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1. In the context of IBM Watsonx and generative AI models, you are tasked with designing a model that needs to classify customer support tickets into different categories. You decide to experiment with both zero-shot and few-shot prompting techniques.

Which of the following best explains the key difference between zero-shot and few-shot prompting?

Zero-shot prompting does not use any examples in the input prompt, while few-shot prompting includes a few examples to guide the model.

Zero-shot prompting provides the model with a few example tasks to help it understand the problem, while few-shot prompting provides no examples at all.

In zero-shot prompting, the model learns from a large number of examples during the inference stage, while in few-shot prompting, only a single example is used.

Few-shot prompting is used only for training the model, while zero-shot prompting is used only for inference tasks.

2. In prompt engineering, prompt variables are used to make your prompts more dynamic and reusable.

Which of the following statements best describes a key benefit of using prompt variables in IBM Watsonx Generative AI?

Prompt variables eliminate the need to change model parameters every time you generate a new response.

Prompt variables automatically improve the accuracy of responses by reducing model variance.

Prompt variables ensure that the AI's response format will always be consistent, regardless of the input data.

Prompt variables allow a single prompt template to handle multiple data points or scenarios by inserting different values.

3. You are working on a project where the AI model needs to generate personalized customer support responses based on various input fields like customer name, issue type, and product details. To make the system scalable and flexible, you decide to use prompt variables in your implementation.

Which of the following statements accurately describe the benefits of using prompt variables in this scenario? (Select two)

Prompt variables improve the model's performance by optimizing its internal architecture, reducing computation time for each request.

Prompt variables reduce redundancy by allowing dynamic inputs to be injected into a single prompt template, improving scalability.

Using prompt variables allows the model to dynamically adjust its output based on context, without requiring multiple task-specific prompts.

Prompt variables eliminate the need for fine-tuning the model on specific tasks since they allow on-the-fly customization of responses.

Prompt variables require a complete re-training of the model whenever a new variable is introduced, which can be time-consuming.

4. You are tasked with designing an AI prompt to extract specific data from unstructured text. You decide to use either a zero-shot or a few-shot prompting technique with an IBM Watsonx model.

Which of the following statements best describes the key difference between zero-shot and few-shot prompting?

Zero-shot prompting provides the model with examples, while few-shot prompting does not.

Zero-shot prompting requires no examples in the prompt, while few-shot prompting provides the model with one or more examples to clarify the task.

Few-shot prompting is used when the model is trained on supervised learning, while zero-shot prompting works only with unsupervised models.

Zero-shot prompting requires retraining the model with additional data, while few-shot prompting uses a pre-trained model without retraining.

5. You are building a chatbot using a generative AI model for a medical advice platform. During testing, you notice that the model occasionally generates medical information that contradicts established guidelines. This is an example of a model hallucination.

Which prompt engineering technique would best mitigate the risk of hallucination in this scenario?

Implementing zero-shot learning techniques

Providing a list of credible sources in the prompt

Using more open-ended prompts

Increasing the model's temperature parameter

6. Your team has developed an AI model that generates automated legal documents based on user inputs. The client, a large law firm, wants to deploy this model but has stringent security, compliance, and auditability requirements due to the sensitive nature of the data.

What is the most appropriate deployment strategy to meet these specific requirements?

Deploy the model on a hybrid cloud, with inference done on the client’s on-premise servers and training done in the public cloud.

Deploy the model on a public cloud with built-in encryption and use APIs to connect to the client’s private data.

Deploy the model using a serverless architecture to minimize operational overhead and maintain compliance.

Use a private cloud with role-based access controls (RBAC) and ensure model activity is logged for auditing purposes.

7. Your team is responsible for deploying a generative AI system that will interact with customers through automated chatbots. To improve the quality and consistency of responses across different queries and customer profiles, the team has developed several prompt templates. These templates aim to standardize input to the model, ensuring that outputs are aligned with business objectives. However, the team is debating whether using these prompt templates will provide tangible benefits in the deployment.

What is the primary benefit of deploying prompt templates in this AI system?

Reducing the overall inference time by streamlining the input-output process for the model, ensuring faster responses.

Improving the scalability of the system by allowing the model to handle more diverse inputs without requiring additional fine-tuning.

Enhancing the model’s ability to generalize across unseen data by training it specifically on the variations included in the prompt template.

Enabling more predictable and consistent outputs across different inputs, aligning the model's responses more closely with the business goals.

8. You have applied a set of prompt tuning parameters to a language model and collected the following statistics: ROUGE-L score, BLEU score, and memory utilization.

Based on these metrics, how would you prioritize further optimizations to balance the model’s performance in terms of output relevance and resource efficiency?

Maximize BLEU score and reduce memory utilization

Reduce memory utilization and maintain BLEU and ROUGE-L scores

Focus on improving the ROUGE-L score while increasing memory utilization

Increase memory utilization to reduce BLEU and ROUGE-L scores

9. You are working on a Retrieval-Augmented Generation (RAG) system to enhance the performance of a

generative model. The RAG model needs to leverage a document corpus to generate answers to complex questions.

Which of the following steps is critical in the RAG pipeline to ensure accurate and relevant answer generation?

Fine-tuning the generative model on the entire document corpus without retrieval components.

Retrieving only the longest document in the corpus as the generative model can synthesize information more effectively from detailed content.

Indexing the document corpus using embeddings, retrieving relevant documents, and feeding them as context into the generative model.

Using keyword-based search to retrieve documents and then allowing the generative model to synthesize answers from those documents.

10. You are tasked with designing a prompt to fine-tune an IBM Watsonx model to summarize legal documents. The summaries must include only factual information, highlight key legal terms, and exclude any personal interpretations or subjective analysis.

Which of the following is the best prompt to achieve this goal?

"Generate a detailed and engaging summary of this legal document, adding your insights to clarify complex legal points for the reader."

"Provide a summary of this legal document, focusing on factual information, including key legal terms and avoiding personal interpretation or subjective analysis."

"Create a brief summary of this legal document, ensuring to exclude any legal jargon and simplifying the content for a layperson audience."

"Summarize this legal document, focusing on key arguments and providing an analysis of the potential outcomes of the case."

11. When deploying AI assets in a deployment space, what is the most critical benefit of using deployment spaces in a large-scale enterprise environment?

Faster training times due to streamlined compute resources

Better data labeling quality through automated labeling tools

Improved model accuracy through hyperparameter tuning

Isolated environments to manage and monitor multiple model versions

12. When generating data for prompt tuning in IBM watsonx, which of the following is the most effective method for ensuring that the model can generalize well to a variety of tasks?

Use a diverse set of prompts covering multiple task domains with varying levels of complexity.

Prioritize prompts with repetitive patterns to help the model memorize key responses.

Focus on generating prompts specific to a single domain to train the model on specialized tasks.

Generate a single highly-detailed prompt that covers all potential use cases to maximize generalization.

13. You are using IBM watsonx Prompt Lab to experiment with different versions of a prompt to generate accurate and creative responses for a customer support chatbot.

Which of the following best describes a key benefit of using Prompt Lab in the process of prompt engineering?

It provides a real-time environment for testing and refining prompts, helping to improve response quality.

It limits the number of iterations a user can test to prevent overfitting the prompt to specific outputs.

It allows users to generate AI models without the need for training data.

It automatically generates prompts based on industry-specific data without any user input.

14. You are working on enhancing the search functionality in a customer service chatbot by implementing the Retrieval-Augmented Generation (RAG) pattern. The chatbot needs to answer customer queries about various technical issues by retrieving relevant information from a knowledge base. Your team is discussing different ways to structure the RAG system and how to implement the pattern efficiently using existing tools.

Which of the following statements best describes the RAG pattern, and how it should be implemented in the context of this chatbot?

The RAG pattern combines dense retrieval with a vector store, where retrieved documents are directly presented as final answers.

The RAG pattern integrates sparse retrieval with a rule-based system for generating responses based on exact document matches.

The RAG pattern prioritizes generating answers based on the frequency of document appearances in the retrieval phase, improving precision.

The RAG pattern enhances a generative model by retrieving relevant documents, which are then used as context for generating a final response.

15. A team is using IBM InstructLab to customize a large language model (LLM) to automate responses in a healthcare chatbot application. The team wants to ensure the chatbot can handle user queries accurately, based on domain-specific instructions.

Which of the following correctly describes the role of the instruction optimization phase within the InstructLab workflow?

Instruction optimization involves retraining the model on a larger dataset for better accuracy.

Instruction optimization focuses on improving the dataset's quality by removing outliers and noise.

Instruction optimization refines prompts to improve the model's ability to follow task-specific instructions.

16. You are working on optimizing a generative AI model that will handle large-scale text generation tasks. The current model is slow during inference, and you need to improve its performance without increasing operational costs. You decide to use IBM Tuning Studio for optimization.

Which of the following is the most significant benefit of using Tuning Studio in this scenario?

It pre-loads commonly used datasets, reducing the need for data handling during the training process.

It provides guidance on reducing the number of parameters in the model to improve inference speed.

It optimizes hyperparameters such as learning rate and batch size to reduce computational overhead during inference.

It automatically scales the model up or down depending on the input data size.

17. Your company is working on deploying a Watsonx Generative AI model for a client, and you have been asked to define the roles involved in the deployment process.

Which of the following roles is responsible for ensuring that the model is properly integrated into the client’s existing systems and that data pipelines are established for continuous model improvement?

DevOps Engineer

Data Scientist

Data Engineer

Solution Architect

18. You are working on a Retrieval-Augmented Generation (RAG) system where large-scale document retrieval is a critical component. To improve the efficiency and accuracy of retrieval, you need to store and query vector embeddings. Given that the system needs to handle billions of high-dimensional embeddings while maintaining low latency for search queries, you are evaluating the use of a vector database.

Which of the following databases would be the most appropriate choice for this purpose, and why?

A document-based NoSQL database like MongoDB, utilizing full-text search capabilities.

A graph database like Neo4j, which is designed for traversing relationships between data points.

A vector database like Pinecone or Weaviate that supports approximate nearest neighbor (ANN) search.

Relational databases with B-tree indexes.

19. You are working on a project that involves deploying a series of prompt templates for a large language model on the IBM Watsonx platform. The team has requested a system that supports prompt versioning so that updates to the prompts can be tracked and tested over time.

Which of the following is the most important consideration when planning prompt versioning for deployment?

Prompts should be stored in a proprietary IBM format, as other formats are not compatible with the Watsonx platform when using versioning.

The versioning system should automatically downgrade to the previous prompt version if the model returns a confidence score below a certain threshold during inference.

Version control should focus exclusively on the syntactical structure of the prompts, as changes to prompt content rarely impact the model’s performance.

Each version of the prompt must have a unique identifier that can be referenced during model inference, to avoid conflicting results from different prompt versions.

20. You are designing a generative AI model to generate customer support responses. During testing, you notice that the model frequently outputs gendered language when referring to certain professions, reinforcing stereotypes.

Which of the following strategies would most effectively reduce bias in the model’s responses?

Increase the diversity of the dataset used to train the model, ensuring that all professions are equally represented.

Reduce the maximum token limit so that the model generates shorter responses, minimizing the chance for bias.

Train the model with a lower learning rate to make it less sensitive to biased patterns in the data.

Apply a post-processing filter that removes any gendered language after the model generates the response.

21. While customizing an LLM in InstructLab to generate more human-like responses for a customer service chatbot, you notice that the responses are too formal and lack empathy.

Which of the following techniques will best address this problem and help tailor the model to generate more empathetic responses?

Use prompt engineering to guide the model towards empathetic responses

Change the decoder strategy from greedy decoding to beam search to increase response quality

Apply transfer learning with a dataset containing casual language

Adjust the model’s max sequence length to encourage longer responses

22. You are tasked with designing a prompt to translate a sentence from English to French using an AI model.

Which of the following prompt would best guide the AI to achieve accurate translation, while maintaining cultural nuance and avoiding literal word-for-word translation?

"Translate 'The weather is nice today' to French but ensure that the translation reflects word-for-word accuracy and no cultural considerations."

"Explain the meaning of 'The weather is nice today' in French."

"Translate the sentence 'The weather is nice today' into French and make sure to avoid literal translation, focusing on cultural nuances."

"Translate the following sentence from English to French: 'The weather is nice today.'"

23. You are tasked with generating synthetic data for a fine-tuning task on an IBM watsonx model. The goal is to mimic the distribution of existing training data while ensuring the synthetic data maintains its statistical similarity to the original. You are provided with two algorithms, Algorithm A (Kolmogorov-Smirnov Test) and Algorithm B, to assess the similarity between the original and synthetic data distributions.

Which of the following best describes how you should implement synthetic data generation using the User Interface and choose the correct algorithm?

Use Algorithm A (Kolmogorov-Smirnov Test) to compare the original and synthetic data distributions, checking for deviations across the entire data range.

Use Algorithm A (Kolmogorov-Smirnov Test) to match the covariance matrix of the original and synthetic data distributions, ensuring high correlation between data points.

Use the User Interface to generate synthetic data and validate it using Algorithm A, which compares the distributions' mean values to ensure close alignment.

Use the User Interface to generate synthetic data and validate it using Algorithm B, which assesses the overall shape of the distributions but does not provide a significance test for statistical similarity.

24. You are building a generative AI model to assist with customer service responses. During evaluation, you notice that the responses generated tend to favor one specific demographic group, showing bias toward certain dialects and cultural references.

How should you adjust the prompt and model parameters to reduce this bias?

Use a prompt that explicitly asks for neutrality across demographic groups.

Incorporate additional training data from underrepresented demographic groups.

Switch to using deterministic (greedy) decoding to ensure more consistent outputs

Lower the temperature to reduce randomness in the model's response.

25. In IBM Watsonx's Prompt Lab, you are refining a prompt to improve the clarity and relevance of the AI's responses. You need to understand which prompt editing options are available to optimize your results.

Which of the following is NOT an available prompt editing option?

Adjusting the context window to include or exclude specific sections of input text.

Setting conditions within the prompt to handle different scenarios based on detected input patterns.

Using tone adjustments to modify the emotional tone or style of the AI's responses.

Adding dynamic variables to the prompt, allowing for flexible and context-specific responses.

26. You are developing a generative AI application using LangChain, and you want the system to perform actions like searching a database or retrieving live web content based on a user’s request.

How can you best incorporate tools in LangChain to enable the AI to perform such tasks autonomously?

Rely on LangChain’s memory module to remember previous user queries and provide real-time data access.

Build a LangChain chain that uses user inputs to sequentially call all the available tools and pick the one with the most relevant output.

Use a LangChain agent with a predefined set of tools to dynamically select and invoke the appropriate tool (e.g., database access, API call) based on the user’s request.

Configure LangChain to automatically load data from static sources based on historical query patterns, avoiding the need for dynamic tool selection.

27. You are designing a workflow using watsonx.ai to generate complex text summaries from multiple sources. To achieve this, you plan to implement a LangChain-based chain that orchestrates different generative AI tasks: document retrieval, natural language processing (NLP) analysis, and summarization.

What is the best way to structure the LangChain-based chain to ensure that each task is effectively handled and results in an accurate summary?

Start with NLP analysis, pass the data to watsonx.ai for summarization, and then perform document retrieval to verify the accuracy of the summary.

Break the LangChain-based chain into individual steps that allow for manual intervention at each stage, ensuring control over the process at every step.

Use watsonx.ai to generate a summary immediately, and then perform NLP analysis and document retrieval in parallel to verify the accuracy of the output.

Perform document retrieval first, followed by NLP analysis to extract relevant information, and then pass the processed data to watsonx.ai for summarization.

28. You are building a generative AI system that uses synthetic data to mimic an existing dataset. You have learned about two primary algorithms: one that focuses on ensuring the synthetic data passes statistical normality tests and another designed to generate realistic-looking data without focusing on distribution conformity.

Which algorithm should you choose if your primary concern is statistical accuracy and passing the Anderson-Darling test?

Anderson-Darling Based Synthetic Data Generation (ADS-DG)

Gaussian Mixture Models (GMMs)

K-Nearest Neighbors (KNN)

Bootstrapping Algorithm

29. In which of the following scenarios would zero-shot prompting be more effective than few-shot prompting when interacting with a generative AI model?

When the goal is to adjust the model's response based on few labeled examples that help refine its predictions.

When the model is expected to perform a novel task it has never seen, but the prompt can include several examples for guidance.

When the prompt is designed for a general task like summarizing a text, which the model is pre-trained on.

When the task requires highly domain-specific knowledge that the model has not been exposed to before.

30. You are working with IBM Watsonx and need to generate synthetic data to improve your model's performance on a custom domain-specific task. After importing a dataset, you want to use the User Interface to generate this synthetic data.

What is the primary benefit of using synthetic data generation in fine-tuning your model?

It automatically anonymizes sensitive data points to comply with data privacy regulations during the synthetic data generation process.

It improves the model’s generalization by exposing it to a wider variety of data points and scenarios.

It creates a larger training dataset by duplicating and randomizing the existing data, which enhances model accuracy.

It eliminates the need for any human intervention in the fine-tuning process.

31. Which of the following decoding strategies would most likely result in generating creative and diverse text outputs while minimizing repetition, when using a generative AI model?

Greedy Decoding

Beam Search Decoding with a small beam size (e.g., 2)

Nucleus Sampling (Top-p) with p = 0.9

Temperature Sampling with temperature = 0.0

32. You are building a customer support chatbot using IBM watsonx.ai and Watson Assistant. The chatbot must use watsonx.ai’s large language model (LLM) to generate dynamic responses and Watson Assistant to manage dialog and interaction flow.

What is the most efficient way to integrate these two services to deliver an optimal solution?

Deploy watsonx.ai’s LLM within Watson Assistant by embedding the LLM directly into the Watson Assistant environment.

