C1000-185 Free Dumps (Part 2, Q41-Q80) Are Also Available to Help You Check More About the IBM C1000-185 Dumps (V8.02)

1. You are deploying a large language model in a financial advisory platform to assist users in making investment decisions.

Which of the following represent significant risks that should be mitigated before full deployment? (Select two)

The model occasionally generates offensive or inappropriate content when responding to user queries.

The model generates recommendations that align with historical financial trends but fail to account for recent economic disruptions.

The model is trained on open-source financial data, which results in slower response times during inference.

The model provides longer-than-expected responses, potentially causing user frustration and increasing abandonment rates on the platform.

The model offers speculative advice without indicating the associated level of uncertainty, which may mislead inexperienced investors.

2. You are developing a machine learning pipeline using IBM watsonx that includes fine-tuning an LLM with a dataset containing sensitive personal information. To ensure privacy, you decide to apply differential privacy.

Which of the following actions is most critical to configure in the user interface to meet the differential privacy requirements during model fine-tuning?

Increase the learning rate and batch size to maximize the noise added by differential privacy algorithms.

Remove differential privacy settings for fine-tuning, but apply them in the final inference model to reduce performance degradation.

Apply a differential privacy mechanism that adds calibrated noise to both the model updates and synthetic data generation process.

Use synthetic data only, which eliminates the need for differential privacy as it does not contain real user information.

3. IBM Watsonx Tuning Studio allows users to fine-tune pre-trained models for their specific use cases.

Which of the following correctly describes the primary benefits of using Tuning Studio for optimizing a generative AI model?

It fully retrains the base model from scratch, ensuring the highest possible accuracy for each new task, regardless of prior training.

It allows users to add new architectural layers to the model to improve accuracy without retraining the entire model.

It significantly reduces the computational costs associated with model fine-tuning by only updating the model’s parameters relevant to the specific task, preserving the general knowledge of the base model.

It enables on-the-fly model optimization during inference, adjusting model weights dynamically based on real-time data input.

4. Which of the following best describes the process of large-scale iterative alignment tuning in the context of customizing LLMs with InstructLab?

Repeated fine-tuning of a model using reinforcement learning, focusing on aligning its outputs with human preferences across a diverse set of tasks

Fine-tuning the model exclusively on binary classification tasks to improve its generalization on all other tasks

Direct training of the model on an expanded version of the dataset, without adjusting prompts or training tasks

A single training run of the model on a dataset to generate better predictions for a fixed number of prompts

5. You are tasked with designing prompts for an IBM Watsonx Generative AI model to minimize hallucinations in responses. One of the ways to reduce hallucinations is by improving the quality of the prompt to guide the model more effectively.

Which of the following prompt engineering strategies would be most effective in reducing the likelihood of hallucinations?

Use highly abstract and open-ended prompts to allow the model more freedom in generating responses.

Include explicit instructions and specific constraints within the prompt to limit the scope of the model's generation.

Increase the temperature parameter to introduce more diversity and creativity into the model's output.

Set the minimum token length high to ensure the model has enough time to fully develop its response.

6. You are tasked with generating high-quality responses from a large language model for a customer support application. You want to minimize the amount of provided examples while ensuring that the model generates relevant and specific answers.

Which of the following statements best differentiates between zero-shot and few-shot prompting in this context? (Select two)

In zero-shot prompting, the model's response is generated purely based on pre-trained knowledge and the structure of the task, while in few-shot prompting, the examples provided offer the model additional context.

Zero-shot prompting is better suited for tasks requiring domain-specific knowledge, while few-shot prompting is better for general knowledge tasks.

Few-shot prompting involves fine-tuning the model on a specific dataset before generating output, whereas zero-shot prompting uses pre-trained knowledge without additional fine-tuning.

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Zero-shot prompting does not require any examples in the input prompt, while few-shot prompting uses a limited number of examples to guide the model's response.

Few-shot prompting improves model performance for unfamiliar tasks by fine-tuning weights based on examples, while zero-shot prompting leaves the model weights unchanged.

7. When analyzing the results of a prompt tuning experiment, which two of the following actions are most appropriate if you observe a consistently high variance in model predictions across different prompt templates? (Select two)

Enable regularization techniques like dropout

Increase the batch size during training

Tune the prompt templates further by standardizing the structure

Increase the number of training samples used for tuning

Add more layers to the model to increase complexity

8. You are generating a list of items using IBM watsonx’s generative AI, but you notice that the model sometimes cuts off mid-sentence when using a stop sequence.

What could be the best approach to ensure that the model finishes generating complete sentences while also stopping after a specific sequence is reached?

Increase the token limit to avoid premature cut-off

Set the stop sequence to a punctuation mark like “;”

Use a more distinct and unlikely stop sequence, such as “<END>”

Use multiple stop sequences, including a period “.”

9. In the context of the decoding process for generative AI models in IBM Watsonx, what is the main characteristic of greedy decoding?

Greedy decoding selects the highest probability token at each step, leading to deterministic and often coherent outputs.

Greedy decoding alternates between high and low probability tokens, ensuring a balance between creativity and correctness.

Greedy decoding generates multiple possible sequences and selects the most grammatically correct one based on predefined rules.

Greedy decoding always selects the token with the lowest probability to encourage diversity in the generated response.

10. When debating the drawbacks of soft prompts in a generative AI application, which of the following is the most significant challenge compared to hard prompts?

Soft prompts introduce more complexity during the training phase, as the model must learn embeddings that are not inherently interpretable by humans.

Soft prompts significantly limit the flexibility of the model because they are tied to specific tasks, unlike hard prompts which can generalize to various scenarios.

Soft prompts require more human intervention during generation because they depend on predefined patterns and rules for guidance.

Soft prompts offer simpler debugging processes because the learned embeddings are directly linked to specific model behaviors and outputs.

11. You are tasked with fine-tuning a language model using a prompt-tuning approach on a dataset consisting of customer service chat logs. The goal is to optimize the model's ability to generate polite and

contextually appropriate responses.

Which of the following steps are essential when preparing the dataset for prompt-tuning in this context? (Select two)

Remove any conversations that contain excessive user slang or misspellings.

Separate the dataset into training, validation, and test subsets.

Convert all user queries into lowercase to reduce noise in the dataset.

Ensure each conversation includes both customer input and agent response as context for the model.

Ensure all examples in the dataset follow the exact same input-output format.

12. After tuning a generative AI model to produce more concise legal document summaries, you notice that while the summaries are accurate, they tend to be overly verbose. The tuning report shows that the model’s perplexity is relatively high, suggesting that it is struggling with token prediction uncertainty, possibly due to an overly complex output format.

Which of the following tuning parameters would you most likely adjust to address the verbosity issue without reducing accuracy?

Increase the number of epochs

Decrease the learning rate

Increase the maximum token length

Decrease the temperature

13. While optimizing the cost of running a Generative AI model, you are instructed to adjust the prompt structure.

Which of the following changes to a prompt would most reduce computational costs while still maintaining effective results?

Including multiple tasks in a single prompt to maximize efficiency.

Switching from a narrative-style prompt to a bulleted list format.

Breaking complex prompts into simpler, sequential prompts.

Using stop tokens early in the prompt to minimize generation length.

14. While developing a Retrieval-Augmented Generation (RAG) system using the transformers library, you want to improve the retrieval quality by ensuring that your queries and documents are represented in the same latent space for effective similarity matching.

Which of the following techniques would be the most appropriate to ensure this alignment between queries and documents?

Use a randomly initialized transformer model to encode both documents and queries for unbiased similarity calculation.

Use different transformer models for documents and queries, and normalize their embeddings to align them in the same latent space.

Fine-tune a transformer model on a document-query similarity task, so that both queries and documents are encoded into the same vector space for retrieval.

Use a pre-trained BERT model to encode the documents and a pre-trained GPT model to encode the queries, ensuring diversity in embeddings.

15. You are designing a Retrieval-Augmented Generation (RAG) system that will handle real-time queries from users, using a combination of a retriever and a transformer-based generator.

Which of the following implementation details is the most critical to ensure that the system delivers responses in a timely manner while maintaining accuracy?

The retriever should use exact match algorithms to minimize retrieval time and complexity.

The retriever and generator should operate independently of each other to avoid communication overhead.

The system should cache previous queries and responses to avoid invoking the retriever and generator multiple times.

The retriever should utilize an efficient vector search algorithm with approximate nearest neighbor (ANN) techniques to balance speed and accuracy.

16. You are developing an AI-driven application using IBM watsonx and LangChain to automate legal document summarization for a law firm. The application needs to extract key legal points, summarize them, and generate insights from various sources, including external APIs, court databases, and private document repositories. You are tasked with creating a LangChain chain that integrates these sources, customizes prompt templates, and uses Large Language Models (LLMs) to provide legal summaries. The prompt template must allow for dynamic insertion of text from external sources and adapt based on the type of legal document.

Which LangChain chain design would best meet the needs of this application?

Use a SequentialChain that first extracts text from external APIs and databases, processes it through custom prompt templates, and then sends the final processed text to an LL

Employ a Retrieval-Augmented Generation (RAG) Chain, where the LLM queries external knowledge sources in real-time while applying a fixed prompt template.

Design a ParallelChain where the text from different sources is processed in parallel by multiple LLMs, combining the results at the end.

Implement a SimpleChain that retrieves the required data from external APIs and directly sends the text to the LLM without prompt templates.

17. You are optimizing a large language model (LLM) for deployment on edge devices with limited computational resources.

To reduce the model size and improve efficiency without significantly compromising performance, which of the following quantization techniques is most appropriate for this scenario?

Post-training 8-bit integer quantization

32-bit floating point quantization with fine-tuning

Binary quantization (1-bit)

Post-training 16-bit floating point quantization

18. You are using IBM's Tuning Studio to fine-tune a generative AI model for a custom text classification task. The model was pre-trained on a large corpus but shows suboptimal performance when applied to your domain-specific data. You aim to improve both accuracy and computational efficiency.

Which of the following is a primary benefit of using Tuning Studio to optimize this model?

Tuning Studio allows for the customization of training data at runtime without needing pre-processing.

Tuning Studio helps reduce overfitting by applying regularization techniques during the fine-tuning process.

Tuning Studio provides detailed performance analytics that allow you to adjust hyperparameters in real-time.

Tuning Studio automatically generates prompt templates that can be used for different tasks without further configuration.

19. Which of the following describes a key benefit of using Prompt Lab in IBM Watsonx for developing generative AI applications?

Prompt Lab provides automatic optimization of model hyperparameters, ensuring the best performance without user intervention.

Prompt Lab enables real-time collaboration between developers, allowing them to modify prompts simultaneously for faster development.

Prompt Lab ensures that all generated outputs are free from bias by automatically filtering inappropriate content.

Prompt Lab allows users to iteratively test and refine prompts in a controlled environment, improving prompt effectiveness and reducing guesswork.

20. Which of the following stopping criteria can help in generating coherent and well-structured text without cutting off mid-sentence or continuing unnecessarily?

Stopping when the model generates special end-of-sequence tokens, such as <EOS>

Stopping the model only when it reaches the end of a predefined phrase from the input prompt

Monitoring the likelihood of the next token and stopping when the likelihood drops below a threshold

Stopping the model after a predetermined number of tokens, regardless of context

21. You are fine-tuning a generative model to generate text-based responses in a customer service chatbot. You want to ensure the responses are concise and relevant, without causing the model to produce overly long or irrelevant output.

Which of the following parameters and stopping criteria would be most effective for achieving this goal?

Increase the temperature to 1.5 and set a high maximum token limit.

Use beam search decoding with a low beam width and a high repetition penalty.

Use greedy decoding with no repetition penalty and a high stopping probability threshold.

Set a low top-k value and implement a repetition penalty with a low maximum token limit.

22. Which of the following statements accurately describes a drawback of using soft prompts in generative AI model optimization?

Soft prompts can increase the model’s interpretability by providing clear, user-defined input instructions.

Soft prompts require additional computational resources during training, which can limit their scalability in real-time applications.

Soft prompts offer improved performance for specific tasks but are harder to implement when fine-tuning models across multiple domains.

Soft prompts make it easier to control the model’s behavior as the prompts are flexible and can be adjusted by the user during inference.

23. When setting up a tuning experiment in IBM watsonx's Tuning Studio, which of the following best describes the process for optimizing a model's hyperparameters?

Set the learning rate to its maximum value to speed up the tuning process and reduce experimentation time.

Manually adjust one hyperparameter at a time while keeping all other parameters constant to precisely identify its impact on performance.

All hyperparameters should be fixed at the default settings to ensure consistency across different experiments.

Use automated hyperparameter search techniques such as grid search or random search to explore multiple configurations efficiently.

24. You are tasked with creating a prompt template for IBM Watsonx to generate customer support responses based on user queries. The response needs to be polite, concise, and address the issue directly.

Which of the following is the most appropriate structure for a reusable prompt template to ensure consistency across multiple queries?

"Generate a detailed and formal response to the customer, focusing on providing as much information as possible, even if it’s unrelated to the query."

"Please write a polite and professional response to the customer's query, including any relevant context or background information and focusing on the core issue."

"Generate a professional response to the customer’s query, avoiding repetition and unnecessary details, while focusing on addressing the issue succinctly."

"Write a short and casual response to the customer, focusing on being friendly and engaging, regardless of the content of the query."

25. You are developing a generative AI model using the IBM Watsonx platform to assist in customer service. While the model's responses are highly accurate, there is concern that the model may inadvertently expose personal information (PII) or sensitive data during interactions. As a responsible AI engineer, it is crucial to mitigate this risk.

Which of the following is the most critical risk associated with the exposure of personal information in generative AI models?

The model can unintentionally memorize and regurgitate personal information from the training data, leading to privacy violations.

The model can generate outputs that are too general, failing to meet the specific needs of the user.

The model might produce content that doesn't align with the cultural preferences of the user.

The model can generate overly creative or non-factual responses, leading to brand reputation damage.

26. In the context of Tuning Studio in IBM watsonx, what is one of the key benefits of using Compute Unit Hours (CUHs) during the fine-tuning process?

It provides real-time feedback on model deployment success rates.

It allows for the precise allocation of computational resources to manage budget constraints.

It limits the number of model versions stored, improving system performance.

It reduces the time required to train models by lowering the accuracy threshold.

27. While working with IBM Watsonx to generate synthetic data, you import a sensitive dataset containing personally identifiable information (PII). You are tasked with anonymizing the imported data before proceeding with any fine-tuning or data augmentation.

Which of the following steps is the most appropriate to ensure proper anonymization?

Generate a synthetic version of the dataset that removes all PII automatically.

Apply a differential privacy algorithm that ensures no individual data point can be traced back to a specific user.

Randomly shuffle the sensitive data fields to prevent direct re-identification.

Use a hashing algorithm to replace PII fields while retaining the ability to reverse the process if needed.

28. Condition-based prompts, where specific actions are taken depending on input patterns, are part of advanced prompt design, allowing developers to create more context-aware interactions.

The temperature parameter controls the length of the generated output by increasing or decreasing the model’s word count limit.

The learning rate parameter adjusts the creativity of the model’s outputs by encouraging the model to explore more diverse topics.

The greedy decoding parameter improves output diversity by ensuring that the most likely token is always chosen at each step in the generation process.

The top-k sampling parameter controls how many potential next words are considered during each generation step, limiting the randomness of the output.

29. You are preparing a dataset for fine-tuning a model to classify customer complaints by category. The dataset is imbalanced, with 70% of the data representing complaints about billing, 20% representing complaints about technical issues, and 10% representing complaints about product quality.

Which of the following actions would help address the imbalance while preparing the dataset for fine-tuning? (Select two)

Leave the dataset as-is, trusting the model to handle the imbalance using its internal mechanisms.

Apply class weighting during model training instead of modifying the dataset itself.

Add more synthetic data for the minority classes by using generative techniques like GPT-3 to create realistic complaints.

Oversample the under-represented classes to ensure a balanced distribution across categories.

Undersample the majority class (billing complaints) to balance the dataset.

30. In the context of sampling decoding for IBM Watsonx Generative AI, which of the following statements best describes how top-k sampling works?

Top-k sampling ensures that the next token is chosen only if it matches one of the predefined input variables.

Top-k sampling selects the token with the highest probability, ignoring all other token options.

Top-k sampling automatically filters out low-probability tokens that were not part of the model's training set.

Top-k sampling selects the next token only from the top k most probable tokens based on their probabilities.

31. In a Retrieval-Augmented Generation (RAG) system designed for technical document retrieval, you are tasked with implementing text chunking techniques using the LangChain library. The technical documents are large and contain numerous tables, figures, and bullet points.

What is the most effective way to handle text splitting to ensure high-quality retrieval?

Split the text into equal-sized chunks of 512 characters, regardless of the content structure, to improve consistency in retrieval.

Split the text only at paragraph breaks, ignoring tables and figures, as they can be processed separately.

Convert tables and figures into plain text and split the document by character count to maintain even chunk sizes.

Use a hybrid approach, splitting the text by both semantic boundaries (like paragraphs) and content-specific markers (like bullet points and tables), while keeping chunks within the model's token limit.

32. In the context of Retrieval-Augmented Generation (RAG), embeddings play a crucial role in ensuring relevant information is retrieved to augment the generative AI’s response.

Which of the following best describes the role of embeddings in the RAG process?

Embeddings represent the search space for the retriever model, allowing the system to retrieve semantically relevant information based on input queries.

Embeddings are pre-trained generative models that augment the retrieval step by generating new query variations.

Embeddings are only used in fine-tuning generative models and play no role in the retrieval process.

Embeddings are used to directly generate the textual responses in the output.

33. You are optimizing a generative AI model that writes product descriptions. The cost of using the model is directly related to the number of tokens generated. To minimize token usage, you decide to introduce a stop sequence in your prompt that signals the model to end its generation early when the description reaches a certain length. Given the following prompt:

"Write a product description for [Product Name]. The description should include the main features and benefits of the product in no more than 50 words."

