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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. As an IBM Watsonx Generative AI engineer, you are tasked with creating a chatbot for a public-facing service. One key concern is ensuring that the model does not generate or propagate hate speech, abusive content, or profanity. To mitigate these risks, you must implement appropriate controls.
Which of the following is the best approach to mitigate hate speech, abuse, and profanity from being generated by your AI model?

A) Use IBM Watsonx's HAP (Hate, Abuse, and Profanity) filter to dynamically detect and block harmful content at inference time.
B) Train the model only on data that excludes all user-generated content to prevent exposure to harmful language.
C) Apply a simple word-level blacklist filter to detect and remove harmful content from the model output.
D) Fine-tune the model with a highly curated dataset that contains labeled examples of hate speech, abuse, and profanity for the model to learn to avoid.

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

3. You are working with a Generative AI model to generate a summary of a large financial report. To reduce costs, you are exploring different model parameters such as minimum and maximum token limits.
Which configuration would help minimize generation costs while ensuring an accurate summary of the document?

A) Set the maximum token limit to 300 and the minimum token limit to 0, allowing the model to generate a brief summary of the most relevant points while avoiding excessive verbosity.
B) Set the maximum token limit to 700 and the minimum token limit to 600 to ensure a well-rounded summary with all necessary sections and detailed information included.
C) Set both the minimum and maximum token limits to 1,000 to ensure the entire report is captured in detail, with no important information left out.
D) Set the maximum token limit to 500 and the minimum token limit to 250 to ensure a balanced summary of key sections with some detailed

4. You are working on a generative AI model for a wide variety of language generation tasks. Your team is debating whether to use soft prompts for optimizing the model's performance.
Which of the following is an accurate benefit of using soft prompts, and what is a potential drawback?

A) Benefit: Soft prompts offer more control over the output. Drawback: Soft prompts require additional manual engineering for each task.
B) Benefit: Soft prompts allow for fine-grained control during inference. Drawback: Soft prompts may lead to lower performance with large datasets.
C) Benefit: Soft prompts can optimize performance without retraining the model. Drawback: They are computationally expensive to tune during inference.
D) Benefit: Soft prompts reduce the amount of explicit data needed for training. Drawback: Soft prompts might not generalize well across domains.

5. You are generating a list of items using IBM watsonx's generative AI, but you notice that the model sometimes cuts off mid-sentence when using a stop sequence.
What could be the best approach to ensure that the model finishes generating complete sentences while also stopping after a specific sequence is reached?

A) Use multiple stop sequences, including a period ."
B) Use a more distinct and unlikely stop sequence, such as "<END>"
C) Increase the token limit to avoid premature cut-off
D) Set the stop sequence to a punctuation mark like ";"

Solutions: