Free 1z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Professional Exam Questions and Answers

Last updated: September 19, 2026

Exam code

1127-25

Provider

Oracle

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1000+

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First 10, with answers

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1. Question #1

   In the context of generating text with a Large Language Model (LLM), what does the process of greedy decoding entail?

   • A. Selecting a random word from the entire vocabulary at each step
   • B. Picking a word based on its position in a sentence structure
   • C. Choosing the word with the highest probability at each step of decoding
   • D. Using a weighted random selection based on a modulated distribution
   Reveal Answer

2. Question #2

   Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

   • A. Retriever
   • B. Encoder-Decoder
   • C. Generator
   • D. Ranker
   Reveal Answer

3. Question #3

   Which is a key characteristic of the annotation process used in T-Few fine-tuning?

   • A. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
   • B. T-Few fine-tuning requires manual annotation of input-output pairs.
   • C. T-Few fine-tuning involves updating the weights of all layers in the model.
   • D. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
   Reveal Answer

4. Question #4

   What does the RAG Sequence model do in the context of generating a response?

   • A. It retrieves a single relevant document for the entire input query and generates a response based on that alone.
   • B. For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response.
   • C. It retrieves relevant documents only for the initial part of the query and ignores the rest.
   • D. It modifies the input query before retrieving relevant documents to ensure a diverse response.
   Reveal Answer

5. Question #5

   You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training data. How many unit hours are required for fine-tuning if the cluster is active for 10 days?

   • A. 480 unit hours
   • B. 240 unit hours
   • C. 744 unit hours
   • D. 20 unit hours
   Reveal Answer

6. Question #6

   Why is normalization of vectors important before indexing in a hybrid search system?

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

7. Question #7

   What is the characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

   • A. It updates all the weights of the model uniformly.
   • B. It selectively updates only a fraction of weights to reduce the number of parameters.
   • C. It selectively updates only a fraction of weights to reduce computational load and avoid overfitting.
   • D. It increases the training time as compared to Vanilla fine-tuning.
   Reveal Answer

8. Question #8

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

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

9. Question #9

   Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

   • A. "Top p" selects tokens from the "Top k" tokens sorted by probability.
   • B. "Top p" assigns penalties to frequently occurring tokens.
   • C. "Top p" limits token selection based on the sum of their probabilities.
   • D. "Top p" determines the maximum number of tokens per response.
   Reveal Answer

10. Question #10

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

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