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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q175-Q180):

NEW QUESTION # 175
You are tasked with designing an AI prompt to extract specific data from unstructured text. You decide to use either a zero-shot or a few-shot prompting technique with an IBM Watsonx model.
Which of the following statements best describes the key difference between zero-shot and few-shot prompting?

A. Zero-shot prompting requires no examples in the prompt, while few-shot prompting provides the model with one or more examples to clarify the task.
B. Zero-shot prompting provides the model with examples, while few-shot prompting does not.
C. Zero-shot prompting requires retraining the model with additional data, while few-shot prompting uses a pre-trained model without retraining.
D. Few-shot prompting is used when the model is trained on supervised learning, while zero-shot prompting works only with unsupervised models.

Answer: A

NEW QUESTION # 176
You are tasked with inspecting and validating a dataset using IBM Watson's Data Refinery tool. The dataset will be used for an AI application that performs natural language processing (NLP) on customer support tickets. Upon inspection, you discover that the 'ticket_description' field contains numerous instances of null values and inconsistent formats.
What is the most appropriate action to take next using Data Refinery?

A. Replace all null values with zeros, as this will standardize the dataset.
B. Proceed with training the model without any changes, as missing data will not impact NLP models significantly.
C. Delete all rows containing null values in the 'ticket_description' field.
D. Use Data Refinery's "Data Quality" feature to generate a profiling report and assess the scope of the issues.

Answer: D

NEW QUESTION # 177
You are tasked with developing a system that uses a vector database to store embeddings generated from a large corpus of documents. The system should be able to perform fast and efficient nearest neighbor search while balancing accuracy and speed. Given the large volume of data and the need for scalability, you are considering different indexing strategies offered by vector databases.
Which of the following indexing techniques is the most appropriate for balancing search accuracy and speed in high-dimensional vector space, and why?

A. Linear search over the entire vector dataset.
B. Rely on full-text indexing of documents and avoid vector search altogether.
C. Approximate nearest neighbor (ANN) search using HNSW (Hierarchical Navigable Small World) graphs.
D. Exact nearest neighbor (ENN) search using KD-trees.

Answer: C

NEW QUESTION # 178
You are tasked with integrating third-party embedding models into a Retrieval-Augmented Generation (RAG) system for document retrieval. Several models offer pre-trained embeddings that can be leveraged for a variety of downstream tasks.
Which of the following third-party models is designed for generating embeddings that capture semantic meaning and context, making it ideal for a RAG-based GenAI system?

A. WordNet
B. GPT-3 Embeddings
C. BERT
D. Naive Bayes Classifier

Answer: C

NEW QUESTION # 179
In a system that generates product recommendations using a generative AI model, you are tasked with creating prompts that incorporate dynamic variables to ensure more relevant and personalized responses.
Prompt Template: "Recommend products for [customer_name] based on their interest in [product_category]." Which portion of the prompt is the best candidate to be replaced with variables to achieve greater personalization and flexibility? (Select two)

A. "based on their interest"
B. "[product_category]"
C. "Recommend products"
D. "[customer_name]"
E. "for"

Answer: B,D

NEW QUESTION # 180
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C1000-185 Exam Actual Questions - C1000-185 Reliable Test Price

C1000-185 Reliable Test Price | C1000-185 Valid Dumps Demo

IBM watsonx Generative AI Engineer - Associate Sample Questions (Q175-Q180):

Updated IBM watsonx Generative AI Engineer - Associate Questions Cram - C1000-185 Pdf Review & IBM watsonx Generative AI Engineer - Associate Examboost Vce

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