C1000-185無料問題集「IBM watsonx Generative AI Engineer

質問 1

While working on a fine-tuning project in IBM watsonx, you need to generate synthetic data that mimics the properties of your existing dataset for training purposes. You have two algorithms available: Algorithm A (Kolmogorov-Smirnov Test) and Algorithm B, which uses a different methodology for assessing similarity between original and synthetic data.
After generating the synthetic data using the User Interface, what would be the primary consideration in choosing the correct algorithm to validate that the generated data sufficiently mimics the original data?

（A）Use Algorithm B to compare the entropy of the original and synthetic data distributions, ensuring both data sets have the same level of uncertainty.

（B）Use Algorithm B if you want to focus on minimizing the difference in mean squared error (MSE) between original and synthetic data distributions.

（C）Choose Algorithm A (Kolmogorov-Smirnov Test) if the synthetic data has categorical variables, as this test is specifically designed for discrete distributions.

（D）Choose Algorithm A (Kolmogorov-Smirnov Test) if you want to ensure that the original and synthetic data distributions are identical across their entire range, not just at specific points.

正解：D 解答を投票する

質問 2

You want a generative AI model to summarize a lengthy text in one sentence. You provide the following prompt: "Summarize the following paragraph in one sentence: 'Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn. The field of AI includes everything from speech recognition to problem-solving and robotics.'" No prior examples are given.
What type of prompting is being used, and what are the expectations?

（A）Few-shot prompting, but the model will likely struggle without a few examples of summaries provided.

（B）Zero-shot prompting, but the model may fail without detailed instructions on what aspects of the text to focus on.

（C）Zero-shot prompting, but it requires the addition of a few examples for the model to generate a summary.

（D）Zero-shot prompting, with the expectation that the model can summarize common concepts like AI due to pre-training.

正解：D 解答を投票する

質問 3

In the context of Retrieval-Augmented Generation (RAG) models, embeddings play a crucial role in retrieving relevant documents.
Which of the following best describes the purpose of embeddings in GenAI, particularly within a RAG system?

（A）Embeddings are used to compress large documents into a smaller format, enabling faster storage and retrieval within a RAG system.

（B）Embeddings represent each word in a fixed-length vector based on its semantic meaning, which allows for precise retrieval of documents by directly matching words between the query and the documents.

（C）Embeddings are primarily used to measure the grammatical correctness of the generated responses by checking the sentence structure in the generated text.

（D）Embeddings are dense vector representations of words, phrases, or entire documents that capture semantic relationships, making it easier to find similar content in the knowledge base during retrieval.

正解：D 解答を投票する

質問 4

You are tasked with generating creative text outputs using an AI language model for a marketing campaign. You want to ensure that the responses are diverse and unexpected but still somewhat relevant to the prompt.
Which combination of temperature and random seed should you use to achieve this?

（A）Temperature: 1.0, Random Seed: 0

（B）Temperature: 1.5, Random Seed: 123

（C）Temperature: 0.1, Random Seed: 42

（D）Temperature: 0.7, Random Seed: None

正解：D 解答を投票する

質問 5

You are tasked with generating reproducible and consistent results for a particular GenAI model prompt during development and testing.
Which of the following is the primary model parameter to adjust in order to ensure that identical inputs produce identical outputs every time the model is run?

（A）Max Tokens

（B）Random Seed

（C）Top-p (Nucleus Sampling)

（D）Temperature

正解：B 解答を投票する

質問 6

You are deploying a Generative AI solution for a client who needs to generate customer service emails in multiple languages. The client has provided a dataset of historical customer service emails, and they want to ensure that their generative model consistently produces accurate, contextually appropriate responses across different languages. The client also has concerns about the latency of the model's responses. Based on these requirements, you are tasked with planning the deployment of the generative AI solution.
Which deployment strategy would be most appropriate for this client's needs, considering latency, language handling, and response quality?

（A）Use a fine-tuned model per language, with nucleus sampling and run them on a single GPU instance to reduce the infrastructure cost.

（B）Deploy a single multilingual model with tuned prompts for each language, using top-k sampling and leveraging a multi-GPU distributed environment to balance response time and quality.

（C）Deploy a tuned prompt for each language on separate models, using greedy decoding to minimize latency, and scale across multiple GPUs for each language.

（D）Deploy a single multilingual model using beam search decoding, and run the model on a single GPU cluster to ensure consistent responses.

正解：B 解答を投票する

質問 7

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?

（A）Set top-p to 0.95

（B）Set top-p to 0.0

（C）Set top-p to 1.0

（D）Set top-p to 0.5

正解：A 解答を投票する

質問 8

A machine learning engineer is optimizing a generative AI model for creative writing. They are debating the use of soft prompts over hard prompts.
What is the primary advantage of using soft prompts in this context, despite their complexity?

（A）Soft prompts provide more direct control over the model's behavior by offering explicit, human-readable instructions.

（B）Soft prompts improve the simplicity of the model's overall structure, making it easier to debug and interpret the generation process.

（C）Soft prompts enforce stricter generation outputs due to the deterministic nature of the learned embeddings, leading to higher consistency in results.

（D）Soft prompts allow for more nuanced control of the model's behavior through learned embeddings, which can adapt to a variety of tasks without requiring explicit human intervention.

正解：D 解答を投票する

質問 9

You are tasked with managing multiple versions of a generative AI model in a production environment. The team has decided to utilize IBM watsonx deployment spaces to version control both models and prompts.
What is the most efficient way to organize these assets within the deployment space?

（A）Assign version tags to both models and prompts within a single deployment space, using metadata and associations to track prompt-model pairings for each version.

（B）Store all versions of both models and prompts in a single deployment space, and use comments to track differences between versions.

（C）Use a third-party version control tool like Git to manage prompt and model versions, syncing with the deployment space only for final deployment.

（D）Create separate deployment spaces for each model and prompt version, linking the spaces manually to ensure consistency between model and prompt versions.

正解：A 解答を投票する

質問 10

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?

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

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

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

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

正解：B 解答を投票する

質問 11

When leveraging existing data for fine-tuning an LLM in IBM watsonx, you want to optimize the model for a highly specialized domain. You also want to generate additional synthetic data to augment your dataset.
Which of the following approaches would best help you achieve your goal?

（A）Relying exclusively on pre-trained general models without making domain-specific modifications

（B）Using the watsonx UI to generate synthetic data that mirrors your existing dataset, filling any data gaps

（C）Manually crafting complex datasets by sampling individual instances from unrelated domains

（D）Using unsupervised learning on your existing dataset without adding synthetic data

正解：B 解答を投票する

質問 12

A business is implementing a RAG solution to enhance its chatbot capabilities. The chatbot needs to answer queries using a large collection of unstructured documents.
Which scenario best highlights when to use a vector database to augment this system?

（A）When you need to provide answers based on the keywords present in the documents, as vector databases are designed for keyword-based retrieval.

（B）When working with large amounts of unstructured text data, to enable semantic search through embeddings that represent the meaning of documents.

（C）When performing traditional database operations like sorting and filtering based on numeric or categorical values.

（D）When storing highly structured relational data, as a vector database excels at managing tabular information efficiently.

正解：B 解答を投票する

質問 13

A team is implementing a Retrieval-Augmented Generation (RAG) system for document search and retrieval. Their goal is to enable users to retrieve contextually relevant documents from a large, unstructured text corpus. They are considering using a vector database to handle this task.
In which scenario is a vector database the most appropriate choice for storing and retrieving documents?

（A）When all documents are structured and can be queried using traditional SQL queries, focusing on specific fields and categories.

（B）When documents need to be retrieved based on semantic similarity to the user's query, even if the exact terms are not matched.

（C）When exact match search is required, such as finding documents containing specific keywords or phrases.

（D）When the system must support real-time updates and frequent data modifications, ensuring that query results always reflect the latest state of the data.

正解：B 解答を投票する

質問 14

When fine-tuning a model in Tuning Studio, which of the following is a key advantage of this tool in reducing resource costs while improving model performance?

（A）It automatically increases the number of layers for more complex tasks.

（B）It optimizes the model for multilingual capabilities by adding new embeddings.

（C）It expands the model's architecture to handle larger datasets.

（D）It allows incremental training, saving computational resources by reusing checkpoints.

正解：D 解答を投票する

質問 15

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?

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

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

（C）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.

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

正解：D 解答を投票する

質問 16

You are developing a natural language generation (NLG) system for financial report summaries. The system needs to generate concise, high-quality summaries for different financial instruments. Tuning Studio is available as a tool to improve the performance of the model.
Which of the following capabilities of Tuning Studio is most helpful for improving the NLG system's performance in this domain-specific application?

（A）It enables the fine-tuning of pre-trained models on custom datasets to improve accuracy and relevance to the specific domain.

（B）It allows you to visually inspect and analyze the model's token-by-token generation process.

（C）It provides real-time feedback on model performance during text generation.

（D）It helps in selecting the most relevant datasets from a pre-existing library for fine-tuning the model.

正解：A 解答を投票する

質問 17

You are tasked with optimizing a large language model (LLM) for deployment in a resource-constrained environment where memory usage and computational cost need to be minimized without significantly compromising model accuracy.
Which quantization technique would be the most appropriate to achieve this balance?

（A）Post-training dynamic quantization

（B）Full integer quantization with 8-bit precision

（C）Mixed-precision floating-point quantization

（D）Post-training weight clustering

正解：A 解答を投票する

C1000-185 無料問題集「IBM watsonx Generative AI Engineer - Associate」

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