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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You have applied a set of prompt tuning parameters to a language model and collected the following statistics: ROUGE-L score, BLEU score, and memory utilization.
Based on these metrics, how would you prioritize further optimizations to balance the model's performance in terms of output relevance and resource efficiency?

A. Focus on improving the ROUGE-L score while increasing memory utilization
B. Maximize BLEU score and reduce memory utilization
C. Increase memory utilization to reduce BLEU and ROUGE-L scores
D. Reduce memory utilization and maintain BLEU and ROUGE-L scores

Question 2

When planning data elements for optimizing a generative AI model's performance in IBM watsonx, which of the following strategies should be prioritized to ensure data quality and model accuracy?

A. Leverage data augmentation techniques only on the smallest available dataset.
B. Ensure all data used is unstructured to allow for maximum model flexibility.
C. Prioritize balanced datasets to minimize model bias and enhance generalization.
D. Focus on using only labeled data, as generative AI models do not perform well on unlabeled data.

Question 3

You are developing a legal research assistant that uses Retrieval-Augmented Generation (RAG) to help lawyers find relevant case law. The system must provide case law that is not only relevant to the query but also semantically similar in meaning to the legal terminology used.
Given this scenario, which of the following retrievers would be the most appropriate choice for this application?

A. A rule-based retriever optimized for simple, pre-defined keyword mappings.
B. A sparse retriever based on keyword matching and BM25 algorithm.
C. A sparse retriever that integrates exact matching of legal terms with synonym matching.
D. A dense retriever based on embeddings from a fine-tuned BERT model.

Question 4

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

Question 5

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

Solutions: