01. Which statement is true about string prompt templates and their capability regarding variables?
a) They require a minimum of two variables to function properly.
b) They are unable to use any variables.
c) They can only support a single variable at a time.
d) They support any number of variables, including the possibility of having none.
02. How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?
a) Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.
b) Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.
c) Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.
d) Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
03. In which scenario is soft prompting appropriate compared to other training styles?
a) When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training
b) When the model requires continued pretraining on unlabeled data
c) When the model needs to be adapted to perform well in a domain on which it was not originally trained
d) When there is a significant amount of labeled, task-specific data available
04. Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?
a) It does not update any weights but restructures the model architecture.
b) It updates all the weights of the model uniformly.
c) It selectively updates only a fraction of the model's weights.
d) It increases the training time as compared to Vanilla fine-tuning.
05. What is the purpose of Retrieval Augmented Generation (RAG) in text generation?
a) To store text in an external database without using it for generation
b) To generate text using extra information obtained from an external data source
c) To retrieve text from an external source and present it without any modifications
d) To generate text based only on the model's internal knowledge without external data
06. How are documents usually evaluated in the simplest form of keyword-based search?
a) Based on the number of images and videos contained in the documents
b) By the complexity of language used in the documents
c) Based on the presence and frequency of the user-provided keywords
d) According to the length of the documents
07. When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?
a) When the LLM does not perform well on a task and the data for prompt engineering is too large
b) When the LLM requires access to the latest data for generating outputs
c) When the LLM already understands the topics necessary for text generation
d) When you want to optimize the model without any instructions
08. 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 map vectors to a data structure for faster searching, enabling efficient retrieval
c) To compress vector data for minimized storage usage
d) To categorize vectors based on their originating data type (text, images, audio)
09. Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?
a) Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
b) PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.
c) Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
d) Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.
10. Why is it challenging to apply diffusion models to text generation?
a) Because text generation does not require complex models
b) Because text is not categorical
c) Because text representation is categorical unlike images
d) Because diffusion models can only produce images
The Oracle Cloud Infrastructure Generative AI Professional (1Z0-1127-24) Sample Question Set is designed to help you prepare for the Oracle Cloud Infrastructure 2024 Generative AI Certified Professional certification exam. To become familiar with the actual Oracle Certification exam environment, we suggest you try our Sample Oracle 1Z0-1127-24 Certification Practice Exam.