LATEST 1Z0-1127-24 EXAM ONLINE, 1Z0-1127-24 RELIABLE BRAINDUMPS QUESTIONS

Latest 1z0-1127-24 Exam Online, 1z0-1127-24 Reliable Braindumps Questions

Latest 1z0-1127-24 Exam Online, 1z0-1127-24 Reliable Braindumps Questions

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2025 Oracle 1z0-1127-24: Oracle Cloud Infrastructure 2024 Generative AI Professional Accurate Latest Exam Online

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.
Topic 2
  • Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.
Topic 3
  • Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.

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

NEW QUESTION # 61
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of- Thought, Least-to-most, or Step-Back prompting technique.
L Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50.
2. Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question.
3. To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, well explore how they trap heat in the Earths atmosphere.

  • A. 1:Chain-of-Thought ,2:Step-Back, 3:Least-to most
  • B. 1:Least-to-most, 2 Chain-of-Thought, 3:Step-Back
  • C. 1:Step-Back, 2:Chain-of-Thought, 3:Least-to-most
  • D. 1:Chain-of-throught, 2: Least-to-most, 3:Step-Back

Answer: D

Explanation:
Chain-of-Thought: The first prompt calculates the total number of wheels and then uses that information to determine how many sets of wheels can be bought. This sequential reasoning process aligns with the Chain-of-Thought technique.
Least-to-most: The second prompt solves a complex problem by first identifying the needed formula and then solving a simpler version before tackling the full question. This incremental approach matches the Least-to-most technique.
Step-Back: The third prompt starts by defining greenhouse gases and then explores their impact on climate change, taking a step back to establish foundational knowledge before addressing the main question.
Reference
Research articles on prompting techniques for language models
Documentation on effective use of prompting strategies


NEW QUESTION # 62
Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

  • A. Reduced model complexity
  • B. Enhanced generalization to unseen data
  • C. Foster training time and lower cost
  • D. Increased model interpretability

Answer: C


NEW QUESTION # 63
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

  • A. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
  • B. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies
  • C. PEFT involves only a few or new parameters and uses labeled, task-specific data.
  • D. PEFT parameters and b typically used when no training data exists.

Answer: C


NEW QUESTION # 64
Which is the main characteristic of greedy decoding in the context of language model word prediction?

  • A. It chooses words randomly from the set of less probable candidates.
  • B. It selects words bated on a flattened distribution over the vocabulary.
  • C. It requires a large temperature setting to ensure diverse word selection.
  • D. It picks the most likely word email at each step of decoding.

Answer: D

Explanation:
Greedy decoding in the context of language model word prediction refers to a decoding strategy where, at each step, the model selects the word with the highest probability (the most likely word). This approach is simple and straightforward but can sometimes lead to less diverse or creative outputs because it always opts for the most likely option without considering alternative sequences that might result in better overall sentences.
Reference
Research papers on decoding strategies in language models
Technical documentation on language model inference methods


NEW QUESTION # 65
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

  • A. Underfitting
  • B. Model Drift
  • C. Data Leakage
  • D. Overfilling

Answer: D


NEW QUESTION # 66
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