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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

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

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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q38-Q43):

NEW QUESTION # 38
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

A. Least to most Prompting
B. Step-Bock Prompting
C. Chain-of-Through
D. In context Learning

Answer: C

Explanation:
Chain-of-Thought prompting involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response. This technique helps the model articulate its thought process and reasoning, leading to more transparent and understandable outputs. By breaking down the problem into smaller, logical steps, the model can provide more accurate and detailed responses.
Reference
Research articles on Chain-of-Thought prompting
Technical guides on enhancing model transparency and reasoning with intermediate steps

NEW QUESTION # 39
What does the Loss metric indicate about a model's predictions?

A. Loss describes the accuracy of the right predictions rather than the incorrect ones.
B. Loss indicates how good a prediction is, and it should increase as the model improves.
C. Loss measures the total number of predictions made by a model.
D. Loss is a measure that indicates how wrong the model's predictions are.

Answer: D

Explanation:
In machine learning and AI models, the loss metric quantifies the error between the model's predictions and the actual values.
Definition of Loss:
Loss represents how far off the model's predictions are from the expected output.
The objective of training an AI model is to minimize loss, improving its predictive accuracy.
Loss functions are critical in gradient descent optimization, which updates model parameters.
Types of Loss Functions:
Mean Squared Error (MSE) - Used for regression problems.
Cross-Entropy Loss - Used in classification problems (e.g., NLP tasks).
Hinge Loss - Used in Support Vector Machines (SVMs).
Negative Log-Likelihood (NLL) - Common in probabilistic models.
Clarifying Other Options:
(B) is incorrect because loss does not count the number of predictions.
(C) is incorrect because loss focuses on both right and wrong predictions.
(D) is incorrect because loss should decrease as a model improves, not increase.
🔹 Oracle Generative AI Reference:
Oracle AI platforms implement loss optimization techniques in their training pipelines for LLMs, classification models, and deep learning architectures.

NEW QUESTION # 40
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

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

Answer: D

Explanation:
The "Top p" parameter, also known as nucleus sampling, in generative AI models limits token selection based on the sum of their probabilities. It ensures that the cumulative probability of the selected tokens meets or exceeds a specified threshold p. This approach dynamically includes as many tokens as necessary to reach the desired probability sum, allowing for more diverse and contextually appropriate outputs compared to a fixed top-k selection.
Reference
Research papers on nucleus sampling and token selection methods
OCI Generative AI model documentation

NEW QUESTION # 41
What does "k-shot prompting* refer to when using Large Language Models for task-specific applications?

A. Explicitly providing k examples of the intended task in the prompt to guide the models output
B. Limiting the model to only k possible outcomes or answers for a given task
C. Providing the exact k words in the prompt to guide the model's response
D. The process of training the model on k different tasks simultaneously to improve its versatility

Answer: A

NEW QUESTION # 42
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 loading the entire model into G PU memory for efficient processing
C. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
D. By allocating separate GPUS for each model instance

Answer: C

NEW QUESTION # 43
......

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