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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

While developing a Retrieval-Augmented Generation (RAG) system using the transformers library, you want to improve the retrieval quality by ensuring that your queries and documents are represented in the same latent space for effective similarity matching.
Which of the following techniques would be the most appropriate to ensure this alignment between queries and documents?

A. Use different transformer models for documents and queries, and normalize their embeddings to align them in the same latent space.
B. Use a pre-trained BERT model to encode the documents and a pre-trained GPT model to encode the queries, ensuring diversity in embeddings.
C. Use a randomly initialized transformer model to encode both documents and queries for unbiased similarity calculation.
D. Fine-tune a transformer model on a document-query similarity task, so that both queries and documents are encoded into the same vector space for retrieval.

Question 2

In a project where watsonx.ai is deployed as the core generative model for text generation, you need to augment its capabilities by integrating it with IBM Watson Discovery to access a large corpus of documents for fact-checking and data retrieval.
What is the best way to integrate these two services to ensure that generated responses are both creative and factual?

A. Use watsonx.ai to generate all responses, and periodically train the model using data indexed by Watson Discovery.
B. Have Watson Discovery preprocess user inputs to identify relevant documents, and then feed the retrieved information into watsonx.ai as a context for text generation.
C. Train watsonx.ai to automatically search Watson Discovery for factual information during the text generation process, eliminating the need for an external integration layer.
D. Deploy a custom API layer that combines watsonx.ai with Watson Assistant, letting the latter manage queries and retrieve documents from Watson Discovery on demand.

Question 3

Which quantization technique aims to optimize a model by converting weights and activations into 8-bit integers while minimizing the impact on the model's performance?

A. Post-training static quantization
B. Post-training dynamic quantization
C. Quantization-aware training (QAT)
D. Hybrid quantization

Question 4

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

Question 5

You are working on a Retrieval-Augmented Generation (RAG) system where large-scale document retrieval is a critical component. To improve the efficiency and accuracy of retrieval, you need to store and query vector embeddings. Given that the system needs to handle billions of high-dimensional embeddings while maintaining low latency for search queries, you are evaluating the use of a vector database.
Which of the following databases would be the most appropriate choice for this purpose, and why?

A. A vector database like Pinecone or Weaviate that supports approximate nearest neighbor (ANN) search.
B. A graph database like Neo4j, which is designed for traversing relationships between data points.
C. Relational databases with B-tree indexes.
D. A document-based NoSQL database like MongoDB, utilizing full-text search capabilities.

Solutions: