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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You've conducted a prompt-tuning experiment, and after reviewing the generated outputs, you observe issues such as incomplete responses, irrelevant content, and occasional factual inaccuracies.
What is the most appropriate action to address these data quality problems?

A) Increase the length of the input prompt to ensure that responses are more complete.
B) Lower the model's perplexity score to improve both completeness and factual accuracy.
C) Fine-tune the model on domain-specific data to improve factual accuracy and relevance.
D) Introduce temperature tuning to adjust the randomness of the model's output and reduce irrelevant content.

2. You are tasked with developing a system that uses a vector database to store embeddings generated from a large corpus of documents. The system should be able to perform fast and efficient nearest neighbor search while balancing accuracy and speed. Given the large volume of data and the need for scalability, you are considering different indexing strategies offered by vector databases.
Which of the following indexing techniques is the most appropriate for balancing search accuracy and speed in high-dimensional vector space, and why?

A) Rely on full-text indexing of documents and avoid vector search altogether.
B) Exact nearest neighbor (ENN) search using KD-trees.
C) Linear search over the entire vector dataset.
D) Approximate nearest neighbor (ANN) search using HNSW (Hierarchical Navigable Small World) graphs.

3. You are using IBM Watsonx to control the randomness of a language model's output by adjusting the top-k parameter.
What happens when you reduce the top-k value from 50 to 5 during text generation?

A) The model will now consider only the top 5 most probable tokens at each step, making the output more deterministic and focused.
B) Reducing the top-k value reduces the model's ability to predict rare or uncommon words, leading to less accurate outputs.
C) Reducing the top-k value increases the temperature of the model, making the outputs more creative and diverse.
D) Lowering the top-k value forces the model to generate shorter outputs by limiting the number of tokens available for selection.

4. You are tasked with optimizing the cost of text generation using a generative AI model by adjusting model parameters. One of the key parameters you consider is the temperature, which controls the randomness of the output. The client has requested outputs that are more predictable and closer to the intended meaning, without unnecessary creativity, in order to reduce unnecessary token usage and ensure the quality of generated responses. Given the following task:
"Generate a brief summary of a technical article that focuses on key innovations without including unrelated or creative content." Which of the following temperature settings would be most appropriate to optimize cost while maintaining focus and minimizing unnecessary token usage?

A) 1.2
B) 0.0
C) 1.0
D) 0.1

5. Which of the following techniques is the most effective for reducing bias in generative AI models through prompt engineering?

A) Allowing the model to auto-correct its own responses by cross-referencing with other outputs
B) Fine-tuning the model using training data that explicitly includes examples of biased outputs
C) Using neutral and carefully phrased prompts to avoid triggering biased outputs
D) Applying Greedy Decoding to ensure the most likely tokens are selected during generation

Solutions: