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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with generating a product description for an e-commerce platform using a generative AI model. However, you notice that the generated text tends to repeat phrases excessively, leading to verbose output. To address this, you decide to adjust the model's temperature parameter.
Which of the following changes would help reduce the repetitiveness of the generated text while maintaining a balance between creativity and coherence?

A) Decrease the temperature from 0.8 to 0.6
B) Set the temperature to 0.0
C) Increase the temperature from 0.5 to 1.5
D) Decrease the temperature from 0.9 to 0.3

2. You are using IBM Watsonx to generate answers for a customer service chatbot. However, the responses generated sometimes include fabricated details that are inaccurate (hallucinations).
Which of the following strategies will best mitigate the risk of hallucination when designing your prompt?

A) Use subjective language in your prompts: ":Can you describe why our product is the best on the market?"
B) Supply explicit product details and constraints in the prompt: "List the latest features of the XYZ smartphone released in 2024, including its AI-powered camera and battery-saving mode."
C) Ask open-ended questions without providing reference information: "What are the latest features of our product?"
D) Rely on the AI model to fact-check and correct hallucinations in its responses automatically: "What can you tell me about our product?"

3. In a RAG system, the retriever is responsible for fetching relevant documents or information from a knowledge base based on the input query. Different retriever types can be used depending on the nature of the task.
Which retriever type is most suitable for a RAG system that requires efficient large-scale retrieval from a document corpus based on semantic similarity?

A) Lexical Retriever: Primarily returns results based on syntactic similarity, relying on word order and surface-level features of the input query.
B) Hybrid Retriever: Combines syntactic retrieval methods (like BM25) with semantic retrieval (like dense retrieval) but sacrifices retrieval speed for accuracy.
C) Exact-Match Retriever: Returns documents based solely on keyword matching and is optimized for highly structured, labeled datasets.
D) Dense Retriever: Uses vector embeddings to retrieve documents based on the semantic similarity of the input query and stored documents.

4. You have just finished a prompt tuning experiment for a large language model (LLM) to optimize its output for generating customer support summaries. The tuning results show that while the accuracy of the generated summaries is high (95%), the response time for generating them has significantly increased. The experiment data suggests that increasing the maximum token length during tuning led to better quality summaries but with slower generation.
Which parameter should you adjust to improve the model's response time without sacrificing too much summary quality?

A) Change the decoding strategy from beam search to greedy decoding
B) Increase the batch size
C) Decrease the maximum token length
D) Decrease the number of epochs

5. When deploying AI assets in a deployment space, what is the most critical benefit of using deployment spaces in a large-scale enterprise environment?

A) Improved model accuracy through hyperparameter tuning
B) Better data labeling quality through automated labeling tools
C) Isolated environments to manage and monitor multiple model versions
D) Faster training times due to streamlined compute resources

Solutions: