IBM C1000-185 valid study dumps : IBM watsonx Generative AI Engineer - Associate

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IBM C1000-185 Exam Overview:

Certification Vendor:	IBM
Exam Name:	IBM watsonx Generative AI Engineer - Associate
Exam Number:	C1000-185
Exam Price:	$200 USD
Related Certifications:	IBM Certified watsonx Generative AI Engineer - Associate
Passing Score:	62%
Real Exam Qty:	62
Exam Format:	Multiple choice, Multiple select
Available Languages:	English
Exam Duration:	90 minutes
Sample Questions:
Exam Way:	Online or Test Center (Pearson VUE)
Pre Condition:	None, but hands-on experience with IBM watsonx.ai Studio is highly recommended.
Official Syllabus URL:	https://www.ibm.com/training/certification/C1000-185

IBM C1000-185 Exam Syllabus Topics:

C1000-185 Exam FAQ: Professional Answers for IBM Candidates

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

When addressing bias in a generative AI model, which of the following strategies is least likely to be effective in reducing biased outputs during text generation?

A. Training the model on a diverse and representative dataset
B. Leveraging prompt rephrasing techniques to remove bias-inducing keywords or phrases
C. Using temperature control during generation to manage diversity in responses
D. Incorporating fairness constraints during the model's training phase

Question 2

In the context of Retrieval-Augmented Generation (RAG), embeddings play a crucial role in ensuring relevant information is retrieved to augment the generative AI's response.
Which of the following best describes the role of embeddings in the RAG process?

A. Embeddings represent the search space for the retriever model, allowing the system to retrieve semantically relevant information based on input queries.
B. Embeddings are used to directly generate the textual responses in the output.
C. Embeddings are only used in fine-tuning generative models and play no role in the retrieval process.
D. Embeddings are pre-trained generative models that augment the retrieval step by generating new query variations.

Question 3

In the context of decoding methods in IBM Watsonx Generative AI, both top-k and top-p sampling are used to control the output of the model.
Which of the following statements correctly distinguishes between top-k and top-p sampling?

A. Top-p sampling ensures deterministic outputs, whereas top-k sampling guarantees random selection from the entire vocabulary.
B. Top-p sampling allows for a more flexible token selection process based on cumulative probabilities, while top-k limits token selection to a fixed number of top choices.
C. Both top-k and top-p sampling ensure that no tokens are selected with a probability below the set threshold, regardless of their context.
D. Top-k sampling samples from a dynamic range of tokens, while top-p sampling restricts choices to a fixed number of tokens.

Question 4

You are working with a Watsonx Generative AI model to create marketing content that balances creativity with efficiency. The goal is to generate engaging content within a predefined time limit without compromising on quality.
Given this context, which two optimization strategies will most effectively help you achieve both speed and content quality? (Select two)

A. Apply top-k sampling with k = 50 to ensure diverse and creative outputs.
B. Use beam search with a beam width of 1 to minimize computation time.
C. Increase the model's learning rate to accelerate content generation.
D. Implement early stopping criteria based on token repetition to avoid lengthy generation.
E. Reduce the batch size to decrease training time and speed up generation.

Question 5

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 single multilingual model using beam search decoding, and run the model on a single GPU cluster to ensure consistent responses.
B. Use a fine-tuned model per language, with nucleus sampling and run them on a single GPU instance to reduce the infrastructure cost.
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.

Solutions: