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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q320-Q325):

NEW QUESTION # 320
You are tasked with deploying a suite of AI assets, including a pretrained generative language model and a set of custom-trained models. Your company operates in an environment where scalability and adaptability are key, as customer needs vary significantly across regions and sectors. You need to ensure that the deployment of these AI assets can meet varying demand, maintain performance, and allow for customization based on specific client needs.
Which deployment strategy best balances scalability, performance, and adaptability for this suite of AI assets?

A. Fine-tune the pretrained model for each client individually, deploy as separate models for each use case, and store them locally for each region.
B. Deploy the AI assets on edge devices near the customer, reducing latency and ensuring consistent performance without relying on cloud infrastructure.
C. Containerize each AI asset separately, deploy them as microservices on a cloud platform, and use load balancing to manage varying demand across regions.
D. Deploy all AI assets as a single monolithic model on a multi-GPU setup, allowing scalability across multiple customers without the need for further customization.

Answer: C

NEW QUESTION # 321
A large language model you are fine-tuning occasionally generates completely fabricated references and citations when responding to user queries. This behavior exemplifies a specific model risk.
Which of the following techniques would most effectively reduce this risk in a production environment?

A. Increasing the model's response diversity by adjusting top-p sampling
B. Deploying rule-based post-processing filters to validate the output
C. Using human-in-the-loop (HITL) methods for real-time validation
D. Switching to greedy decoding for more deterministic responses

Answer: B

NEW QUESTION # 322
You are fine-tuning a large language model (LLM) for a sentiment analysis task using customer reviews. The dataset is relatively small, so you decide to augment it using IBM InstructLab.
Which approach would be the most effective in generating high-quality synthetic data for this fine-tuning process?

A. Use IBM InstructLab to generate synthetic data, but only for neutral sentiment, as the model already handles positive and negative sentiment well.
B. Use a generic prompt to generate a wide variety of data from IBM InstructLab, regardless of sentiment polarity.
C. Increase the diversity of synthetic data by focusing on outliers and rare sentiment cases that are underrepresented in the original dataset.
D. Fine-tune IBM InstructLab itself to generate data that closely resembles the training data format, ensuring consistent sentiment distribution.

Answer: D

NEW QUESTION # 323
You are deploying a generative AI model to summarize legal documents. During testing, you observe that the model sometimes produces inaccurate or fabricated information about legal clauses that do not exist in the original document.
Which of the following strategies would be the most effective in reducing hallucinations in the model's output?

A. Increase the model's temperature to allow for more diverse outputs.
B. Use beam search with a high beam width to generate the most likely sequence.
C. Reduce the maximum token limit and set a higher repetition penalty.
D. Implement a post-processing step that filters for factual accuracy using a knowledge base.

Answer: D

NEW QUESTION # 324
You are tasked with fine-tuning a generative AI model for text data using synthetic data created through the IBM watsonx platform's user interface. The data you are working with is skewed, containing mostly outliers, and you need to ensure that the synthetic data mimics the distribution accurately.
Which algorithm would be most appropriate for generating synthetic data that mirrors the original distribution, considering the Anderson-Darling test for normality?

A. Anderson-Darling Based Synthetic Data Generation (ADS-DG)
B. Generative Adversarial Networks (GANs)
C. Bootstrapping
D. Decision Trees

Answer: A

NEW QUESTION # 325
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