IBM C1000-185 Advanced Testing Engine | C1000-185 Brain Exam

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q162-Q167):

NEW QUESTION # 162
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 # 163
When deploying a generative AI model using IBM watsonx, which of the following actions should be prioritized to ensure high availability and minimize downtime in production environments? (Select two)

A. Setting up redundant storage for the model's dataset but not for the model itself
B. Configuring auto-scaling policies based on usage patterns and resource demand
C. Implementing containerization for the model through Kubernetes
D. Disabling failover mechanisms to reduce deployment complexity
E. Deploying the model on a single cloud-based server with load balancing enabled

Answer: B,C

NEW QUESTION # 164
You are tasked with integrating IBM watsonx with a third-party customer relationship management (CRM) system to enhance customer interactions through conversational AI.
Which of the following approaches best ensures real-time responses while minimizing the latency caused by data exchange between systems?

A. Set up periodic data synchronization between the CRM system and IBM watsonx using FTP file transfers.
B. Use IBM watsonx API for batch processing of customer requests, triggering the CRM system via a scheduled job.
C. Utilize watsonx's real-time streaming API with pre-configured webhooks in the CRM system for instant data retrieval and response.
D. Employ an asynchronous message queue between IBM watsonx and the CRM system to handle customer requests.

Answer: C

NEW QUESTION # 165
You are developing a generative AI model using the IBM Watsonx platform to assist in customer service. While the model's responses are highly accurate, there is concern that the model may inadvertently expose personal information (PII) or sensitive data during interactions. As a responsible AI engineer, it is crucial to mitigate this risk.
Which of the following is the most critical risk associated with the exposure of personal information in generative AI models?

A. The model can unintentionally memorize and regurgitate personal information from the training data, leading to privacy violations.
B. The model can generate overly creative or non-factual responses, leading to brand reputation damage.
C. The model can generate outputs that are too general, failing to meet the specific needs of the user.
D. The model might produce content that doesn't align with the cultural preferences of the user.

Answer: A

NEW QUESTION # 166
In the context of Generative AI models, which scenario best illustrates the use of greedy decoding for determining the output sequence?

A. Averaging the probabilities of multiple tokens at each step to generate a sequence
B. Randomly selecting tokens from the top-k highest probability tokens at each step
C. Selecting the token that maximizes the cumulative probability of the sequence, considering future tokens
D. Selecting the token with the highest probability at each step, without considering future possibilities

Answer: D

NEW QUESTION # 167
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