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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q353-Q358):

NEW QUESTION # 353
Your team is responsible for deploying a generative AI system that will interact with customers through automated chatbots. To improve the quality and consistency of responses across different queries and customer profiles, the team has developed several prompt templates. These templates aim to standardize input to the model, ensuring that outputs are aligned with business objectives. However, the team is debating whether using these prompt templates will provide tangible benefits in the deployment.
What is the primary benefit of deploying prompt templates in this AI system?

A. Reducing the overall inference time by streamlining the input-output process for the model, ensuring faster responses.
B. Enabling more predictable and consistent outputs across different inputs, aligning the model's responses more closely with the business goals.
C. Improving the scalability of the system by allowing the model to handle more diverse inputs without requiring additional fine-tuning.
D. Enhancing the model's ability to generalize across unseen data by training it specifically on the variations included in the prompt template.

Answer: B

NEW QUESTION # 354
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 predefined input that alters the architecture of the AI model during runtime.
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 placeholder within a prompt template that can be replaced with specific input values during execution.

Answer: D

NEW QUESTION # 355
In a project where watsonx.ai is deployed as the core generative model for text generation, you need to augment its capabilities by integrating it with IBM Watson Discovery to access a large corpus of documents for fact-checking and data retrieval.
What is the best way to integrate these two services to ensure that generated responses are both creative and factual?

A. Deploy a custom API layer that combines watsonx.ai with Watson Assistant, letting the latter manage queries and retrieve documents from Watson Discovery on demand.
B. Train watsonx.ai to automatically search Watson Discovery for factual information during the text generation process, eliminating the need for an external integration layer.
C. Have Watson Discovery preprocess user inputs to identify relevant documents, and then feed the retrieved information into watsonx.ai as a context for text generation.
D. Use watsonx.ai to generate all responses, and periodically train the model using data indexed by Watson Discovery.

Answer: C

NEW QUESTION # 356
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. Increase the learning rate
B. Apply dropout during training
C. Reduce the size of the training dataset
D. Increase the number of epochs

Answer: B

NEW QUESTION # 357
You are tasked with fine-tuning a large language model (LLM) on a specific industry dataset using the IBM watsonx user interface. Due to the lack of labeled data, your team decides to generate synthetic data to supplement the training set. The objective is to ensure the fine-tuned model can generalize effectively to real-world scenarios in this industry. You need to configure synthetic data generation and perform the fine-tuning.
Which of the following actions should you take to ensure the synthetic data is suitable for fine-tuning the model and does not lead to overfitting or model bias? (Select two)

A. Generate only a small amount of synthetic data to minimize computational costs and training time.
B. Ensure that the synthetic data covers edge cases as well as common industry scenarios.
C. Configure the synthetic data to exclude rare or uncommon events, as these are not representative of the overall dataset.
D. Use the IBM watsonx Data Refinery tool to inspect and balance the synthetic data before fine-tuning.
E. Rely solely on synthetic data for fine-tuning, as it is fully representative of the industry domain.

Answer: B,D

NEW QUESTION # 358
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C1000-185 New Study Notes | C1000-185 Dumps Vce

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q353-Q358):

C1000-185 New Study Notes - Pass Guaranteed Quiz 2025 First-grade IBM C1000-185 Dumps Vce

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