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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 (Q52-Q57):

NEW QUESTION # 52
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

A. The token will be the only one considered in the next generation step.
B. The token is more likely to follow the current token.
C. The token is unrelated to the current token and will not be used.
D. The token is less likely to follow the current token.

Answer: B

Explanation:
In the "Show Likelihoods" feature of language model token generation, a higher number assigned to a token indicates that the token is more likely to follow the current token. This likelihood is based on the model's probability distribution, where tokens with higher probabilities are considered more likely to be the next in the sequence. This feature helps in understanding the model's decision-making process and the relative probabilities of different tokens.
Reference
Technical documentation on language model token generation
Research articles on probability distributions in generative models

NEW QUESTION # 53
Why is it challenging to apply diffusion models to text generation?

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

Answer: B

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 # 54
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

A. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
B. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
C. The level of incorrectness in the models predictions, with lower values indicating better performance
D. The improvement in accuracy achieved by the model during training on the user-uploaded data set

Answer: C

NEW QUESTION # 55
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

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

Answer: C

Explanation:
Dot Product and Cosine Distance are both metrics used to compare text embeddings, but they operate differently:
Dot Product: Measures the magnitude and direction of the vectors. It takes into account both the size (magnitude) and the angle (direction) between the vectors. This can result in higher similarity scores for longer vectors, even if they point in similar directions.
Cosine Distance: Focuses on the orientation of the vectors regardless of their magnitude. It measures the cosine of the angle between two vectors, which normalizes the vectors to unit length. This makes it a measure of the angle (or orientation) between the vectors, providing a similarity score that is independent of the vector lengths.
Reference
Research papers on text embedding comparison metrics
Technical documentation on vector similarity measures

NEW QUESTION # 56
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 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 describes the accuracy of the right predictions rather than the incorrect ones.

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 # 57
......

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