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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

Topic 1

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.

Topic 2

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 3

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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q39-Q44):

NEW QUESTION # 39
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:Step-Back, 2:Chain-of-Thought, 3:Least-to-most
C. 1:Least-to-most, 2 Chain-of-Thought, 3:Step-Back
D. 1:Chain-of-throught, 2: Least-to-most, 3:Step-Back

Answer: B

NEW QUESTION # 40
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

A. By optimizing GPIJ memory utilization for each model's unique para
B. By allocating separate GPUS for each model instance
C. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
D. By loading the entire model into G PU memory for efficient processing

Answer: C

Explanation:
The architecture of dedicated AI clusters contributes to minimizing GPU memory overhead for fine-tuned model inference by sharing base model weights across multiple fine-tuned models on the same group of GPUs. This approach allows different fine-tuned models to leverage the shared base model weights, reducing the memory requirements and enabling efficient use of GPU resources. By not duplicating the base model weights for each fine-tuned model, the system can handle more models simultaneously with lower memory overhead.
Reference
Technical documentation on AI cluster architectures
Research articles on optimizing GPU memory utilization in model inference

NEW QUESTION # 41
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

Answer: D

Explanation:
Soft prompting is an efficient method for modifying LLM behavior without full retraining. Unlike fine-tuning, soft prompting adds learnable embeddings (soft prompts) to guide the model.
When Soft Prompting is Useful:
Enhances model behavior without full retraining.
Uses small trainable prompt tokens, avoiding large parameter updates.
Works well when labeled, task-specific data is unavailable.
Why Other Options Are Incorrect:
(A) is incorrect because continued pretraining involves modifying core model weights.
(C) is incorrect because adapting a model to a new domain is better suited to fine-tuning or full retraining.
(D) is incorrect because soft prompting is designed for low-data scenarios, while full fine-tuning requires labeled datasets.
🔹 Oracle Generative AI Reference:
Oracle AI supports efficient adaptation methods, including soft prompting and LoRA, to improve LLM flexibility.

NEW QUESTION # 42
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

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

Answer: D

NEW QUESTION # 43
What does a dedicated RDMA cluster network do during model fine-tuning and inference?

A. It leads to higher latency in model inference.
B. It increases G PU memory requirements for model deployment.
C. It enables the deployment of multiple fine-tuned models.
D. It limits the number of fine-tuned model deployable on the same GPU cluster.

Answer: C

NEW QUESTION # 44
......

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