Practice Test 3

1. A company is developing an ML model to make loan approvals. The company must implement a solution to detect bias in the model. The company must also be able to explain the model’s predictions. Which solution will meet these requirements?
   • A. Amazon SageMaker Clarify
   • B. Amazon SageMaker Data Wrangler
   • C. Amazon SageMaker Model Cards
   • D. AWS AI Service Cards
   Answer

   Correct answer: A

   Explanation: Amazon SageMaker Clarify is a tool that helps detect bias in machine learning models and provides explainability for model predictions. It allows users to understand the factors influencing a model’s predictions and assess whether the model is fair across different demographic groups. This is exactly what the company needs for detecting bias and explaining model decisions, especially for high-stakes applications like loan approvals. Bias detection: SageMaker Clarify can analyze the training data and the model’s predictions to identify and mitigate bias. Explainability: It provides features for explaining the predictions made by the model, which helps users understand the reasons behind the model’s decisions.

2. A company has developed a generative text summarization model by using Amazon Bedrock. The company will use Amazon Bedrock automatic model evaluation capabilities. Which metric should the company use to evaluate the accuracy of the model?
   • A. Area Under the ROC Curve (AUC) score
   • B. F1 score
   • C. BERTScore
   • D. Real world knowledge (RWK) score
   Answer

   Correct answer: C

   Explanation: BERTScore is a metric specifically designed for evaluating the quality of text generated by models, particularly in tasks like text summarization and machine translation. It uses contextual embeddings from a pre-trained BERT model to compare generated text with reference text at the word level, making it wellsuited for evaluating the accuracy of generative text summarization models. BERTScore assesses the semantic similarity between the generated text and the reference text, providing a more nuanced evaluation compared to traditional methods that focus on exact matches.

3. An AI practitioner wants to predict the classification of flowers based on petal length, petal width, sepal length, and sepal width. Which algorithm meets these requirements?
   • A. K-nearest neighbors (k-NN)
   • B. K-mean
   • C. Autoregressive Integrated Moving Average (ARIMA)
   • D. Linear regression
   Answer

   Correct answer: A

   Explanation: K-nearest neighbors (k-NN) is a supervised learning algorithm used for classification tasks. It works by classifying a data point based on how its features (such as petal length, petal width, sepal length, and sepal width in this case) are similar to the data points in the training set. For this problem, where the task is to classify flowers based on their features, k-NN is an appropriate choice. k-NN is simple and effective for classification problems where the decision boundary is not necessarily linear, as it looks at the closest neighbors to make predictions.

4. A company is using custom models in Amazon Bedrock for a generative AI application. The company wants to use a company managed encryption key to encrypt the model artifacts that the model customization jobs create. Which AWS service meets these requirements?
   • A. AWS Key Management Service (AWS KMS)
   • B. Amazon Inspector
   • C. Amazon Macie
   • D. AWS Secrets Manager
   Answer

   Correct answer: A

   Explanation: AWS Key Management Service (AWS KMS) is a fully managed service that allows you to create and control encryption keys used to encrypt your data. It is ideal for scenarios like this, where a company wants to use a custom, company-managed encryption key to protect model artifacts. AWS KMS allows you to securely manage keys for encrypting and decrypting data, and it integrates with various AWS services, including Amazon Bedrock. AWS KMS provides centralized key management, and it is designed for use cases that involve encrypting both data and artifacts, such as model customization jobs.

5. A company wants to use large language models (LLMs) to produce code from natural language code comments. Which LLM feature meets these requirements?
   • A. Text summarization
   • B. Text generation
   • C. Text completion
   • D. Text classification
   Answer

   Correct answer: B

   Explanation: Text generation is the feature of large language models (LLMs) that enables them to produce text based on a given prompt or input. In this scenario, the input would be natural language code comments, and the LLM would generate code based on those comments. This process involves understanding the context of the comment and generating corresponding code, which is the core functionality of text generation.

6. A company is introducing a mobile app that helps users learn foreign languages. The app makes text more coherent by calling a large language model (LLM). The company collected a diverse dataset of text and supplemented the dataset with examples of more readable versions. The company wants the LLM output to resemble the provided examples. Which metric should the company use to assess whether the LLM meets these requirements?
   • A. Value of the loss function
   • B. Semantic robustness
   • C. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score
   • D. Latency of the text generation
   Answer

   Correct answer: C

   Explanation: ROUGE is a set of metrics that evaluates the quality of summaries by comparing the overlap of n-grams, word sequences, and word pairs between the model output and reference examples. Since the company is working on a language model that improves the coherence of text and wants the output to resemble the provided examples (which are more readable versions of the original text), ROUGE is the most appropriate metric. It assesses how closely the generated text matches the reference text in terms of content and readability. ROUGE score is commonly used to evaluate the performance of models in tasks like summarization, where the goal is to ensure the generated text aligns closely with human-provided examples.

7. A company notices that its foundation model (FM) generates images that are unrelated to the prompts. The company wants to modify the prompt techniques to decrease unrelated images. Which solution meets these requirements?
   • A. Use zero-shot prompts.
   • B. Use negative prompts.
   • C. Use positive prompts.
   • D. Use ambiguous prompts.
   Answer

   Correct answer: B

   Explanation: Negative prompts are used to explicitly instruct the model about what to avoid or not generate. By providing the model with specific guidance on what is not desired (e.g., by including terms or concepts that should not appear in the image), the model can better focus on generating relevant and related content. This technique can help reduce unrelated or irrelevant images by constraining the model’s creative generation process.

8. A company wants to use a large language model (LLM) to generate concise, feature-specific descriptions for the company’s products. Which prompt engineering technique meets these requirements?
   • A. Create one prompt that covers all products. Edit the responses to make the responses more specific, concise, and tailored to each product.
   • B. Create prompts for each product category that highlight the key features. Include the desired output format and length for each prompt response.
   • C. Include a diverse range of product features in each prompt to generate creative and unique descriptions.
   • D. Provide detailed, product-specific prompts to ensure precise and customized descriptions.
   Answer

   Correct answer: B

   Explanation: To generate concise, feature-specific descriptions for each product, the company should create prompts tailored to specific product categories. By highlighting the key features of each product category in the prompt, the model can focus on generating descriptions that are relevant and aligned with the unique attributes of each product. Additionally, specifying the desired output format and length ensures that the responses meet the company’s requirements for conciseness and clarity. Tailored prompts help ensure the model generates relevant and accurate descriptions by focusing on the most important features of each product category. Desired output format and length ensure the responses are consistent and concise, as required.

9. A company is developing an ML model to predict customer churn. The model performs well on the training dataset but does not accurately predict churn for new data. Which solution will resolve this issue?
   • A. Decrease the regularization parameter to increase model complexity.
   • B. Increase the regularization parameter to decrease model complexity.
   • C. Add more features to the input data.
   • D. Train the model for more epochs.
   Answer

   Correct answer: B

   Explanation: The issue described is a common case of overfitting, where the model performs well on the training data but fails to generalize to new, unseen data. This suggests that the model is too complex and has learned to memorize the training data rather than identifying generalizable patterns. Increasing the regularization parameter helps to reduce model complexity by penalizing large weights, thereby encouraging simpler models that are less likely to overfit. This will improve the model’s ability to generalize to new data, potentially improving performance on unseen customer data.

10. A company is implementing intelligent agents to provide conversational search experiences for its customers. The company needs a database service that will support storage and queries of embeddings from a generative AI model as vectors in the database. Which AWS service will meet these requirements?
    • A. Amazon Athena
    • B. Amazon Aurora PostgreSQL
    • C. Amazon Redshift
    • D. Amazon EMR
    Answer

    Correct answer: B

    Explanation: Amazon Aurora PostgreSQL is a relational database service that supports a wide range of applications, including those that require vector-based operations. Specifically, Aurora PostgreSQL can be extended with vector search capabilities using extensions like pgvector. This extension allows you to store, index, and query vector embeddings from generative AI models, making it well-suited for the company’s needs to store and query embeddings as vectors. pgvector is an extension for PostgreSQL that provides efficient similarity search for vector data, which is ideal for storing and querying embeddings.

11. A financial institution is building an AI solution to make loan approval decisions by using a foundation model (FM). For security and audit purposes, the company needs the AI solution’s decisions to be explainable. Which factor relates to the explainability of the AI solution’s decisions?
    • A. Model complexity
    • B. Training time
    • C. Number of hyperparameters
    • D. Deployment time
    Answer

    Correct answer: A

    Explanation: Model complexity plays a significant role in the explainability of an AI solution. Simpler models tend to be more explainable because it is easier to understand how they make decisions. For example, linear regression or decision trees are generally easier to interpret compared to more complex models like deep neural networks. In contrast, highly complex models, such as deep learning models, may provide very accurate results but are often considered “black boxes,” meaning their decision-making process is not easily interpretable. This lack of transparency makes it difficult to explain their decisions in a way that satisfies regulatory requirements or customer understanding.

12. A pharmaceutical company wants to analyze user reviews of new medications and provide a concise overview for each medication. Which solution meets these requirements?
    • A. Create a time-series forecasting model to analyze the medication reviews by using Amazon Personalize.
    • B. Create medication review summaries by using Amazon Bedrock large language models (LLMs).
    • C. Create a classification model that categorizes medications into different groups by using Amazon
    • D. Create medication review summaries by using Amazon Rekognition.
    Answer

    Correct answer: B

    Explanation: Amazon Bedrock provides access to a variety of large language models (LLMs), which are well-suited for tasks such as text summarization. By using LLMs, the pharmaceutical company can automatically generate concise and coherent summaries of user reviews for each medication. These models are designed to understand and process large amounts of text, making them ideal for summarizing user reviews in a clear and efficient manner. Amazon Bedrock allows the company to utilize LLMs that can generate summaries, which is exactly what is needed in this case.

13. A company wants to build a lead prioritization application for its employees to contact potential customers. The application must give employees the ability to view and adjust the weights assigned to different variables in the model based on domain knowledge and expertise. Which ML model type meets these requirements?
    • A. Logistic regression model
    • B. Deep learning model built on principal components
    • C. K-nearest neighbors (k-NN) model
    • D. Neural network
    Answer

    Correct answer: A

    Explanation: A logistic regression model is a simple, interpretable machine learning model that allows users to adjust the weights of different variables based on domain knowledge and expertise. Since logistic regression is based on a linear relationship between the input features and the predicted outcome, each feature has a corresponding weight (coefficient) that can be easily viewed and adjusted. This makes it a suitable choice for a lead prioritization application where domain experts need the ability to modify the model based on their knowledge.

14. A company wants to build an ML application. ML workload. Each Select and order the correct steps from the following list to develop a well-architected step should be selected one time.

    1. Deploy model
    2. Develop model
    3. Monitor model
    4. Define business goal and frame ML problem
    Answer

    Correct answer: 4 → 2 → 1 → 3

    Explanation: The typical sequence of steps in building an ML application involves: 1. Defining the business goal and framing the ML problem - This step involves understanding the business need and determining how ML can address it. 2. Developing the model - This involves selecting the appropriate algorithm, training the model, and evaluating it. 3. Deploying the model - Once the model is trained, it is deployed to production to serve predictions. 4. Monitoring the model - After deployment, the model is monitored to ensure it performs well over time and to detect any issues like model drift.

15. Which strategy will determine if a foundation model (FM) effectively meets business objectives?
    • A. Evaluate the model’s performance on benchmark datasets.
    • B. Analyze the model’s architecture and hyperparameters.
    • C. Assess the model’s alignment with specific use cases.
    • D. Measure the computational resources required for model deployment.
    Answer

    Correct answer: C

    Explanation: To determine if a foundation model (FM) effectively meets business objectives, the key is to assess how well the model aligns with the specific use cases or business goals it is intended to address. This involves evaluating whether the model can provide meaningful and accurate outputs relevant to the business’s needs, ensuring that it solves the real-world problems the company aims to tackle. Aligning with specific use cases ensures that the model’s capabilities are tailored to the tasks at hand, such as improving customer support, enhancing decision-making, or automating certain processes.

16. A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more data. Which type of learning should the company use to train the model?
    • A. Supervised learning
    • B. Unsupervised learning
    • C. Reinforcement learning
    • D. Active learning
    Answer

    Correct answer: A

    Explanation: In supervised learning, the model is trained using a labeled dataset, where each image (input) has a corresponding label (the type of animal, in this case). Since the company already has a large dataset of labeled images, supervised learning is the most appropriate approach. The model learns to classify the images based on the features and labels in the training data.

17. Which phase of the ML lifecycle determines compliance and regulatory requirements?
    • A. Feature engineering
    • B. Model training
    • C. Data collection
    • D. Business goal identification
    Answer

    Correct answer: C

    Explanation: The data collection phase of the ML lifecycle is the most relevant for determining compliance and regulatory requirements. During this phase, organizations must ensure that the data being collected and used for training the model complies with legal and regulatory standards, such as data privacy laws (e.g., GDPR, HIPAA), industry-specific regulations, and ethical considerations. The organization must also verify that they have the proper consent to use the data and that the data does not contain any biases or violate any regulations.

18. A food service company wants to develop an ML model to help decrease daily food waste and increase sales revenue. The company needs to continuously improve the model’s accuracy. Which solution meets these requirements?
    • A. Use Amazon SageMaker and iterate with newer data.
    • B. Use Amazon Personalize and iterate with historical data.
    • C. Use Amazon CloudWatch to analyze customer orders.
    • D. Use Amazon Rekognition to optimize the model.
    Answer

    Correct answer: A

    Explanation: To continuously improve the accuracy of the machine learning model, Amazon SageMaker is the appropriate solution. SageMaker allows for efficient model training, deployment, and iteration. The company can use SageMaker to retrain the model regularly with newer data to account for changes in customer behavior, market trends, and other dynamic factors that may affect food waste and sales revenue. Iterating with newer data helps improve the model’s performance over time, ensuring it remains accurate and relevant.

19. A company has developed an ML model to predict real estate sale prices. The company wants to deploy the model to make predictions without managing servers or infrastructure. Which solution meets these requirements?
    • A. Deploy the model on an Amazon EC2 instance.
    • B. Deploy the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
    • C. Deploy the model by using Amazon CloudFront with an Amazon S3 integration.
    • D. Deploy the model by using an Amazon SageMaker endpoint.
    Answer

    Correct answer: D

    Explanation: Amazon SageMaker provides a fully managed service for deploying machine learning models at scale without needing to manage servers or infrastructure. When you deploy a model using Amazon SageMaker endpoints, it handles all the infrastructure, scaling, and maintenance aspects. This allows the company to focus solely on making predictions and integrating the model into their application, without worrying about managing servers or clusters. Amazon SageMaker simplifies the deployment of ML models by providing scalable, secure, and serverless endpoints, which are ideal for serving predictions in real-time.

20. A company wants to develop an AI application to help its employees check open customer claims, identify details for a specific claim, and access documents for a claim. Which solution meets these requirements?
    • A. Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.
    • B. Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.
    • C. Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application.
    • D. Use Amazon SageMaker to build the application by training a new ML model.
    Answer

    Correct answer: B

    Explanation: Amazon Bedrock is a service that allows you to build AI-powered applications using foundation models (FMs) without managing infrastructure. Agents for Amazon Bedrock allows you to create intelligent agents that can help users interact with data and perform tasks such as checking open customer claims, identifying claim details, and accessing relevant documents. By using Amazon Bedrock knowledge bases, the AI agent can access relevant information (e.g., details about claims, documents, or customer data) in real time, and provide precise, context-driven answers based on the claim-related data stored in the knowledge base. Agents for Amazon Bedrock can help automate interactions by understanding and responding to natural language queries, making it a good fit for this use case. Amazon Bedrock knowledge bases enable the system to access stored information, documents, and details for specific claims efficiently.

21. A manufacturing company uses AI to inspect products and find any damages or defects. Which type of AI application is the company using?
    • A. Recommendation system
    • B. Natural language processing (NLP)
    • C. Computer vision
    • D. Image processing
    Answer

    Correct answer: C

    Explanation: Computer vision is a field of AI that enables machines to interpret and understand visual information from the world, such as images and videos. In the case of the manufacturing company, the AI is used to inspect products for damages or defects, which involves analyzing visual data (e.g., product images or videos). This is a classic application of computer vision, where the AI system identifies and classifies objects or defects within images.

22. A company wants to create an ML model to predict customer satisfaction. The company needs fully automated model tuning. IT meets these requirements? Which AWS service
    • A. Amazon Personalize
    • B. Amazon SageMaker
    • C. Amazon Athena
    • D. Amazon Comprehend
    Answer

    Correct answer: B

    Explanation: Amazon SageMaker provides a fully managed environment for building, training, and deploying machine learning models, including automatic model tuning. Specifically, SageMaker includes a feature called Automatic Model Tuning (or Hyperparameter Optimization), which automates the process of finding the best hyperparameters for your machine learning model. This is essential when you want to optimize the model’s performance without manual intervention. Amazon SageMaker allows you to automate the training process and hyperparameter tuning, which aligns perfectly with the company’s need for fully automated model tuning.

23. Which technique can a company use to lower bias and toxicity in generative AI applications during the postprocessing ML lifecycle?
    • A. Human-in-the-loop
    • B. Data augmentation
    • C. Feature engineering
    • D. Adversarial training
    Answer

    Correct answer: A

    Explanation: Human-in-the-loop (HITL) is a technique where human oversight is involved in the decision-making process of AI systems. In the context of generative AI applications, HITL can be used during the postprocessing phase to identify and mitigate biases or toxic outputs. Humans can review and intervene when the model generates inappropriate or biased content, providing corrections or adjustments that help reduce the likelihood of toxicity and bias. This feedback loop helps refine and improve the model’s outputs over time.

24. A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics. How should the bank fix this issue MOST cost-effectively?
    • A. Include more diverse training data. Fine-tune the model again by using the new data.
    • B. Use Retrieval Augmented Generation (RAG) with the fine-tuned model.
    • C. Use AWS Trusted Advisor checks to eliminate bias.
    • D. Pre-train a new LLM with more diverse training data.
    Answer

    Correct answer: A

    Explanation: The issue described is a case of bias in the model’s decision-making process, where the model is showing a preference for a specific demographic. The most cost-effective way to address this is to include more diverse training data that better represents all demographics. By fine-tuning the model with this more diverse data, you can help ensure that the model treats all demographic groups fairly and does not exhibit biased behavior. Fine-tuning the model with updated data ensures that the model learns from a more representative sample, improving fairness in its predictions without needing to completely retrain the model from scratch.

25. A company has developed a large language model (LLM) and wants to make the LLM available to multiple internal teams. The company needs to select the appropriate inference mode for each team.
    Select the correct inference mode from the following list for each use case. Each inference mode should be selected one or more times.
    • The company’s chatbot needs predictions from the LLM to understand users’ intent with minimal latency: (Batch transform or Real-time inference)
    • A data processing job needs to query the LLM to process gigabytes of text files on weekends: (Batch transform or Real-time inference)
    • The company’s engineering team needs to create an API that can process small pieces of text content and provide low-latency predictions: (Batch transform or Real-time inference)
    Answer

    Explanation: The company’s chatbot needs predictions from the LLM to understand users’ intent with minimal latency - Real-time inference This scenario requires Real-time inference, as chatbots typically need immediate responses with low latency to provide an interactive user experience. A data processing job needs to query the LLM to process gigabytes of text files on weekends Batch transform This use case is best suited for Batch transform. Since the data processing job involves handling large amounts of data, and it is scheduled for weekends, batch processing can handle these large volumes efficiently. The company’s engineering team needs to create an API that can process small pieces of text content and provide low-latency predictions - Real-time inference Real-time inference is the correct choice here, as the API needs to process text quickly and provide immediate responses, making real-time inference appropriate.

26. A company needs to log all requests made to its Amazon Bedrock API. The company must retain the logs securely for 5 years at the lowest possible cost. Which combination of AWS service and storage class meets these requirements? (Choose 2)
    • A. AWS CloudTrail
    • B. Amazon CloudWatch
    • C. AWS Audit Manager
    • D. Amazon S3 Intelligent-Tiering
    • E. Amazon S3 Standard
    Answer

    Correct answer: A, D

    Explanation: AWS CloudTrail is the AWS service designed for logging and monitoring API calls made to AWS services, including Amazon Bedrock. CloudTrail records detailed information about the API requests, including the identity of the requester, the time of the request, and the source IP address. This service is ideal for logging all requests made to the Amazon Bedrock API and meets the logging requirement. Amazon S3 Intelligent-Tiering is a storage class designed for storing data that has unpredictable access patterns. It automatically moves data between two access tiers (frequent and infrequent) based on usage, which helps reduce costs while ensuring data is still available when needed. For retaining logs securely over 5 years at the lowest possible cost, this storage class provides an efficient way to handle long-term storage requirements without incurring unnecessary costs.

27. An ecommerce company wants to improve search engine recommendations by customizing the results for each user of the company’s ecommerce platform. Which AWS service meets these requirements?
    • A. Amazon Personalize
    • B. Amazon Kendra
    • C. Amazon Rekognition
    • D. Amazon Transcribe
    Answer

    Correct answer: A

    Explanation: Amazon Personalize is an AWS service specifically designed to build and deploy personalized recommendations for users. It allows you to create custom machine learning models for personalized user experiences, such as product recommendations, search results, or content suggestions. For an ecommerce company aiming to improve search engine recommendations tailored to each user, Amazon Personalize is the ideal choice as it leverages user behavior data and machine learning to deliver highly relevant recommendations.

28. A hospital is developing an AI system to assist doctors in diagnosing diseases based on patient records and medical images. To comply with regulations, the sensitive patient data must not leave the country the data is located in. Which data governance strategy will ensure compliance and protect patient privacy?
    • A. Data residency
    • B. Data quality
    • C. Data discoverability
    • D. Data enrichment
    Answer

    Correct answer: A

    Explanation: Data residency refers to the practice of ensuring that data is stored and processed within a specific geographical location to comply with regulations or policies. In this scenario, where sensitive patient data must not leave the country it originates from, implementing a data residency strategy ensures compliance with legal and regulatory requirements while protecting patient privacy. This approach is crucial for organizations like hospitals operating under strict data governance frameworks such as HIPAA or GDPR.

29. A company needs to monitor the performance of its ML systems by using a highly scalable AWS service. Which AWS service meets these requirements?
    • A. Amazon CloudWatch
    • B. AWS CloudTrail
    • C. AWS Trusted Advisor
    • D. AWS Config
    Answer

    Correct answer: A

    Explanation: Amazon CloudWatch is a highly scalable AWS service designed for monitoring and observability. It provides real-time monitoring of system metrics, including performance data for ML systems, such as resource utilization (CPU, memory, etc.), model inference latency, request counts, and errors. CloudWatch enables users to set up alarms, visualize metrics, and automate actions based on performance thresholds, making it ideal for monitoring the performance of ML systems.

30. An AI practitioner is developing a prompt for an Amazon Titan model. The model is hosted on Amazon Bedrock. The AI practitioner is using the model to solve numerical reasoning challenges. The AI practitioner adds the following phrase to the end of the prompt: “Ask the model to show its work by explaining its reasoning step by step.” Which prompt engineering technique is the AI practitioner using?
    • A. Chain-of-thought prompting
    • B. Prompt injection
    • C. Few-shot prompting
    • D. Prompt templating
    Answer

    Correct answer: A

    Explanation: Chain-of-thought prompting is a technique in prompt engineering where the AI model is encouraged to break down its reasoning process step by step to solve a problem, such as numerical reasoning challenges. By explicitly instructing the model to “show its work by explaining its reasoning step by step,” the practitioner ensures the model provides a logical sequence of intermediate steps leading to the solution. This improves the accuracy and transparency of the model’s outputs, particularly for complex reasoning tasks.

31. Which AWS service makes foundation models (FMs) available to help users build and scale generative AI applications?
    • A. Amazon Q Developer
    • B. Amazon Bedrock
    • C. Amazon Kendra
    • D. Amazon Comprehend
    Answer

    Correct answer: B

    Explanation: Amazon Bedrock is an AWS service that allows users to build and scale generative AI applications using foundation models (FMs) without the need to manage infrastructure. It provides access to pre-trained models from AWS and third-party providers, enabling developers to integrate generative AI capabilities like text generation, summarization, and question-answering into their applications. This makes Amazon Bedrock the ideal choice for building and scaling generative AI solutions.

32. A company is building a mobile app for users who have a visual impairment. The app must be able to hear what users say and provide voice responses. Which solution will meet these requirements?
    • A. Use a deep learning neural network to perform speech recognition.
    • B. Build ML models to search for patterns in numeric data.
    • C. Use generative AI summarization to generate human-like text.
    • D. Build custom models for image classification and recognition.
    Answer

    Correct answer: A

    Explanation: To meet the requirements of enabling the app to “hear what users say and provide voice responses,” the solution must include speech recognition and text-to-speech capabilities. Using a deep learning neural network for speech recognition allows the app to convert spoken words into text. Once the input is understood, text-to-speech systems can provide voice responses back to users. This approach is fundamental in applications that assist users with visual impairments by enabling interaction through spoken language.

33. A company wants to enhance response quality for a large language model (LLM) for complex problemsolving tasks. The tasks require detailed reasoning and a step-by-step explanation process. Which prompt engineering technique meets these requirements?
    • A. Few-shot prompting
    • B. Zero-shot prompting
    • C. Directional stimulus prompting
    • D. Chain-of-thought prompting
    Answer

    Correct answer: D

    Explanation: Chain-of-thought prompting is specifically designed to enhance the reasoning capabilities of a large language model (LLM) by encouraging it to provide step-by-step explanations for complex problem-solving tasks. This approach helps the model break down a problem into smaller, logical steps, ensuring that the response is detailed, accurate, and easy to follow.

34. A company wants to keep its foundation model (FM) relevant by using the most recent data. The company wants to implement a model training strategy that includes regular updates to the FM. Which solution meets these requirements?
    • A. Batch learning
    • B. Continuous pre-training
    • C. Static training
    • D. Latent training
    Answer

    Correct answer: B

    Explanation: Continuous pre-training keeps a foundation model “always learning” by periodically (or even continuously) ingesting the latest data and updating its weights. This approach ensures the model stays current without having to start training from scratch every time new data arrives, unlike static or strictly batch-oriented methods.

35. A company wants to develop ML applications to improve business operations and efficiency. Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times.

    • Binary classification: (Supervised learning or Unsupervised learning)
    • Multi-class classification: (Supervised learning or Unsupervised learning)
    • K-means clustering: (Supervised learning or Unsupervised learning)
    • Dimensionality reduction: (Supervised learning or Unsupervised learning)
    Answer

    Explanation: Binary classification: Supervised learning Binary classification involves predicting one of two possible outcomes, requiring labeled training data. Multi-class classification: Supervised learning Multi-class classification extends binary classification to predict one of several classes, also requiring labeled data. K-means clustering: Unsupervised learning K-means clustering is used to group data points into clusters without requiring labeled data. Dimensionality reduction: Unsupervised learning Dimensionality reduction techniques, like PCA, reduce the number of features in the dataset without needing labeled data.

36. Which option is a characteristic of AI governance frameworks for building trust and deploying humancentered AI technologies?
    • A. Expanding initiatives across business units to create long-term business value
    • B. Ensuring alignment with business standards, revenue goals, and stakeholder expectations
    • C. Overcoming challenges to drive business transformation and growth
    • D. Developing policies and guidelines for data, transparency, responsible AI, and compliance
    Answer

    Correct answer: D

    Explanation: AI governance frameworks focus on building trust and ensuring the responsible deployment of AI technologies. They establish clear policies and guidelines to address critical aspects such as data management, transparency, ethical considerations, responsible AI practices, and regulatory compliance. These frameworks help organizations mitigate risks, promote fairness, and foster public trust in AI systems, making them essential for creating human-centered AI technologies.

37. An ecommerce company is using a generative AI chatbot to respond to customer inquiries. The company wants to measure the financial effect of the chatbot on the company’s operations. Which metric should the company use?
    • A. Number of customer inquiries handled
    • B. Cost of training AI models
    • C. Cost for each customer conversation
    • D. Average handled time (AHT)
    Answer

    Correct answer: C

    Explanation: To measure the financial effect of a generative AI chatbot on the company’s operations, the cost for each customer conversation is the most relevant metric. It directly quantifies the operational expense associated with using the chatbot to handle customer inquiries. By analyzing this metric, the company can evaluate how much it spends per conversation and compare it to the cost of alternative methods (e.g., human agents), providing insight into the chatbot’s financial efficiency and ROI.

38. A company wants to find 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
    Answer

    Correct answer: B

    Explanation: K-means is a clustering algorithm used in unsupervised learning to group data points into clusters based on their similarities. It is well-suited for finding patterns in customer demographics and buying behavior. The algorithm identifies groups (clusters) of customers with similar characteristics, which can then be used for targeted marketing, personalized recommendations, or segmentation.

39. A company’s large language model (LLM) is experiencing hallucinations. How can the company decrease hallucinations?
    • A. Set up Agents for Amazon Bedrock to supervise the model training.
    • B. Use data pre-processing and remove any data that causes hallucinations.
    • C. Decrease the temperature inference parameter for the model.
    • D. Use a foundation model (FM) that is trained to not hallucinate.
    Answer

    Correct answer: C

    Explanation: In the context of large language models (LLMs), the temperature inference parameter controls the randomness of the model’s output. Lowering the temperature reduces randomness and makes the model’s responses more deterministic and focused on the most likely predictions. By decreasing the temperature, the likelihood of hallucinations (when the model generates incorrect or nonsensical information) is reduced, as the model relies more on high-probability outputs rather than exploring less likely possibilities.

40. A company is using a large language model (LLM) on Amazon Bedrock to build a chatbot. The chatbot processes customer support requests. To resolve a request, the customer and the chatbot must interact a few times. Which solution gives the LLM the ability to use content from previous customer messages?
    • A. Turn on model invocation logging to collect messages.
    • B. Add messages to the model prompt.
    • C. Use Amazon Personalize to save conversation history.
    • D. Use Provisioned Throughput for the LLM.
    Answer

    Correct answer: B

    Explanation: To give a large language model (LLM) the ability to use content from previous customer messages in a conversation, the previous messages must be included in the model prompt. This technique is known as prompt engineering and allows the LLM to retain context by incorporating a history of the interaction within the prompt. By appending prior exchanges to the prompt, the model can generate contextually relevant and coherent responses throughout the multi-turn conversation.

41. A company’s employees provide product descriptions and recommendations to customers when customers call the customer service center. These recommendations are based on where the customers are located. The company wants to use foundation models (FMs) to automate this process. Which AWS service meets these requirements?
    • A. Amazon Macie
    • B. Amazon Transcribe
    • C. Amazon Bedrock
    • D. Amazon Textract
    Answer

    Correct answer: C

    Explanation: Amazon Bedrock enables companies to use foundation models (FMs) to build and automate tasks like generating product descriptions and recommendations. It allows the integration of pre-trained FMs into applications without managing infrastructure, making it an ideal choice for automating customer service tasks. With Amazon Bedrock, the company can leverage FMs to generate tailored recommendations based on customer locations, enabling dynamic and efficient customer interactions.

42. A company wants to upload customer service email messages to Amazon S3 to develop a business analysis application. The messages sometimes contain sensitive data. The company wants to receive an alert every time sensitive information is found. Which solution fully automates the sensitive information detection process with the LEAST development effort?
    • A. Configure Amazon Macie to detect sensitive information in the documents that are uploaded to Amazon
    • B. Use Amazon SageMaker endpoints to deploy a large language model (LLM) to redact sensitive data.
    • C. Develop multiple regex patterns to detect sensitive data. Expose the regex patterns on an Amazon
    • D. Ask the customers to avoid sharing sensitive information in their email messages.
    Answer

    Correct answer: A

    Explanation: Amazon Macie is a fully managed data security and privacy service that uses machine learning to discover and protect sensitive data in Amazon S3. It can automatically detect sensitive information, such as personally identifiable information (PII) or financial data, and send alerts when such data is found. This approach minimizes development effort, as it does not require custom regex patterns or model development, and it is specifically designed to handle the scenario described.

43. A company is training its employees on how to structure prompts for foundation models. Select the correct prompt engineering technique from the following list for each prompt template. Each prompt engineering technique should be selected one time.

    Answer

    Correct answer: Chain-of-thought reasoning

    Explanation: “Classify the following text as either sports, politics, or entertainment: [input text].” Zero-shot learning involves providing the model with a task and no prior examples, relying entirely on the model’s pre-trained knowledge to perform the classification. “A [image 1], [image 2], and [image 3] are examples of [target class]. Classify the following image as [target class].” Few-shot learning provides the model with a few examples (image 1, image 2, and image 3) before asking it to classify a new instance, helping it generalize to the task with minimal examples. “[Question.] [Instructions to follow.] Think step by step and walk me through your thinking.” Chain-of-thought reasoning encourages the model to break down its reasoning process step by step, enhancing its ability to solve complex tasks logically.

44. How does top-p (nucleus) sampling differ from traditional temperature-based sampling?
    • A. It samples from the smallest possible token set
    • B. It selectively samples from tokens that sum to probability p
    • C. It always produces deterministic output
    • D. It only works with specific model architectures
    Answer

    Correct answer: B

    Explanation: Top-p (nucleus) sampling:

    • Dynamically selects tokens based on cumulative probability
    • More adaptive than fixed temperature or top-k approaches
    • Balances quality and diversity
    • Helps prevent unlikely token combinations

    Task Reference: This relates to Task Statement 3.1 regarding inference parameters and their effects on model outputs.

45. Which option is a benefit of using Amazon SageMaker Model Cards to document AI models?
    • A. Providing a visually appealing summary of a model’s capabilities.
    • B. Standardizing information about a model’s purpose, performance, and limitations.
    • C. Reducing the overall computational requirements of a model.
    • D. Physically storing models for archival purposes.
    Answer

    Correct answer: B

    Explanation: Amazon SageMaker Model Cards provide a structured way to document key details about AI models, including their intended use, performance metrics, and limitations. This helps organizations maintain transparency, compliance, and governance in AI model development, making it easier to track and manage models over time.

46. What does an F1 score measure in the context of foundation model (FM) performance?
    • A. Model precision and recall
    • B. Model speed in generating responses
    • C. Financial cost of operating the model
    • D. Energy efficiency of the model’s computations
    Answer

    Correct answer: A

    Explanation: The F1 score is a metric used to evaluate the performance of a classification model by considering both precision (the proportion of correctly predicted positive cases out of all predicted positives) and recall (the proportion of correctly predicted positive cases out of all actual positives). It is the harmonic mean of precision and recall, ensuring a balance between them, especially when dealing with imbalanced datasets.

47. A company deployed an AI/ML solution to help customer service agents respond to frequently asked questions. The questions can change over time. The company wants to give customer service agents the ability to ask questions and receive automatically generated answers to common customer questions. Which strategy will meet these requirements MOST cost-effectively?
    • A. Fine-tune the model regularly.
    • B. Train the model by using context data.
    • C. Pre-train and benchmark the model by using context data.
    • D. Use Retrieval Augmented Generation (RAG) with prompt engineering techniques.
    Answer

    Correct answer: D

    Explanation: Retrieval Augmented Generation (RAG) is a cost-effective approach that enhances AI-generated responses by retrieving relevant information from external knowledge sources. Instead of fine-tuning or re-training a model, RAG dynamically pulls the most recent and relevant data at query time. This is particularly useful in scenarios where questions change over time, ensuring that the AI/ML solution provides accurate and up-todate responses without requiring expensive and time-consuming model retraining. Prompt engineering techniques further optimize how the model processes and generates responses, improving accuracy and relevance.

48. A company built an AI-powered resume screening system. The company used a large dataset to train the model. The dataset contained resumes that were not representative of all demographics. Which core dimension of responsible AI does this scenario present?
    • A. Fairness
    • B. Explainability
    • C. Privacy and security
    • D. Transparency
    Answer

    Correct answer: A

    Explanation: The scenario describes a dataset that is not representative of all demographics, which can lead to biased model predictions. This directly relates to fairness, a core dimension of responsible AI that ensures AI systems make unbiased and equitable decisions across different demographic groups. Addressing fairness involves techniques such as balanced dataset curation, bias detection, and mitigation strategies to ensure that the AI system does not discriminate against any group.

49. A global financial company has developed an ML application to analyze stock market data and provide stock market trends. The company wants to continuously monitor the application development phases and to ensure that company policies and industry regulations are followed. Which AWS services will help the company assess compliance requirements? (Choose 2)
    • A. AWS Audit Manager
    • B. AWS Config
    • C. Amazon Inspector
    • D. Amazon CloudWatch
    • E. AWS CloudTrail
    Answer

    Correct answer: A, B

    Explanation: AWS Audit Manager helps organizations continuously assess and audit compliance with industry regulations and internal policies by automating evidence collection and generating audit reports. This is essential for ensuring that the ML application meets regulatory requirements. AWS Config enables continuous monitoring and compliance checks by tracking configuration changes in AWS resources. It helps the company ensure that infrastructure settings align with security policies and industry standards.

50. A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock. Which solution meets these requirements MOST cost-effectively?
    • A. Fine-tune the FM.
    • B. Retrain the FM.
    • C. Train a new FM.
    • D. Use prompt engineering.
    Answer

    Correct answer: D

    Explanation: Using prompt engineering is the most cost-effective way to improve the accuracy of responses from a generative AI application without retraining or fine-tuning the foundation model (FM). Prompt engineering involves carefully designing the input prompts to guide the model toward producing better responses, improving relevance and accuracy.