HOTSPOT
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An ecommerce company is developing a generative AI solution to create personalized product recommendations for its application users. The company wants to track how effectively the AI solution increases product sales and user engagement in the application.
Select the correct business metric from the following list for each business goal. Each business metric should be selected one time.
Show Answer
Correct Answer: Engagement with recommendations:
Click-through rate (CTR)
Total value of user purchases:
Average order value (AOV)
Users returning to the platform:
Retention rate
Explanation: CTR measures how often users interact with recommended items.
AOV reflects changes in spending per purchase influenced by recommendations.
Retention rate indicates whether users return over time, showing long-term engagement.
Question 56
A company that streams media is selecting an Amazon Nova foundation model (FM) to process documents and images. The company is comparing Nova Micro and Nova Lite. The company wants to minimize costs.
Which model characteristics should the company consider to meet these requirements?
A. Nova Micro uses transformer-based architectures. Nova Lite does not use transformer-based architectures.
B. Nova Micro supports only text data. Nova Lite is optimized for numerical data.
C. Nova Micro supports only text. Nova Lite supports images, videos, and text.
D. Nova Micro runs only on CPUs. Nova Lite runs only on GPUs.
Show Answer
Correct Answer: C
Explanation: To process both documents and images at minimal cost, the company should choose the lowest-cost model that supports multimodal inputs. Nova Micro is text-only, while Nova Lite supports text and images (and video). Therefore, Nova Lite meets the document-and-image requirement, making option C correct.
Question 57
A company is developing an ML application. The application must automatically group similar customers and products based on their characteristics. Which ML strategy should the company use to meet these requirements?
A. Unsupervised learning
B. Supervised learning
C. Reinforcement learning
D. Semi-supervised learning
Show Answer
Correct Answer: A
Explanation: The requirement is to automatically group similar customers and products based on their characteristics without labeled data. This is a clustering problem, which is a core use case of unsupervised learning. Supervised and semi-supervised learning require labels, and reinforcement learning focuses on sequential decision-making, not grouping.
Question 57
A research company is growing microbiological cultures. The company captures images of the cultures without any prior labeled data about growth areas. The company needs to identify the regions of the images that show culture growth.
Which ML technique will meet these requirements?
A. Logistic regression
B. Decision tree
C. Clustering
D. Dimensionality reduction
Show Answer
Correct Answer: C
Explanation: The images have no labeled data, so an unsupervised learning approach is required. The task is to identify regions with similar visual characteristics corresponding to culture growth versus background. Clustering groups similar pixels or image regions based on features (e.g., color, texture, intensity) without labels, making it suitable. Logistic regression and decision trees require labeled data, and dimensionality reduction focuses on feature compression rather than region identification.
Question 58
A company has created multiple ML models. The company needs a solution for storing, managing, and versioning the models.
Which AWS service or feature meets these requirements?
A. AWS Audit Manager
B. Amazon SageMaker Model Monitor
C. Amazon SageMaker Model Registry
D. Amazon SageMaker Canvas
Show Answer
Correct Answer: C
Explanation: Amazon SageMaker Model Registry is specifically designed to store, manage, and version machine learning models, including model artifacts, metadata, approval status, and deployment history. The other options do not provide model versioning and lifecycle management capabilities.
Question 58
A company uses Amazon SageMaker AI to generate article summaries in multiple languages. The company needs a metric to evaluate the quality of the summary translations in multiple languages.
Which evaluation metric will meet these requirements?
A. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
B. Bilingual evaluation understudy (BLEU)
C. Area Under the ROC Curve (AUC)
D. Precision
Show Answer
Correct Answer: B
Explanation: The task is to evaluate the quality of translated summaries across multiple languages. BLEU is specifically designed to measure machine translation quality by comparing n‑gram overlap between a generated translation and one or more reference translations. ROUGE is mainly used for summarization quality, while AUC and Precision are classification metrics and not suitable for evaluating translation quality.
Question 59
A company is developing a generative AI application to automatically generate product descriptions for an ecommerce website. The product descriptions must consist of paragraphs of text that are consistent in style and tone. The application must generate thousands of unique descriptions each day.
Which type of generative model will meet these requirements?
A. A variational autoencoder (VAE) model
B. A transformer-based model
C. A diffusion model
D. A generative adversarial network (GAN) model
Show Answer
Correct Answer: B
Explanation: The task requires generating large volumes of coherent, stylistically consistent text. Transformer-based models are specifically designed for natural language generation, can maintain tone and structure over paragraphs, and scale efficiently to produce thousands of unique text outputs. VAEs and GANs are not well-suited for high-quality long-form text, and diffusion models are primarily used for images and other continuous data.
Question 59
A company is developing a product recommendation application by using a generative AI model. The company must minimize the application’s environmental impact.
Which solution will meet these requirements?
A. Optimize the deployed model architecture to prioritize computational efficiency during model inference.
B. Adopt a distributed inference approach by using multiple smaller models across multiple Availability Zones.
C. Adopt a hybrid strategy by deploying the model on premises and storing the data on AWS.
D. Deploy multiple models and use a dynamic model selection mechanism that queries different models randomly.
Show Answer
Correct Answer: A
Explanation: Minimizing environmental impact focuses on reducing energy consumption. Optimizing the model architecture for computational efficiency during inference directly lowers compute usage, power consumption, and carbon footprint. The other options increase complexity, duplication, or randomness without reducing overall energy use.
Question 60
A company is developing an ML model to predict heart disease risk. The model uses patient data, such as age, cholesterol, blood pressure, smoking status, and exercise habits. The dataset includes a target value that indicates whether a patient has heart disease.
Which ML technique will meet these requirements?
A. Unsupervised learning
B. Supervised learning
C. Reinforcement learning
D. Semi-supervised learning
Show Answer
Correct Answer: B
Explanation: The dataset includes input features and an explicit target label indicating whether a patient has heart disease. Learning from labeled examples to predict an outcome is supervised learning, specifically a classification task.
Question 60
A company is building a job recommendation system based on job posting data and job seeker user profiles. The system shows bias in job recommendations based on gender for user profiles that are otherwise equivalent.
Which principle should the company follow to address this issue, according to AWS best practices for responsible AI?
A. Governance
B. Explainability
C. Controllability
D. Fairness
Show Answer
Correct Answer: D
Explanation: The issue described is biased recommendations based on gender for otherwise equivalent profiles. AWS responsible AI best practices address this under **Fairness**, which focuses on preventing discrimination and ensuring equitable treatment across protected attributes such as gender. Therefore, the correct principle to follow is Fairness.
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