EXAM ORACLE 1Z0-1127-25 PRACTICE | LATEST 1Z0-1127-25 REAL TEST

Exam Oracle 1Z0-1127-25 Practice | Latest 1Z0-1127-25 Real Test

Exam Oracle 1Z0-1127-25 Practice | Latest 1Z0-1127-25 Real Test

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q26-Q31):

NEW QUESTION # 26
When does a chain typically interact with memory in a run within the LangChain framework?

  • A. Before user input and after chain execution.
  • B. Only after the output has been generated.
  • C. After user input but before chain execution, and again after core logic but before output.
  • D. Continuously throughout the entire chain execution process.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, a chain interacts with memory after receiving user input (to load prior context) but before execution (to inform the process), and again after the core logic (to update memory with new context) but before the final output. This ensures context continuity, making Option C correct. Option A is too late, missing pre-execution context. Option B is misordered. Option D overstates interaction, as it's not continuous but at specific points. Memory integration is key for stateful chains.
OCI 2025 Generative AI documentation likely details memory interaction under LangChain workflows.


NEW QUESTION # 27
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 days?

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

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, a dedicated AI cluster's usage is typically measured in unit hours, where 1 unit hour = 1 hour of cluster activity. For 10 days, assuming 24 hours per day, the calculation is: 10 days × 24 hours/day = 240 hours. Thus, Option B (240 unit hours) is correct. Option A (480) might assume multiple clusters or higher rates, but the question specifies one cluster. Option C (744) approximates a month (31 days), not 10 days. Option D (20) is arbitrarily low.
OCI 2025 Generative AI documentation likely specifies unit hour calculations under Dedicated AI Cluster pricing.


NEW QUESTION # 28
What do embeddings in Large Language Models (LLMs) represent?

  • A. The semantic content of data in high-dimensional vectors
  • B. The frequency of each word or pixel in the data
  • C. The grammatical structure of sentences in the data
  • D. The color and size of the font in textual data

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Embeddings in LLMs are high-dimensional vectors that encode the semantic meaning of words, phrases, or sentences, capturing relationships like similarity or context (e.g., "cat" and "kitten" being close in vector space). This allows the model to process and understand text numerically, making Option C correct. Option A is irrelevant, as embeddings don't deal with visual attributes. Option B is incorrect, as frequency is a statistical measure, not the purpose of embeddings. Option D is partially related but too narrow-embeddings capture semantics beyond just grammar.
OCI 2025 Generative AI documentation likely discusses embeddings under data representation or vectorization topics.


NEW QUESTION # 29
Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?

  • A. They cannot generate responses without fine-tuning.
  • B. They always use an external database for generating responses.
  • C. They rely on internal knowledge learned during pretraining on a large text corpus.
  • D. They use vector databases exclusively to produce answers.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LLMs without Retrieval Augmented Generation (RAG) depend solely on the knowledge encoded in their parameters during pretraining on a large, general text corpus. They generate responses basedon this internal knowledge without accessing external data at inference time, making Option B correct. Option A is false, as external databases are a feature of RAG, not standalone LLMs. Option C is incorrect, as LLMs can generate responses without fine-tuning via prompting or in-context learning. Option D is wrong, as vector databases are used in RAG or similar systems, not in basic LLMs. This reliance on pretraining distinguishes non-RAG LLMs from those augmented with real-time retrieval.
OCI 2025 Generative AI documentation likely contrasts RAG and non-RAG LLMs under model architecture or response generation sections.


NEW QUESTION # 30
What do prompt templates use for templating in language model applications?

  • A. Python's list comprehension syntax
  • B. Python's str.format syntax
  • C. Python's lambda functions
  • D. Python's class and object structures

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates in LLM applications (e.g., LangChain) typically use Python's str.format() syntax to insert variables into predefined string patterns (e.g., "Hello, {name}!"). This makes Option B correct. Option A (list comprehension) is for list operations, not templating. Option C (lambda functions) defines functions, not templates. Option D (classes/objects) is overkill-templates are simpler constructs. str.format() ensures flexibility and readability.
OCI 2025 Generative AI documentation likely mentions str.format() under prompt template design.


NEW QUESTION # 31
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