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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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q15-Q20):

NEW QUESTION # 15
Which statement describes the difference between Top V and Top p" in selecting the next token in the OCI Generative AI Generation models?

A. Top k and Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.
B. Top K considers the sum of probabilities of the top tokens, whereas Top" selects from the Top k" tokens sorted by probability.
C. Top k and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.
D. Top k selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the Top token.

Answer: B

NEW QUESTION # 16
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

A. Stored in Object Storage encrypted by default
B. Stored in an unencrypted form in Object Storage
C. Shared among multiple customers for efficiency
D. Stored in Key Management service

Answer: A

Explanation:
Fine-tuned customer models in the OCI Generative AI service are stored in Object Storage, and they are encrypted by default. This encryption ensures strong data privacy and security by protecting the model data from unauthorized access. Using encrypted storage is a key measure in safeguarding sensitive information and maintaining compliance with security standards.
Reference
OCI documentation on data storage and security practices
Technical details on encryption and data privacy in OCI services

NEW QUESTION # 17
How does the structure of vector databases differ from traditional relational databases?

A. A vector database stores data in a linear or tabular format.
B. It is not optimized for high-dimensional spaces.
C. It uses simple row-based data storage.
D. It is based on distances and similarities in a vector space.

Answer: D

Explanation:
Vector databases are specialized database systems designed to store and retrieve high-dimensional vector embeddings. Unlike traditional relational databases (RDBMS), which organize data into tables with rows and columns, vector databases function using mathematical distances in a multi-dimensional vector space.
How Vector Databases Differ:
Optimized for High-Dimensional Spaces: Designed to efficiently search for similar embeddings in large AI-driven applications (e.g., recommendation systems, image search).
Similarity-Based Retrieval: Uses distance metrics such as cosine similarity, Euclidean distance, or Manhattan distance to find the closest vectors.
Indexing Techniques: Implements approximate nearest neighbor (ANN) algorithms to speed up searches.
Why Other Options Are Incorrect:
(A) is incorrect because vector databases are optimized for high-dimensional spaces.
(C) & (D) are incorrect because vector databases do not use row-based or tabular storage.
🔹 Oracle Generative AI Reference:
Oracle integrates vector databases into its AI and ML solutions, enabling efficient similarity searches and AI-driven applications.

NEW QUESTION # 18
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

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

Answer: B

NEW QUESTION # 19
What does the Loss metric indicate about a model's predictions?

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

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 # 20
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