Use Watson Assistant as the primary interface and call watsonx.ai’s LLM through an API for generating dynamic responses in specific intents.

Use Watson Assistant to directly generate all responses, bypassing watsonx.ai’s LL

Build a separate microservice for each service, allowing Watson Assistant and watsonx.ai’s LLM to operate independently, with no communication between them.

33. You are optimizing a prompt-tuned LLM for a financial institution’s automated assistant. The assistant's main tasks include responding to customer inquiries about account balances, providing detailed transaction histories, and explaining complex financial products.

Which task should be prioritized for prompt-tuning to improve the model's performance in this domain?

Focus on improving the model’s ability to generate financial market forecasts.

Train the model to generate creative financial advice tailored to each customer.

Fine-tune the model for information retrieval tasks related to customer accounts.

Optimize for natural language generation to improve customer engagement.

34. In the context of prompt engineering for IBM Watsonx Generative AI, which of the following is the most accurate description of a prompt variable?

A prompt variable is a fixed string that the AI uses to refine its generative process for more context-aware responses.

A prompt variable is a function that allows real-time feedback from the model to modify the prompt after generation.

A prompt variable is a predefined input that alters the architecture of the AI model during runtime.

A prompt variable is a placeholder within a prompt template that can be replaced with specific input values during execution.

35. When designing a generative AI system to minimize the risk of producing hate speech or abusive content, which of the following strategies in prompt engineering is the most effective?

Use offensive words in prompts to test the model's robustness in handling them.

Implement real-time content moderation at the output level rather than relying on input prompts alone.

Use strict prompt filtering to block any input that contains offensive words.

Ensure that the model is trained on a larger dataset, even if the dataset contains diverse viewpoints, including some controversial opinions.

36. You are tasked with optimizing a generative AI model's usage in a chatbot that provides troubleshooting instructions for software issues. The current prompt template is:

"Please provide step-by-step troubleshooting instructions for the following issue: [Issue Description]. Be detailed, include specific commands or settings the user should check, and provide potential reasons for failure."

To reduce the token count and ensure cost efficiency, which of the following prompt template modifications would best manage token usage while preserving essential information?

"Provide detailed troubleshooting instructions for the issue: [Issue Description], with steps, commands, and potential reasons for failure."

"Give troubleshooting instructions for [Issue Description], including steps, commands, and reasons for failure."

"Troubleshoot the following issue: [Issue Description]. Offer step-by-step commands and reasons for failure."

"Provide step-by-step instructions for troubleshooting the issue: [Issue Description]. Include commands and reasons for failure."

37. You are working on generating synthetic training data using IBM InstructLab to supplement a small dataset for a question-answering system.

Which strategy would most effectively enhance the dataset without introducing biases or artifacts?

Use prompts that closely mimic the structure and semantics of the real dataset's questions to maintain consistency.

Automatically generate synthetic data using a different model architecture than the one being fine-tuned.

Manually tweak each generated response to ensure it's free of errors and aligns with the intended task.

Generate a large amount of synthetic data by directly feeding the model with random prompts, ensuring data diversity.

38. When optimizing a prompt-tuned model, which parameter adjustment would most likely help prevent overfitting without negatively impacting the model’s ability to generalize to unseen prompts?

Removing weight decay entirely

Increasing the model’s dropout rate

Reducing the model’s learning rate

Increasing the model’s learning rate

39. You are experimenting with a generative AI model to write a personalized email response template. You want to ensure that the output maintains a formal tone but occasionally produces creative phrasing without making nonsensical sentences. You are advised to adjust the top-p (nucleus sampling) parameter.

Which of the following settings would most effectively balance between formal coherence and occasional creativity in the generated output?

Set top-p to 0.5

Set top-p to 0.95

Set top-p to 1.0

Set top-p to 0.0

40. You are developing a Retrieval-Augmented Generation (RAG) system using IBM WatsonX LLM and a vector database. Your dataset consists of long legal documents, and you want to ensure the system retrieves the most relevant sections of these documents efficiently.

Which of the following best describes the appropriate approach to text chunking for this RAG implementation?

Chunking the documents at arbitrary points, ignoring sentence or paragraph boundaries to enhance retrieval speed.

Splitting the documents into smaller chunks based on logical or semantic breaks such as paragraphs, while maintaining a token count that matches the LLM's context window.

Splitting the legal documents into fixed-size chunks of 10,000 tokens each to maximize retrieval accuracy.

Chunking the documents based solely on page numbers, as legal documents typically follow consistent formatting.

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