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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 (Q22-Q27):

NEW QUESTION # 22
ow do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

A. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
B. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
C. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.
D. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.

Answer: C

NEW QUESTION # 23
Why is it challenging to apply diffusion models to text generation?

A. Because diffusion models can only produce images
B. Because text generation does not require complex models
C. Because text representation is categorical unlike images
D. Because text is not categorical

Answer: C

Explanation:
Diffusion models are primarily used for image generation because they work by incrementally adding noise to a data distribution and then learning to remove it, effectively denoising an image over time. This method works well for continuous data, such as pixel values in images.
However, text is fundamentally categorical, meaning:
Discrete Nature of Text - Unlike images where pixel values change smoothly, text is composed of discrete symbols (words, characters, or tokens), making it difficult to apply continuous noise diffusion.
Tokenization Challenges - Language models work with tokenized words or subwords. Diffusion models would need a way to gradually transition between discrete text tokens, which is not straightforward.
Non-Sequential Nature of Noise Addition - Image-based diffusion models can modify pixel values slightly to learn transformations, but text does not have an equivalent smooth transformation between words.
Alternative Approaches in Text Generation - Due to these challenges, text generation relies more on transformer-based models (like Oracle's AI-driven NLP models), which handle categorical text more effectively than diffusion methods.
🔹 Oracle Generative AI Reference:
Oracle focuses on transformer-based models for text-related AI applications rather than diffusion models, as transformers are more effective in understanding and generating text.

NEW QUESTION # 24
What is the primary purpose of LangSmith Tracing?

A. To monitor the performance of language models
B. To debug issues in language model outputs
C. To generate test cases for language models
D. To analyze the reasoning process of language

Answer: D

NEW QUESTION # 25
Which is the main characteristic of greedy decoding in the context of language model word prediction?

A. It picks the most likely word email at each step of decoding.
B. It requires a large temperature setting to ensure diverse word selection.
C. It selects words bated on a flattened distribution over the vocabulary.
D. It chooses words randomly from the set of less probable candidates.

Answer: A

Explanation:
Greedy decoding in the context of language model word prediction refers to a decoding strategy where, at each step, the model selects the word with the highest probability (the most likely word). This approach is simple and straightforward but can sometimes lead to less diverse or creative outputs because it always opts for the most likely option without considering alternative sequences that might result in better overall sentences.
Reference
Research papers on decoding strategies in language models
Technical documentation on language model inference methods

NEW QUESTION # 26
Which is NOT a typical use case for LangSmith Evaluators?

A. Measuring coherence of generated text
B. Aliening code readability
C. Detecting bias or toxicity
D. Evaluating factual accuracy of outputs

Answer: B

NEW QUESTION # 27
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Updated 1z0-1127-24 Exam Lab Questions | 1z0-1127-24 100% Free Reliable Real Test

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q22-Q27):

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