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

質問 1

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' responses?

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

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

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

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

正解：C 解答を投票する

質問 2

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 3

You are building a RAG system in IBM watsonx for a legal document retrieval platform. The platform deals with highly structured legal texts and unstructured client queries. Your task is to ensure that both structured documents and unstructured queries can be efficiently retrieved using vector embeddings generated by the system.
Which of the following actions will help you optimize the generation of vector embeddings to support accurate retrieval in the RAG system, considering the diverse data types? (Select two)

（A）Fine-tune the embedding model on legal text data to ensure embeddings capture the specific structure and semantics of the documents.

（B）Rely on pre-trained general-purpose embeddings without any domain-specific adaptation, as they provide broad coverage across many industries.

（C）Generate embeddings for legal documents at the sentence level rather than the document level, as this increases precision in retrieval.

（D）Ensure that embeddings for legal documents are based solely on the document metadata, as it contains the most important information.

（E）Use a separate embedding model for legal documents and client queries to account for differences in data structure.

正解：A、E 解答を投票する

質問 4

You are fine-tuning a machine learning model using IBM Watsonx with a dataset that includes sensitive information. You decide to enable differential privacy while generating synthetic data to ensure the privacy of individual records.
What key feature of differential privacy ensures that the synthetic data does not leak private information from the original dataset?

（A）Adding controlled noise to the data, ensuring that no individual's data point is easily distinguishable from aggregate data.

（B）Limiting the number of data points generated to avoid overfitting the synthetic data.

（C）Using clustering techniques to group similar data points, preventing individual-level data from being exposed.

（D）Masking sensitive data fields before creating the synthetic data, ensuring no private information is directly used.

正解：A 解答を投票する

質問 5

You are developing a Retrieval-Augmented Generation (RAG) system for a question-answering application. The system relies on generating vector embeddings to retrieve relevant documents based on the input query.
What is the key advantage of using vector embeddings for document retrieval in a RAG pipeline compared to traditional keyword-based search methods?

（A）Vector embeddings capture the semantic meaning of text, allowing for more accurate retrieval of contextually similar documents, even if they do not share exact keywords with the query.

（B）Vector embeddings increase the memory requirements of the system, making retrieval slower but improving the generation quality of the model.

（C）Vector embeddings represent text as fixed-length vectors, allowing for faster indexing but no improvements in retrieval accuracy.

（D）Vector embeddings do not provide any meaningful improvement over keyword-based methods unless combined with reinforcement learning algorithms.

正解：A 解答を投票する

質問 6

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

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

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

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

正解：B 解答を投票する

質問 7

You've conducted a prompt-tuning experiment, and after reviewing the generated outputs, you observe issues such as incomplete responses, irrelevant content, and occasional factual inaccuracies.
What is the most appropriate action to address these data quality problems?

（A）Introduce temperature tuning to adjust the randomness of the model's output and reduce irrelevant content.

（B）Lower the model's perplexity score to improve both completeness and factual accuracy.

（C）Increase the length of the input prompt to ensure that responses are more complete.

（D）Fine-tune the model on domain-specific data to improve factual accuracy and relevance.

正解：D 解答を投票する

質問 8

In the context of Generative AI (GenAI), various embedding models are used to represent textual data.
Which of the following best describes the difference between Word2Vec, BERT, and Sentence-BERT embedding models?

（A）Word2Vec uses a transformer architecture for embedding generation, whereas BERT and Sentence-BERT use neural networks to model context.

（B）Word2Vec creates static word embeddings, BERT generates dynamic embeddings based on context, and Sentence-BERT produces embeddings specifically optimized for sentence-level tasks like semantic similarity.

（C）Word2Vec captures contextual relationships between words, while BERT and Sentence-BERT generate sentence-level embeddings based on the overall document length.

（D）Word2Vec captures both word and sentence meanings in a single vector space, BERT generates only word embeddings, and Sentence-BERT generates embeddings for entire documents.

正解：B 解答を投票する

質問 9

You are testing a new version of a prompt template designed to improve the accuracy of responses from a generative model deployed on IBM Watsonx. After deploying the new prompt version, you need to ensure that it performs better or at least as well as the previous version.
Which of the following approaches provides the most reliable method for testing the performance of the new prompt template version?

（A）Use a random subset of production data and test both versions in a local environment, as local tests always replicate the conditions of production.

（B）Replace the old prompt with the new one in the live system immediately to avoid confusion between prompt versions.

（C）Test the new prompt in production without monitoring and observe user feedback to gauge performance.

（D）Run a series of A/B tests comparing the new prompt template to the old one, using a set of predetermined metrics, such as response accuracy and completion time.

正解：D 解答を投票する

質問 10

When selecting parameters to optimize a prompt-tuned model experiment in IBM watsonx, which parameter is the most critical for controlling the model's ability to generate coherent and contextually accurate responses?

（A）Dropout rate

（B）Batch size

（C）Gradient clipping

（D）Learning rate

正解：D 解答を投票する

質問 11

In a generative AI-based customer service chatbot, you notice that the model sometimes generates user responses that inadvertently reveal sensitive personal information, such as names, addresses, or social security numbers.
What is the most effective prompt engineering technique to reduce this risk while preserving the chatbot's functionality?

（A）Use generic prompts that include placeholders for sensitive information (e.g., [USER_NAME])

（B）Increase the model's randomness by adjusting the temperature parameter

（C）Reduce the model's token limit to restrict the amount of text generated

（D）Introduce explicit instructions in the prompt to avoid generating personal information

正解：D 解答を投票する

質問 12

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 13

You are tasked with building a Retrieval-Augmented Generation (RAG) system using Elasticsearch for document storage, Watson ML for model hosting, and LangChain for orchestration. The chatbot is supposed to query a large database of medical records and generate responses based on the retrieved information. During testing, you notice that irrelevant documents are often retrieved, leading to low-quality responses.
What would be the best approach to improve document relevance in this RAG setup?

（A）Fine-tune the Watson ML model on a dataset containing both queries and relevant documents, then update Elasticsearch with the model outputs.

（B）Update the Elasticsearch index by re-indexing documents using embeddings from the Watson ML model.

（C）Implement a BM25 similarity algorithm in Elasticsearch to improve document retrieval.

（D）Pre-process the medical records in LangChain to remove irrelevant information before sending the query to Elasticsearch.

正解：B 解答を投票する

質問 14

When deploying a machine learning model in a highly regulated industry (e.g., healthcare or finance), which strategy is most effective to ensure ongoing model performance while adhering to AI governance standards?

（A）Ensure model interpretability is maximized by simplifying the architecture to a linear model

（B）Implement a model performance monitoring framework with fairness and bias detection metrics

（C）Perform real-time continuous training of the model using live data from the production environment

（D）Deploy the model with hard-coded rules to ensure it does not drift from expected behavior

正解：B 解答を投票する

質問 15

You are designing prompt templates for an automated content generation tool that your team uses for marketing copy. The tool must balance cost efficiency with the generation of high-quality outputs. The prompts should be structured to avoid excessive token usage while ensuring that the model provides coherent and actionable content.
Which of the following techniques would best optimize the design of cost-effective prompt templates?

（A）Designing prompts that incorporate redundancy to ensure model outputs address all possible user needs.

（B）Embedding explicit stop words within the prompt to control the length of generated outputs.

（C）Using placeholders in the prompt templates for dynamic insertion of variables specific to each task.

（D）Creating templates that provide extensive background information to give the model context.

正解：C 解答を投票する

質問 16

When planning data elements for optimizing a generative AI model's performance in IBM watsonx, which of the following strategies should be prioritized to ensure data quality and model accuracy?

（A）Focus on using only labeled data, as generative AI models do not perform well on unlabeled data.

（B）Ensure all data used is unstructured to allow for maximum model flexibility.

（C）Leverage data augmentation techniques only on the smallest available dataset.

（D）Prioritize balanced datasets to minimize model bias and enhance generalization.

正解：D 解答を投票する

質問 17

You are working on a large-scale enterprise application using IBM watsonx and need to ensure that different versions of your generative AI model prompts are properly managed for deployment.
Which of the following is the most appropriate action when planning the deployment of prompt versions?

（A）Embed the prompt version directly into the API request body so that the deployed model can select the correct prompt dynamically at runtime.

（B）Store all prompt versions directly in the model's code repository, updating the main branch with each new version.

（C）Keep prompt versions in an external document management system and manually track which versions are deployed in the application.

（D）Use a deployment space in IBM watsonx to version your prompts, assigning unique tags to each version and ensuring rollback capabilities.

正解：D 解答を投票する

質問 18

In the context of generative AI and large language models, text embeddings are a key component.
What is the primary purpose of text embeddings in a retrieval-augmented generation (RAG) system, and how are they used?

（A）Text embeddings are used to randomly assign values to words in the corpus, providing variability in how the model generates text.

（B）Text embeddings generate a one-to-one mapping of words, allowing for the exact reconstruction of the original input text.

（C）Text embeddings are used to reduce the dimensionality of the input text for faster training of the generative model.

（D）Text embeddings map words and phrases to high-dimensional vectors that capture their semantic meaning, allowing for efficient document retrieval based on contextual similarity.

正解：D 解答を投票する

質問 19

You are tasked with explaining the outcomes produced by a Watsonx Generative AI model based on specific prompts.
Which of the following approaches is most effective in ensuring transparency and understanding of how the model arrives at its decisions?

（A）Explaining the optimization process that minimized the model's loss function

（B）Providing an interpretable

正解：B 解答を投票する

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

弊社を連絡する

関連リンク

トップ試験