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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) Train the model only on data that excludes all user-generated content to prevent exposure to harmful language.
B) Apply a simple word-level blacklist filter to detect and remove harmful content from the model output.
C) 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.
D) Use IBM Watsonx's HAP (Hate, Abuse, and Profanity) filter to dynamically detect and block harmful content at inference time.

2. In the context of Tuning Studio in IBM watsonx, what is one of the key benefits of using Compute Unit Hours (CUHs) during the fine-tuning process?

A) It reduces the time required to train models by lowering the accuracy threshold.
B) It allows for the precise allocation of computational resources to manage budget constraints.
C) It provides real-time feedback on model deployment success rates.
D) It limits the number of model versions stored, improving system performance.

3. As a Generative AI engineer, you're tasked with optimizing the performance and cost-efficiency of a model by adjusting the model parameters.
Given that your objective is to reduce the cost of generation while maintaining acceptable quality, which of the following parameter changes is most likely to result in cost savings?

A) Decrease the max tokens parameter.
B) Increase the max tokens parameter to allow for more complex output.
C) Increase the top-k sampling value.
D) Set the temperature parameter to a higher value.

4. A team is tasked with selecting a vector database to store and search through millions of document embeddings efficiently. They need to ensure that the chosen database supports operations such as approximate nearest neighbor (ANN) search and scalability.
Which of the following considerations is most important when selecting the appropriate vector database for this application?

A) The vector database should allow for both ANN search and customizable distance metrics, such as cosine or Euclidean distance.
B) The database should limit the embedding size to 256 dimensions to reduce storage costs and ensure scalability.
C) The database should store embeddings in relational format (rows and columns) for compatibility with traditional SQL queries.
D) The database must support exact nearest neighbor (ENN) searches to ensure high-precision results.

5. You are building a chatbot using a generative AI model for a medical advice platform. During testing, you notice that the model occasionally generates medical information that contradicts established guidelines. This is an example of a model hallucination.
Which prompt engineering technique would best mitigate the risk of hallucination in this scenario?

A) Increasing the model's temperature parameter
B) Implementing zero-shot learning techniques
C) Providing a list of credible sources in the prompt
D) Using more open-ended prompts

Solutions: