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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. In the context of quantizing large language models (LLMs), which of the following statements best describes the key trade-offs between model size, performance, and accuracy when using quantization techniques?

A) Quantization maintains model accuracy but doubles the computation required for inference.
B) Quantization reduces model size but may lead to a loss of accuracy, especially with aggressive quantization methods.
C) Quantization always improves model performance but significantly increases model size.
D) Quantization eliminates the need for fine-tuning after deployment, ensuring zero accuracy loss.

2. During prompt engineering for IBM Watsonx, you need to understand how the decoding process works when generating responses.
Which of the following best describes a high-level overview of the decoding process in generative AI?

A) Decoding is the final step in training, where the AI model verifies the accuracy of its outputs against a predefined set of labels.
B) Decoding involves translating the input data into a format that the AI model can understand before generating an output.
C) Decoding is the process where the model generates a response token-by-token, choosing each token based on the probability distribution over all possible tokens.
D) Decoding occurs only in reinforcement learning, where the model refines its responses based on user feedback over multiple generations.

3. You are deploying a generative AI model to summarize legal documents. During testing, you observe that the model sometimes produces inaccurate or fabricated information about legal clauses that do not exist in the original document.
Which of the following strategies would be the most effective in reducing hallucinations in the model's output?

A) Increase the model's temperature to allow for more diverse outputs.
B) Reduce the maximum token limit and set a higher repetition penalty.
C) Use beam search with a high beam width to generate the most likely sequence.
D) Implement a post-processing step that filters for factual accuracy using a knowledge base.

4. In a scenario where a developer is creating reusable prompt templates for a Watsonx Generative AI project, what is the most effective method to track the usage and performance of these templates over time?

A) Embedding unique identifiers in the prompt templates and using Watsonx's logging mechanisms
B) Using static prompt templates without any tracking, as tracking adds unnecessary overhead
C) Leveraging Watsonx's native analytics and monitoring tools with built-in prompt tracking features
D) Relying on manual tracking of templates in a spreadsheet for each generation

5. A healthcare organization is deploying a generative AI model to assist in summarizing patient records. Given the sensitivity of patient data, the model must not output Personally Identifiable Information (PII) such as names, addresses, or Social Security numbers.
Which of the following strategies would best help prevent the model from unintentionally generating PII?

A) Fine-tune the model specifically on anonymized datasets with PII removed.
B) Increase the temperature and top-k values to reduce the probability of PII being generated.
C) Disable the model's ability to use context from previous tokens to prevent PII from being included in the output.
D) Use a post-processing step to detect and mask PII after the model generates the text.

Solutions: