C1000-185 Study Test, C1000-185 Relevant Exam Dumps

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q159-Q164):

NEW QUESTION # 159
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. Approximate nearest neighbor (ANN) search using HNSW (Hierarchical Navigable Small World) graphs.
B. Rely on full-text indexing of documents and avoid vector search altogether.
C. Exact nearest neighbor (ENN) search using KD-trees.
D. Linear search over the entire vector dataset.

Answer: A

NEW QUESTION # 160
You're developing a generative AI system for a medical diagnosis application that uses patient data. Your responsibility includes designing prompts that extract valuable insights without exposing sensitive patient information.
Which of the following steps is the most effective way to reduce model risks related to privacy while ensuring useful outputs from the AI?

A. Employ differential privacy techniques to add noise to the model's outputs.
B. Increase the length of the prompts to provide more context, ensuring more accurate results.
C. Restrict the model's output length to reduce the risk of sensitive information leakage.
D. Utilize a smaller model to minimize the likelihood of overfitting sensitive data.

Answer: A

NEW QUESTION # 161
In the context of a Retrieval-Augmented Generation (RAG) system using IBM Watsonx, which of the following is the correct process for generating vector embeddings for document retrieval?

A. Directly extract the vector embeddings from raw text without any tokenization or processing steps by passing the raw data to a pre-trained language model.
B. Tokenize the text data, then pass the tokens through a pre-trained language model to generate vector embeddings for each token, combining them into a final vector.
C. Load the text data into the model, tokenize it, and directly feed the tokens into a decoder for vector generation.
D. Use a pre-trained generative model to generate text predictions, then use these predicted tokens as the basis for vector embedding generation.

Answer: B

NEW QUESTION # 162
You are tasked with developing a generative AI application for customer service, using a Watsonx model to generate responses based on user queries. The dataset includes user-provided data such as past interaction histories, query types, and feedback ratings.
Which two approaches should you implement to optimize the usage of these data elements for generating highly relevant responses? (Select two)

A. Use query type as an additional input feature to ensure the model tailors responses accordingly.
B. Apply data augmentation techniques to artificially expand the dataset with new, synthetic interaction histories.
C. Filter out user interaction history data that is more than six months old to focus on recent patterns.
D. Normalize feedback ratings data by converting them into discrete categories (e.g., low, medium, high).
E. Prioritize the use of user feedback ratings over query type for generating more personalized responses.

Answer: A,D

NEW QUESTION # 163
You are deploying a Generative AI solution for a client who needs to generate customer service emails in multiple languages. The client has provided a dataset of historical customer service emails, and they want to ensure that their generative model consistently produces accurate, contextually appropriate responses across different languages. The client also has concerns about the latency of the model's responses. Based on these requirements, you are tasked with planning the deployment of the generative AI solution.
Which deployment strategy would be most appropriate for this client's needs, considering latency, language handling, and response quality?

A. Use a fine-tuned model per language, with nucleus sampling and run them on a single GPU instance to reduce the infrastructure cost.
B. Deploy a single multilingual model using beam search decoding, and run the model on a single GPU cluster to ensure consistent responses.
C. Deploy a tuned prompt for each language on separate models, using greedy decoding to minimize latency, and scale across multiple GPUs for each language.
D. Deploy a single multilingual model with tuned prompts for each language, using top-k sampling and leveraging a multi-GPU distributed environment to balance response time and quality.

Answer: D

NEW QUESTION # 164
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C1000-185 Study Test, C1000-185 Relevant Exam Dumps

Top C1000-185 Study Test | Easy To Study and Pass Exam at first attempt & Latest updated C1000-185: IBM watsonx Generative AI Engineer - Associate

IBM watsonx Generative AI Engineer - Associate Sample Questions (Q159-Q164):

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