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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q61-Q66):

NEW QUESTION # 61
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?

A. Translation models
B. Embedding models
C. Summarization models
D. Generation models

Answer: A

NEW QUESTION # 62
Which is NOT a built-in memory type in LangChain?

A. Conversation ImgeMemory
B. Conversation Token Buffer Memory
C. Conversation Buffer Memory
D. Conversation Summary Memory

Answer: A

NEW QUESTION # 63
Why is normalization of vectors important before indexing in a hybrid search system?

A. It significantly reduces the size of the database.
B. It ensures that all vectors represent keywords only.
C. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.
D. It converts all sparse vectors to dense vectors.

Answer: C

Explanation:
Normalization of vectors is crucial in a hybrid search system because it standardizes the lengths of vectors, ensuring they have a unit norm. This standardization is essential for meaningful comparison using similarity metrics such as Cosine Similarity. Without normalization, the magnitudes of vectors could skew the similarity scores, leading to inaccurate comparisons and search results. Normalizing vectors ensures that the similarity measure focuses purely on the direction of the vectors rather than their magnitude.
Reference
Research papers on vector normalization in information retrieval
Technical documentation on hybrid search systems

NEW QUESTION # 64
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

A. The token will be the only one considered in the next generation step.
B. The token is more likely to follow the current token.
C. The token is unrelated to the current token and will not be used.
D. The token is less likely to follow the current token.

Answer: B

NEW QUESTION # 65
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

A. By loading the entire model into G PU memory for efficient processing
B. By allocating separate GPUS for each model instance
C. By optimizing GPIJ memory utilization for each model's unique para
D. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs

Answer: D

Explanation:
The architecture of dedicated AI clusters contributes to minimizing GPU memory overhead for fine-tuned model inference by sharing base model weights across multiple fine-tuned models on the same group of GPUs. This approach allows different fine-tuned models to leverage the shared base model weights, reducing the memory requirements and enabling efficient use of GPU resources. By not duplicating the base model weights for each fine-tuned model, the system can handle more models simultaneously with lower memory overhead.
Reference
Technical documentation on AI cluster architectures
Research articles on optimizing GPU memory utilization in model inference

NEW QUESTION # 66
......

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