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Oracle Cloud Infrastructure 2025 Generative AI Professional - 1Z0-1127-25 Exam Practice Test

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Total 90 questions

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Question 1

How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

A. Temperature has no effect on the probability distribution; it only changes the speed of decoding. B. Decreasing temperature broadens the distribution, making less likely words more probable. C. Increasing temperature flattens the distribution, allowing for more varied word choices. D. Increasing temperature removes the impact of the most likely word.

Discussion 0

Correct Answer: C Vote an answer

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Question 2

What is prompt engineering in the context of Large Language Models (LLMs)?

A. Adjusting the hyperparameters of the model B. Iteratively refining the ask to elicit a desired response C. Adding more layers to the neural network D. Training the model on a large dataset

Discussion 0

Correct Answer: B Vote an answer

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Question 3

How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?

A. Dot Product measures the magnitude and direction of vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude. B. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance. C. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons. D. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.

Discussion 0

Correct Answer: A Vote an answer

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Question 4

Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks?

A. The GPUs allocated for a customer's generative AI tasks are isolated from other GPUs. B. Each customer's GPUs are connected via a public Internet network for ease of access. C. GPUs are used exclusively for storing large datasets, not for computation. D. GPUs are shared with other customers to maximize resource utilization.

Discussion 0

Correct Answer: A Vote an answer

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Question 5

What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

A. Support for tokenizing longer sentences B. Improved retrievals for Retrieval Augmented Generation (RAG) systems C. Capacity to translate text in over 100 languages D. Emphasis on syntactic clustering of word embeddings

Discussion 0

Correct Answer: B Vote an answer

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Question 6

Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship. What is the nature of these relationships, and why arethey crucial for language models?

A. Semantic relationships; crucial for understanding context and generating precise language B. Hierarchical relationships; important for structuring database queries C. Linear relationships; they simplify the modeling process D. Temporal relationships; necessary for predicting future linguistic trends

Discussion 0

Correct Answer: A Vote an answer

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Question 7

In which scenario is soft prompting appropriate compared to other training styles?

A. When the model needs to be adapted to perform well in a domain on which it was not originally trained B. When the model requires continued pretraining on unlabeled data C. When there is a significant amount of labeled, task-specific data available D. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training

Discussion 0

Correct Answer: D Vote an answer

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Question 8

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 less likely to follow the current token. C. The token is more likely to follow the current token. D. The token is unrelated to the current token and will not be used.

Discussion 0

Correct Answer: C Vote an answer

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Question 9

When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

A. When the LLM already understands the topics necessary for text generation B. When the LLM does not perform well on a task and the data for prompt engineering is too large C. When you want to optimize the model without any instructions D. When the LLM requires access to the latest data for generating outputs

Discussion 0

Correct Answer: B Vote an answer

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Total 90 questions

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