application here.
References:
AWS AI Practitioner Learning Path: Module on AI Concepts
Amazon Rekognition Developer Guide: Image Analysis (https://docs.aws.amazon.com/rekognition/latest/dg
/what-is.html)
AWS Documentation: Introduction to Computer Vision (https://aws.amazon.com/computer-vision/)
NEW QUESTION # 24
A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns.
The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.
Which solution meets these requirements?
Answer: D
Explanation:
Creating effective prompts is the best solution to ensure that the content generated by a pre-trained generative AI model aligns with the company's brand voice and messaging requirements.
* Effective Prompt Engineering:
* Involves crafting prompts that clearly outline the desired tone, style, and content guidelines for the model.
* By providing explicit instructions in the prompts, the company can guide the AI to generate content that matches the brand's voice and messaging.
* Why Option C is Correct:
* Guides Model Output: Ensures the generated content adheres to specific brand guidelines by shaping the model's response through the prompt.
* Flexible and Cost-effective: Does not require retraining or modifying the model, which is more resource-efficient.
* Why Other Options are Incorrect:
* A. Optimize the model's architecture and hyperparameters: Improves model performance but does not specifically address alignment with brand voice.
* B. Increase model complexity: Adding more layers may not directly help with content alignment.
* D. Pre-training a new model: Is a costly and time-consuming process that is unnecessary if the goal is content alignment.
NEW QUESTION # 25
A company's large language model (LLM) is experiencing hallucinations.
How can the company decrease hallucinations?
Answer: A
Explanation:
Hallucinations in large language models (LLMs) occur when the model generates outputs that are factually incorrect, irrelevant, or not grounded in the input data. To mitigate hallucinations, adjusting the model's inference parameters, particularly the temperature, is a well-documented approach in AWS AI Practitioner resources. The temperature parameter controls the randomness of the model's output. A lower temperature makes the model more deterministic, reducing the likelihood of generating creative but incorrect responses, which are often the cause of hallucinations.
Exact Extract from AWS AI Documents:
From the AWS documentation on Amazon Bedrock and LLMs:
"The temperature parameter controls the randomness of the generated text. Higher values (e.g., 0.8 or above) increase creativity but may lead to less coherent or factually incorrect outputs, while lower values (e.g., 0.2 or
0.3) make the output more focused and deterministic, reducing the likelihood of hallucinations." (Source: AWS Bedrock User Guide, Inference Parameters for Text Generation) Detailed Explanation:
Option A: Set up Agents for Amazon Bedrock to supervise the model training.Agents for Amazon Bedrock are used to automate tasks and integrate LLMs with external tools, not to supervise model training or directly address hallucinations. This option is incorrect as it does not align with the purpose of Agents in Bedrock.
Option B: Use data pre-processing and remove any data that causes hallucinations.While data pre-processing can improve model performance, identifying and removing specific data that causes hallucinations is impractical because hallucinations are often a result of the model's generative process rather than specific problematic data points. This approach is not directly supported by AWS documentation for addressing hallucinations.
Option C: Decrease the temperature inference parameter for the model.This is the correct approach. Lowering the temperature reduces the randomness in the model's output, making it more likely to stick to factual and contextually relevant responses. AWS documentation explicitly mentions adjusting inference parameters like temperature to control output quality and mitigate issues like hallucinations.
Option D: Use a foundation model (FM) that is trained to not hallucinate.No foundation model is explicitly trained to "not hallucinate," as hallucinations are an inherent challenge in LLMs. While some models may be fine-tuned for specific tasks to reduce hallucinations, this is not a standard feature of foundation models available on Amazon Bedrock.
References:
AWS Bedrock User Guide: Inference Parameters for Text Generation (https://docs.aws.amazon.com/bedrock
/latest/userguide/model-parameters.html)
AWS AI Practitioner Learning Path: Module on Large Language Models and Inference Configuration Amazon Bedrock Developer Guide: Managing Model Outputs (https://docs.aws.amazon.com/bedrock/latest
/devguide/)
NEW QUESTION # 26
A company wants to implement a generative AI assistant to provide consistent responses to various phrasings of user questions.
Which advantages can generative AI provide in this use case?
Answer: A
Explanation:
The correct answer is B - Adaptability and responsiveness, which are core strengths of generative AI models such as the foundation models available in Amazon Bedrock. According to AWS documentation, generative AI systems excel at understanding natural language variations, meaning they can interpret different phrasings, synonyms, sentence structures, and conversational styles while still generating contextually consistent answers. This capability comes from pretraining on diverse natural language corpora, allowing models to generalize across multiple linguistic patterns. AWS highlights that generative AI models are designed to handle "flexible, dynamic, and conversational inputs" and provide responses grounded in understanding user intent rather than matching exact keywords. Options A and D describe infrastructure performance characteristics, not the reasoning ability required for this use case. Option C (deterministic outputs) is incorrect because LLMs are inherently probabilistic and not fixed unless using advanced constraints. Therefore, generative AI's adaptability to varied user phrasing makes it ideal for assistants requiring consistent, intent-based responses.
Referenced AWS Documentation:
Amazon Bedrock Developer Guide - Foundation Model Capabilities
AWS Generative AI Best Practices - Natural Language Understanding
NEW QUESTION # 27
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