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C1000-185 Real Dump | Latest C1000-185 Version

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q284-Q289):

NEW QUESTION # 284
You are tasked with deploying a watsonx Generative AI solution for a client who requires real-time inference with minimal latency. The solution will be used for large-scale text generation tasks, where throughput is critical. The client also requires the system to handle fluctuating loads efficiently.
What would be the most appropriate deployment strategy?

A. Deploy the model on a multi-node GPU cluster with Kubernetes to handle auto-scaling and fault tolerance.
B. Deploy the model on a single GPU-based cloud instance with auto-scaling enabled.
C. Use a CPU-based deployment for cost efficiency, and manually adjust the number of instances as needed.
D. Deploy the model in a hybrid architecture, splitting inference tasks between on-premise servers and cloud resources.

Answer: A

NEW QUESTION # 285
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's embeddings should always be fine-tuned on your specific corpus before use, as pre-trained embeddings are too general for most tasks.
C. 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.
D. The model should generate embeddings based on sentence-level inputs only, as document-level embeddings are always too large for effective retrieval.

Answer: C

NEW QUESTION # 286
You are developing a machine learning pipeline using IBM watsonx that includes fine-tuning an LLM with a dataset containing sensitive personal information. To ensure privacy, you decide to apply differential privacy.
Which of the following actions is most critical to configure in the user interface to meet the differential privacy requirements during model fine-tuning?

A. Apply a differential privacy mechanism that adds calibrated noise to both the model updates and synthetic data generation process.
B. Use synthetic data only, which eliminates the need for differential privacy as it does not contain real user information.
C. Remove differential privacy settings for fine-tuning, but apply them in the final inference model to reduce performance degradation.
D. Increase the learning rate and batch size to maximize the noise added by differential privacy algorithms.

Answer: A

NEW QUESTION # 287
A healthcare organization is deploying a generative AI model to assist in summarizing patient records. Given the sensitivity of patient data, the model must not output Personally Identifiable Information (PII) such as names, addresses, or Social Security numbers.
Which of the following strategies would best help prevent the model from unintentionally generating PII?

A. Use a post-processing step to detect and mask PII after the model generates the text.
B. Fine-tune the model specifically on anonymized datasets with PII removed.
C. Increase the temperature and top-k values to reduce the probability of PII being generated.
D. Disable the model's ability to use context from previous tokens to prevent PII from being included in the output.

Answer: B

NEW QUESTION # 288
In the context of sampling decoding for IBM Watsonx Generative AI, which of the following statements best describes how top-k sampling works?

A. Top-k sampling automatically filters out low-probability tokens that were not part of the model's training set.
B. Top-k sampling ensures that the next token is chosen only if it matches one of the predefined input variables.
C. Top-k sampling selects the token with the highest probability, ignoring all other token options.
D. Top-k sampling selects the next token only from the top k most probable tokens based on their probabilities.

Answer: D

NEW QUESTION # 289
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