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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 (Q55-Q60):

NEW QUESTION # 55
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?

A. Translation models
B. Generation models
C. Summarization models
D. Embedding models

Answer: A

Explanation:
In the OCI Generative AI service, the categories of pre-trained foundational models available include Summarization models, Generation models, and Embedding models. However, Translation models are not listed as a category of pre-trained foundational models available in OCI Generative AI service. The service focuses on providing models that support text generation, summarization, and embedding tasks.
Reference
OCI Generative AI service documentation
Listings and descriptions of pre-trained foundational models in OCI

NEW QUESTION # 56
In the context of generating text with a Large Language Model (LLM), what does the process of greedy decoding entail?

A. Selecting a random word from the entire vocabulary at each step
B. Using a weighted random selection based on a modulated distribution
C. Picking a word based on its position in a sentence structure
D. Choosing the word with the highest probability at each step of decoding

Answer: D

Explanation:
Greedy Decoding is a simple and fast text generation strategy where the model always selects the word with the highest probability at each step.
How Greedy Decoding Works:
At each step of text generation, the model picks the most probable next word.
No consideration is given to long-term coherence or fluency.
This method can lead to repetitive and suboptimal outputs due to the lack of exploration.
Limitations of Greedy Decoding:
May miss optimal sentence structures because it only considers the next word, not the full sequence.
Less diversity in generated text, as it follows the highest-probability path rigidly.
Better alternatives exist: Beam Search, Top-k Sampling, and Temperature Scaling provide more refined results.
Why Other Options Are Incorrect:
(A) is incorrect because greedy decoding does not select random words.
(C) is incorrect because word choice is based on probability, not sentence structure.
(D) is incorrect because weighted random selection is used in sampling methods like Top-k or Top-p (nucleus sampling).
🔹 Oracle Generative AI Reference:
Oracle AI incorporates Greedy Decoding, Beam Search, and Stochastic Sampling in its text generation models to optimize for accuracy and diversity.

NEW QUESTION # 57
Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?

A. RetrievalQA
B. Text Leader
C. GenerativeAI
D. Chain Deployment

Answer: C

NEW QUESTION # 58
What does accuracy measure in the context of fine-tuning results for a generative model?

A. The number of predictions a model makes, regardless of whether they are correct or incorrect
B. The proportion of incorrect predictions made by the model during an evaluation
C. How many predictions the model made correctly out of all the predictions in an evaluation
D. The depth of the neural network layers used in the model

Answer: C

Explanation:
Accuracy in machine learning measures the proportion of correct predictions made by a model relative to the total predictions during an evaluation.
How Accuracy is Calculated:

A higher accuracy indicates better model performance.
Used primarily in classification tasks, but it can also assess LLM fine-tuning results.
Why Other Options Are Incorrect:
(A) is incorrect because the number of neural network layers does not define accuracy.
(B) is incorrect because accuracy considers correctness, not just total predictions.
(D) is incorrect because accuracy measures correct predictions, not just incorrect ones.
🔹 Oracle Generative AI Reference:
Oracle AI assesses model fine-tuning performance using accuracy, loss, and perplexity to improve LLM capabilities.

NEW QUESTION # 59
In the simplified workflow for managing and querying vector data, what is the role of indexing?

A. To convert vectors into a nonindexed format for easier retrieval
B. To categorize vectors based on their originating data type (text, images, audio)
C. To compress vector data for minimized storage usage
D. To map vectors to a data structure for faster searching, enabling efficient retrieval

Answer: D

Explanation:
Vector indexing plays a crucial role in vector search and retrieval systems, particularly in AI-driven databases. The key functions of vector indexing include:
Efficient Search and Retrieval - Vector indexing structures (such as HNSW, FAISS, or Annoy) help organize vector embeddings to enable fast retrieval of similar vectors.
Mapping to Searchable Data Structures - The process involves creating indexes that efficiently store and map vectors, reducing computational overhead when searching for similar embeddings.
Handling High-Dimensional Data - Since vector embeddings (used in NLP, image recognition, etc.) are often high-dimensional, indexing helps compress and cluster similar vectors, improving retrieval speed.
Used in Vector Databases - Many AI applications, including Oracle's AI-driven database solutions, use indexing techniques for faster similarity searches.
🔹 Oracle Generative AI Reference:
Oracle integrates vector search within its AI and database services, allowing enterprises to efficiently manage and retrieve vectorized data.

NEW QUESTION # 60
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