Which of the following stop sequences would be most effective in ensuring the generation is concise and does not exceed the desired word limit?

"

."

"

---"

"

End of description."

"###END###"

34. You are building a customer support chatbot for an e-commerce company using IBM watsonx and LangChain. The chatbot will interact with an external database that holds customer order history, shipping details, and product catalog data. You need to create a LangChain chain that dynamically generates responses using prompt templates tailored to customer queries, retrieves data from the external database, and incorporates LLMs to refine the answers. The goal is to provide accurate, context-aware responses to questions about order status and product details.

Which LangChain strategy will best ensure that the chatbot provides accurate, dynamic responses based on real-time customer data?

Use a RetrievalChain to query the external database and combine the retrieved data with a dynamic prompt template before sending it to an LL

Implement a SimpleChain that directly connects the chatbot to the external database and generates responses from pre-defined LLM outputs.

Apply a MemoryChain that remembers past customer queries and uses this memory to answer future questions more accurately.

Design a ParallelChain where multiple LLMs process different aspects of the customer query, such as order history and product details, combining them in the final answer.

35. You are developing a document understanding system that integrates IBM watsonx.ai and Watson Discovery to extract insights from large sets of documents. The system needs to leverage watsonx.ai’s large language model to summarize documents and Watson Discovery to search and extract relevant data from those documents.

What is the best approach to achieve this integration?

Use watsonx.ai’s LLM to both retrieve and summarize the documents, bypassing Watson Discovery.

Use Watson Discovery for summarizing documents and watsonx.ai’s LLM for only retrieving relevant content from the documents.

Use watsonx.ai’s LLM to create a summary for each document in advance, and Watson Discovery only for searching pre-generated summaries.

Use Watson Discovery to index and search documents, and then send the retrieved documents to watsonx.ai’s LLM for summarization through API calls.

36. You are implementing a few-shot prompting strategy with IBM Watsonx to improve the model's performance in generating customer service responses. The goal is to ensure the model understands the tone and format required for polite and concise replies.

Which of the following strategies best illustrates the correct way to use few-shot prompting?

Provide example prompts with multiple different output styles to give the model a range of responses to choose from.

Provide one or two well-structured examples that demonstrate the expected tone and format of the customer service responses within the prompt.

Include a large number of examples, typically over 10, in the input prompt to ensure the model learns from diverse cases.

Use only negative examples in the prompt to show the model what not to generate in terms of tone and format.

37. In a Retrieval-Augmented Generation (RAG) setup, you notice that the model is generating responses that are not always relevant to the query, despite the knowledge base containing useful information.

What could be the most likely cause of this issue, and how should you address it?

The model is over-relying on the retrieval system and ignoring the language model’s ability to generate coherent responses, so you should disable the retrieval component for general questions.

The knowledge base might contain outdated or irrelevant documents, so removing all non-recent documents would ensure the model generates more relevant responses.

The problem likely lies with the input format, so changing all queries to a pre-structured format (like templates) will ensure the retrieval and generation stages perform optimally.

The retrieval mechanism might be failing to fetch the most relevant documents from the knowledge base, so you should improve the search algorithm or use a better ranking system.

38. You are tasked with creating a prompt-tuned model using IBM watsonx.ai to enhance the quality of text generation for customer support. The goal is to fine-tune the model for improved context understanding based on specific customer queries.

Which of the following approaches would be the best method to initialize the prompt for tuning?

Use a pre-trained general-purpose prompt with no domain-specific customization

Use a manually crafted prompt tailored to the specific context of customer support queries

Construct a prompt using a large set of random tokens from the training corpus

Use a prompt with pre-defined output patterns to restrict the model's possible responses

39. Which of the following is a key component of IBM’s InstructLab framework for customizing large language models (LLMs)?

Prompt engineering module designed to automatically generate synthetic training data for prompt-tuned models

Tools to iteratively optimize the model’s alignment with human preferences, such as reinforcement learning from human feedback (RLHF)

A tokenization algorithm designed to reduce model size by removing unused tokens

A fine-tuning mechanism based on few-shot learning that only updates the model's output layer

40. After prompt-tuning a language model, you notice that certain outputs are semantically correct but syntactically flawed.

Which of the following actions is most appropriate to resolve this issue and optimize the tuned model's performance?

Fine-tune the prompt template to emphasize grammar

Lower the learning rate during the tuning phase

Increase the model's training dataset size

Use a higher temperature during the generation process

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