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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q63-Q68):

NEW QUESTION # 63
You are using IBM Watsonx to control the randomness of a language model's output by adjusting the top-k parameter.
What happens when you reduce the top-k value from 50 to 5 during text generation?

A. Reducing the top-k value reduces the model's ability to predict rare or uncommon words, leading to less accurate outputs.
B. Lowering the top-k value forces the model to generate shorter outputs by limiting the number of tokens available for selection.
C. Reducing the top-k value increases the temperature of the model, making the outputs more creative and diverse.
D. The model will now consider only the top 5 most probable tokens at each step, making the output more deterministic and focused.

Answer: D

NEW QUESTION # 64
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. Use sentiment analysis to detect and block negative content that may indicate hate speech or abuse.
B. Implement regular expression-based filters that cover a wide range of variations in harmful language.
C. Continuously monitor and retrain the model on datasets containing edge cases of hate speech, abuse, and profanity.
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 # 65
You are tuning a generative AI model to reduce repetitive outputs, which often occur when generating long texts.
Which of the following parameter adjustments would most likely reduce the model's tendency to repeat words or phrases without compromising the quality of the generated text?

A. Lowering the temperature to 0.1
B. Increasing the repetition penalty to 1.2
C. Increasing the Top-k value to 200
D. Reducing the beam size to 1

Answer: B

NEW QUESTION # 66
In the context of zero-shot prompting, you are developing a Watsonx AI model to generate a summary of financial reports. You input the following prompt: "Summarize the financial report in one sentence." Based on your understanding of zero-shot prompting, what outcome should you expect from the model?

A. he model will refuse to generate a response due to lack of examples provided in the prompt.
B. The model will ask for additional data before generating the summary.
C. The model will output a random sentence because zero-shot prompting is unreliable without examples.
D. The model may generate a reasonable summary based solely on the prompt, even if it hasn't been explicitly trained on financial report summaries.

Answer: D

NEW QUESTION # 67
In a Retrieval-Augmented Generation (RAG) system designed for technical document retrieval, you are tasked with implementing text chunking techniques using the LangChain library. The technical documents are large and contain numerous tables, figures, and bullet points.
What is the most effective way to handle text splitting to ensure high-quality retrieval?

A. Convert tables and figures into plain text and split the document by character count to maintain even chunk sizes.
B. Split the text only at paragraph breaks, ignoring tables and figures, as they can be processed separately.
C. Split the text into equal-sized chunks of 512 characters, regardless of the content structure, to improve consistency in retrieval.
D. Use a hybrid approach, splitting the text by both semantic boundaries (like paragraphs) and content-specific markers (like bullet points and tables), while keeping chunks within the model's token limit.

Answer: D

NEW QUESTION # 68
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