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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic	Details
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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q47-Q52):

NEW QUESTION # 47
What does a cosine distance of 0 indicate about the relationship between two embeddings?

A. They are unrelated
B. They are similar in direction
C. They are completely dissimilar
D. They have the same magnitude

Answer: B

Explanation:
Cosine distance (or cosine similarity) is a metric used to measure the angular similarity between two vectors in high-dimensional space.
Cosine Distance Calculation:
Cosine similarity formula:

The value ranges from -1 to 1:
1 → Vectors are identical.
0 → Vectors are orthogonal (unrelated).
-1 → Vectors are completely opposite.
Why a Cosine Distance of 0 Means Similar Direction:
A cosine similarity of 1 means vectors point in the same direction.
A cosine distance of 0 means maximum similarity (no angular difference).
Why Other Options Are Incorrect:
(A) is incorrect because a cosine distance of 0 implies similarity, not dissimilarity.
(B) is incorrect because unrelated vectors have a cosine similarity close to 0, not exactly 0.
(C) is incorrect because cosine similarity does not measure vector magnitude, only direction.
🔹 Oracle Generative AI Reference:
Oracle's vector search and embedding-based AI models rely on cosine similarity for semantic search, recommendation systems, and NLP tasks.

NEW QUESTION # 48
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

A. It determines the maximum number of tokens the model can generate per response.
B. It com rob the randomness of the model* output, affecting its creativity.
C. It assigns a penalty to frequently occurring tokens to reduce repetitive text.
D. It specifies a string that tells the model to stop generating more content

Answer: D

NEW QUESTION # 49
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Determines the maximum number of tokens the model can generate per response
B. Assigns a penalty to tokens that have already appeared in the preceding text
C. Controls the randomness of the model's output, affecting its creativity
D. Specifies a string that tells the model to stop generating more content

Answer: C

Explanation:
The "temperature" parameter in generative AI models controls the randomness of the model's output. It affects the creativity and diversity of the generated text:
Low temperature: Leads to more deterministic and focused outputs, where the model tends to choose the most probable tokens, resulting in less randomness and creativity.
High temperature: Increases randomness by making the probability distribution over the next tokens flatter. This allows for more diverse and creative outputs, as the model is more likely to choose less probable tokens.
Adjusting the temperature parameter enables fine-tuning the balance between creativity and coherence in the model's responses.
Reference
Research articles on the role of temperature in generative models
Technical guides for tuning generative AI models in OCI

NEW QUESTION # 50
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 is a measure that indicates how wrong the model's predictions are.
D. Loss measures the total number of predictions made by a model.

Answer: C

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 # 51
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

A. It increases the training time as compared to Vanilla fine-tuning.
B. It selectively updates only a fraction of the model's weights.
C. It does not update any weights but restructures the model architecture.
D. It updates all the weights of the model uniformly.

Answer: B

Explanation:
T-Few (Task-Specific Fine-tuning with Few-Shot Learning) is a fine-tuning approach designed to efficiently adapt Large Language Models (LLMs) to new tasks with minimal training data while using a small subset of model weights.
Characteristics of T-Few Fine-Tuning:
Selective Weight Updating: It does not update all model weights but focuses on a small fraction.
Few-Shot Learning Efficiency: Reduces the amount of labeled data required for fine-tuning.
Computational Cost Reduction: Requires significantly less compute than full model fine-tuning.
Better Transferability: Preserves the general knowledge of the base model while adapting to specific tasks.
Why Other Options Are Incorrect:
(B) is incorrect because T-Few updates weights rather than restructuring the model.
(C) is incorrect because not all weights are updated-only a small fraction.
(D) is incorrect because T-Few is optimized for efficiency and does not significantly increase training time.
🔹 Oracle Generative AI Reference:
Oracle AI supports efficient fine-tuning techniques like T-Few and LoRA (Low-Rank Adaptation) to enhance task-specific performance while reducing computational overhead.

NEW QUESTION # 52
......

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Oracle 1z0-1127-24 Exam Syllabus Topics:

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q47-Q52):

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