IBM IBM Certified watsonx Generative AI Engineer - Associate C1000-185

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are working with IBM watsonx's generative AI model and wish to reduce the likelihood of generating rare, low-probability tokens while still retaining some level of creativity. You decide to use top-k sampling for this purpose.
Which of the following settings for the top-k parameter would be most effective in achieving a balance between creativity and maintaining coherent outputs?

A) Set top-k to 1
B) Set top-k to 5
C) Set top-k to 50
D) Set top-k to 1000

2. You are tasked with building a Retrieval-Augmented Generation (RAG) system using Elasticsearch for document storage, Watson ML for model hosting, and LangChain for orchestration. The chatbot is supposed to query a large database of medical records and generate responses based on the retrieved information. During testing, you notice that irrelevant documents are often retrieved, leading to low-quality responses.
What would be the best approach to improve document relevance in this RAG setup?

A) Update the Elasticsearch index by re-indexing documents using embeddings from the Watson ML model.
B) Fine-tune the Watson ML model on a dataset containing both queries and relevant documents, then update Elasticsearch with the model outputs.
C) Pre-process the medical records in LangChain to remove irrelevant information before sending the query to Elasticsearch.
D) Implement a BM25 similarity algorithm in Elasticsearch to improve document retrieval.

3. A business wants to deploy a customer service chatbot using IBM watsonx, integrated with multiple back-end systems including ERP, CRM, and a payment gateway. To manage this complex integration, the chatbot should dynamically switch between these systems based on the customer's intent.
Which architecture best supports this requirement while ensuring scalability and minimal orchestration overhead?

A) Implement a monolithic architecture where all system interactions are handled within a single service.
B) Utilize IBM watsonx's API orchestration layer to dynamically route requests to the correct system based on intent analysis.
C) Use synchronous REST APIs for all back-end systems, relying on watsonx's built-in intent recognition to manage API routing.
D) Set up IBM watsonx to queue customer interactions and process them sequentially using a single system at a time.

4. In which scenario would using a soft prompt be more beneficial than a hard prompt in optimizing generative AI outputs?

A) When the prompt needs to be manually adjusted by the user in real time during interaction with the AI.
B) When fine-tuning a pre-trained model for domain-specific tasks, allowing the system to adapt its understanding through learned embeddings.
C) When the task requires explicit and consistent user instructions to ensure deterministic outcomes.
D) When the model needs to generate a strictly factual output with minimal deviation from the prompt.

5. 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) Allowing the model to auto-correct its own responses by cross-referencing with other outputs
C) Applying Greedy Decoding to ensure the most likely tokens are selected during generation
D) Using neutral and carefully phrased prompts to avoid triggering biased outputs

Solutions: