最新的IBM C1000-185免費考試真題

問題1

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?

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

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

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

正確答案: A

問題2

You are fine-tuning a large language model (LLM) with InstructLab to generate high-quality summaries for long-form text documents.
After conducting an initial experiment, the performance seems suboptimal, particularly on technical documents with specialized vocabulary.
What steps could you take to improve the fine-tuning process to achieve better performance? (Select two)

A. Experiment with a smaller model to reduce complexity and increase inference speed.

B. Use greedy decoding during inference to generate concise summaries.

C. Add more domain-specific training data to improve the model's understanding of technical vocabulary.

D. Perform prompt engineering by providing specific task instructions for summarization.

E. Increase the learning rate for better adaptation to specialized data.

正確答案: C,D

問題3

You are fine-tuning a pre-trained language model on a dataset of financial news articles to improve its ability to generate summaries of financial reports. After several epochs of training, you observe that the model performs well on the training data, achieving near-perfect accuracy. However, the model's performance on the validation set is much lower, indicating potential overfitting.
What is the most effective adjustment to reduce overfitting while continuing to fine-tune the model?

A. Reduce the size of the training dataset

B. Apply dropout during training

C. Increase the number of epochs

D. Increase the learning rate

正確答案: B

問題4

In a Retrieval-Augmented Generation (RAG) system, embeddings play a central role in linking input queries with relevant external knowledge. Different embedding models can be used to generate these embeddings.
Which of the following embedding models is best suited for capturing semantic meaning in text for use in a RAG system?

A. Latent Dirichlet Allocation (LDA)

B. One-Hot Encoding

C. Word2Vec

D. Bag-of-Words (BoW)

正確答案: C

問題5

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

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

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

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

正確答案: A

問題6

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

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

C. 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.

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

正確答案: C

問題7

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

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

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

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

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

正確答案: C

問題8

How can developers leverage Prompt Lab in IBM Watsonx to build reusable prompts for a conversational AI system that handles multiple topics?

A. By generating a unique prompt for each possible user query to ensure maximum precision.

B. By designing prompts with dynamic variables for user-specific information, allowing for flexibility across different topics.

C. By creating a single prompt template that adapts to all user inputs regardless of the subject.

D. By utilizing Prompt Lab's automatic suggestion feature, which builds new prompts based on the system's training data.

正確答案: B

問題9

You are working with a healthcare provider to deploy a generative AI model that assists in diagnosing patient conditions based on medical history and symptoms. The healthcare provider is concerned about ethical use, bias, and compliance with health data regulations such as HIPAA.
Which governance approach is the most critical to ensure both compliance and ethical AI deployment?

A. Implement a bias detection framework to identify and mitigate any biases present in the model's outputs.

B. Use a cloud-based infrastructure with HIPAA certification to deploy the model, ensuring compliance with healthcare regulations.

C. Ensure data encryption during model inference to protect patient information from unauthorized access.

D. Apply a federated learning approach to train the model without directly accessing the healthcare provider's patient data.

正確答案: A

問題10

When deploying a generative AI model using IBM watsonx, which of the following actions should be prioritized to ensure high availability and minimize downtime in production environments? (Select two)

A. Configuring auto-scaling policies based on usage patterns and resource demand

B. Deploying the model on a single cloud-based server with load balancing enabled

C. Implementing containerization for the model through Kubernetes

D. Setting up redundant storage for the model's dataset but not for the model itself

E. Disabling failover mechanisms to reduce deployment complexity

正確答案: A,C

問題11

You are tuning a generative AI model to control the length of the generated responses.
Which of the following parameter configurations will ensure that the model generates responses that are at least 50 tokens long but no longer than 150 tokens?

A. Setting the maximum tokens to 50 and minimum tokens to 150

B. Setting the minimum tokens to 150 and maximum tokens to 50

C. Setting no minimum token value but configuring the maximum tokens to 150

D. Setting the minimum tokens to 50 and maximum tokens to 150

正確答案: D

問題12

You are tasked with deploying a versioned prompt for a customer-facing generative AI application. The prompts are iteratively improved based on feedback, and you need to ensure that each version of the prompt is tracked and accessible for rollback in case a newer version produces worse results.
Which strategy would best ensure that all prompt versions are stored and easily retrievable, while minimizing disruption to the current deployment?

A. Leverage a cloud-based deployment pipeline with integrated versioning that automates prompt rollback and audit tracking.

B. Store each version of the prompt as a separate file in a local folder and manually deploy the desired version when needed.

C. Use a version control system like Git to track prompt changes and synchronize the latest version with the deployment environment.

D. Deploy prompts directly to production without versioning and manually track changes in a spreadsheet.

正確答案: C

問題13

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

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

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

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

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

正確答案: C

問題14

In a Retrieval-Augmented Generation (RAG) system, you are tasked with generating vector embeddings for a large corpus of documents. You plan to use a pre-trained transformer-based model to generate these embeddings.
What is the most important factor to consider when choosing a pre-trained model for generating embeddings in this scenario?

A. The model's size (in terms of parameters) should be minimized to reduce memory usage, even if it impacts embedding quality.

B. The model should have been trained on a similar task (e.g., document retrieval), as this ensures the embeddings will be relevant for your corpus.

C. The model should generate embeddings based on sentence-level inputs only, as document-level embeddings are always too large for effective retrieval.

D. The model's embeddings should always be fine-tuned on your specific corpus before use, as pre-trained embeddings are too general for most tasks.

正確答案: B

最新的IBM watsonx Generative AI Engineer - Associate - C1000-185免費考試真題

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