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1Z0-1127-25 - Oracle Cloud Infrastructure 2025 Generative AI Professional–Reliab
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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q34-Q39):NEW QUESTION # 34
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?
- A. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
- B. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.
- C. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.
- D. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In RAG, "Groundedness" assesses whether the response is factually correct and supported by retrieved data, while "Answer Relevance" evaluates how well the response addresses the user's query. Option A captures this distinction accurately. Option B is off-groundedness isn't just contextual alignment, and relevance isn't about syntax. Option C swaps the definitions. Option D misaligns-groundedness isn't solely data integrity, and relevance isn't lexical diversity. This distinction ensures RAG outputs are both true and pertinent.
OCI 2025 Generative AI documentation likely defines these under RAG evaluation metrics.
NEW QUESTION # 35
What is the purpose of frequency penalties in language model outputs?
- A. To randomly penalize some tokens to increase the diversity of the text
- B. To penalize tokens that have already appeared, based on the number of times they have been used
- C. To reward the tokens that have never appeared in the text
- D. To ensure that tokens that appear frequently are used more often
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Frequency penalties reduce the likelihood of repeating tokens that have already appeared in the output, based on their frequency, to enhance diversity and avoid repetition. This makes Option B correct. Option A is the opposite effect. Option C describes a different mechanism (e.g., presence penalty in some contexts). Option D is inaccurate, as penalties aren't random but frequency-based.
OCI 2025 Generative AI documentation likely covers frequency penalties under output control parameters.
Below is the next batch of 10 questions (11-20) from your list, formatted as requested with detailed explanations. These answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.
NEW QUESTION # 36
In the context of generating text with a Large Language Model (LLM), what does the process of greedy decoding entail?
- A. Choosing the word with the highest probability at each step of decoding
- B. Picking a word based on its position in a sentence structure
- C. Selecting a random word from the entire vocabulary at each step
- D. Using a weighted random selection based on a modulated distribution
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Greedy decoding selects the word with the highest probability at each step, aiming for locally optimal choices without considering future tokens. This makes Option C correct. Option A (random selection) describes sampling, not greedy decoding. Option B (position-based) isn't how greedy decoding works-it's probability-driven. Option D (weighted random) aligns with top-k or top-p sampling, not greedy. Greedy decoding is fast but can lack diversity.
OCI 2025 Generative AI documentation likely explains greedy decoding under decoding strategies.
NEW QUESTION # 37
Which is NOT a typical use case for LangSmith Evaluators?
- A. Aligning code readability
- B. Evaluating factual accuracy of outputs
- C. Measuring coherence of generated text
- D. Detecting bias or toxicity
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangSmith Evaluators assess LLM outputs for qualities like coherence (A), factual accuracy (C), and bias/toxicity (D), aiding development and debugging. Aligning code readability (B) pertains to software engineering, not LLM evaluation, making it the odd one out-Option B is correct as NOT a use case. Options A, C, and D align with LangSmith's focus on text quality and ethics.
OCI 2025 Generative AI documentation likely lists LangSmith Evaluator use cases under evaluation tools.
NEW QUESTION # 38
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?
- A. A Retrieval Augmented Generation (RAG) model that uses text as input and output
- B. A diffusion model that specializes in producing complex outputs.
- C. A language model that operates on a token-by-token output basis
- D. A Large Language Model-based agent that focuses on generating textual responses
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
The task requires bidirectional text-image capabilities: analyzing images to generate text and generating images from text. Diffusion models (e.g., Stable Diffusion) excel at complex generative tasks, including text-to-image and image-to-text with appropriate extensions, making Option A correct. Option B (LLM) is text-only. Option C (token-based LLM) lacks image handling. Option D (RAG) focuses on text retrieval, not image generation. Diffusion models meet both needs.
OCI 2025 Generative AI documentation likely discusses diffusion models under multimodal applications.
NEW QUESTION # 39
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