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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with deploying a custom Watsonx Generative AI model for a client who requires low-latency responses and scalability to handle unpredictable traffic.
Which deployment architecture would best meet the client's requirements?

A) Deploying the model on a single dedicated server
B) Using a serverless architecture with auto-scaling
C) Using a containerized architecture without orchestration
D) Deploying the model in a local on-premise data center

2. You are tasked with optimizing a generative AI model's output for a natural language generation task.
Which of the following combinations of model parameters is most appropriate for encouraging creative and varied responses without sacrificing too much coherence?

A) Temperature = 0.7, Top-p = 0.4, Max tokens = 100, Frequency penalty = 0.9, Presence penalty = 0.3
B) Temperature = 0.5, Top-p = 0.9, Max tokens = 300, Frequency penalty = 0.8, Presence penalty = 0.7
C) Temperature = 1.5, Top-p = 0.8, Max tokens = 150, Frequency penalty = 0.5, Presence penalty = 0.6
D) Temperature = 1.2, Top-p = 1.0, Max tokens = 250, Frequency penalty = 0.3, Presence penalty = 0.2

3. In the context of decoding methods in IBM Watsonx Generative AI, both top-k and top-p sampling are used to control the output of the model.
Which of the following statements correctly distinguishes between top-k and top-p sampling?

A) Top-p sampling ensures deterministic outputs, whereas top-k sampling guarantees random selection from the entire vocabulary.
B) Top-p sampling allows for a more flexible token selection process based on cumulative probabilities, while top-k limits token selection to a fixed number of top choices.
C) Both top-k and top-p sampling ensure that no tokens are selected with a probability below the set threshold, regardless of their context.
D) Top-k sampling samples from a dynamic range of tokens, while top-p sampling restricts choices to a fixed number of tokens.

4. You are tasked with improving the performance of a Retrieval-Augmented Generation (RAG) system in IBM watsonx. Part of this improvement involves selecting the right embedding model for document retrieval.
Which of the following is the best description of the differences between various embedding models, and how would you choose the most suitable model for your task?

A) TF-IDF is an advanced embedding model that captures both the frequency and semantic meaning of words, making it more effective than deep learning-based models like BERT for retrieval in RAG systems.
B) BERT embeddings are context-independent, which makes them less useful for a RAG system than Word2Vec or GloVe, which focus on learning semantic relationships between words.
C) Word2Vec embeddings capture only the syntactic relationships between words, while BERT embeddings focus on both syntax and semantic context, making BERT more suitable for complex retrieval tasks in a RAG system.
D) Word2Vec, GloVe, and BERT are all embedding models, but BERT embeddings capture richer context by considering the entire sentence rather than just the local context, making it more effective for generating semantically relevant embeddings.

5. In the context of Generative AI (GenAI), various embedding models are used to represent textual data.
Which of the following best describes the difference between Word2Vec, BERT, and Sentence-BERT embedding models?

A) Word2Vec uses a transformer architecture for embedding generation, whereas BERT and Sentence-BERT use neural networks to model context.
B) Word2Vec captures both word and sentence meanings in a single vector space, BERT generates only word embeddings, and Sentence-BERT generates embeddings for entire documents.
C) Word2Vec captures contextual relationships between words, while BERT and Sentence-BERT generate sentence-level embeddings based on the overall document length.
D) Word2Vec creates static word embeddings, BERT generates dynamic embeddings based on context, and Sentence-BERT produces embeddings specifically optimized for sentence-level tasks like semantic similarity.

Solutions: