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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q197-Q202):

NEW QUESTION # 197
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. Deploy a tuned prompt for each language on separate models, using greedy decoding to minimize latency, and scale across multiple GPUs for each language.
B. 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.
C. Deploy a single multilingual model using beam search decoding, and run the model on a single GPU cluster to ensure consistent responses.
D. Use a fine-tuned model per language, with nucleus sampling and run them on a single GPU instance to reduce the infrastructure cost.

Answer: B

NEW QUESTION # 198
In the context of generative AI and large language models, text embeddings are a key component.
What is the primary purpose of text embeddings in a retrieval-augmented generation (RAG) system, and how are they used?

A. Text embeddings are used to reduce the dimensionality of the input text for faster training of the generative model.
B. Text embeddings map words and phrases to high-dimensional vectors that capture their semantic meaning, allowing for efficient document retrieval based on contextual similarity.
C. Text embeddings generate a one-to-one mapping of words, allowing for the exact reconstruction of the original input text.
D. Text embeddings are used to randomly assign values to words in the corpus, providing variability in how the model generates text.

Answer: B

NEW QUESTION # 199
During the deployment of a generative AI model using IBM Watsonx, you are responsible for ensuring that the model does not generate content containing hate speech, abusive language, or profanity. However, in some scenarios, the model might bypass basic filtering mechanisms and produce subtle or context-specific harmful language.
Which of the following best addresses edge cases where hate speech or abuse may slip through more basic filtering mechanisms?

A. Implement regular expression-based filters that cover a wide range of variations in harmful language.
B. Continuously monitor and retrain the model on datasets containing edge cases of hate speech, abuse, and profanity.
C. Use sentiment analysis to detect and block negative content that may indicate hate speech or abuse.
D. Use IBM Watsonx's HAP (Hate, Abuse, and Profanity) filter, which is designed to handle context-specific and subtle forms of harmful language.

Answer: D

NEW QUESTION # 200
In the context of large-scale synthetic data generation for fine-tuning a generative AI model, which of the following practices can lead to data that effectively improves the model's performance on downstream tasks?

A. Generating synthetic data that overrepresents the simplest task cases to reduce computational load
B. Using domain-specific templates to generate synthetic data that reflects the target use case
C. Randomly generating sentences without adhering to the task-specific instructions
D. Avoiding any post-processing or filtering of synthetic data to retain diversity

Answer: B

NEW QUESTION # 201
Which of the following techniques is the most effective for reducing bias in generative AI models through prompt engineering?

A. Fine-tuning the model using training data that explicitly includes examples of biased outputs
B. Applying Greedy Decoding to ensure the most likely tokens are selected during generation
C. Allowing the model to auto-correct its own responses by cross-referencing with other outputs
D. Using neutral and carefully phrased prompts to avoid triggering biased outputs

Answer: D

NEW QUESTION # 202
......

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