What does the Ranker do in a text generation system?
A. It generates the final text based on the user's query.
B. It sources information from databases to use in text generation.
C. It evaluates and prioritizes the information retrieved by the Retriever.
D. It interacts with the user to understand the query better.
Explanation:
Comprehensive and Detailed In-Depth Explanation:
In systems like RAG, the Ranker evaluates and sorts the information retrieved by the
Retriever (e.g., documents or snippets) based on relevance to the query, ensuring the most
pertinent data is passed to the Generator. This makes Option C correct. Option A is the
Generator’s role. Option B describes the Retriever. Option D is unrelated, as the Ranker
doesn’t interact with users but processes retrieved data. The Ranker enhances output
quality by prioritizing relevant content.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline
components.
What is the function of the Generator in a text generation system?
A. To collect user queries and convert them into database search terms
B. To rank the information based on its relevance to the user's query
C. To generate human-like text using the information retrieved and ranked, along with the user's original query
D. To store the generated responses for future use
Explanation:
Comprehensive and Detailed In-Depth Explanation:
In a text generation system (e.g., with RAG), the Generator is the component (typically an
LLM) that produces coherent, human-like text based on the user’s query and any retrieved
information (if applicable). It synthesizes the final output, making Option C correct. Option A
describes a Retriever’s role. Option B pertains to a Ranker. Option D is unrelated, as
storage isn’t the Generator’s function but a separate system task. The Generator’s role is critical in transforming inputs into natural language responses.
OCI 2025 Generative AI documentation likely defines the Generator under RAG or text
generation workflows.
How are documents usually evaluated in the simplest form of keyword-based search?
A. By the complexity of language used in the documents
B. Based on the number of images and videos contained in the documents
C. Based on the presence and frequency of the user-provided keywords
D. According to the length of the documents
Explanation:
Comprehensive and Detailed In-Depth Explanation:
In basic keyword-based search, documents are evaluated by matching user-provided
keywords, with relevance often determined by their presence and frequency (e.g., term
frequency in TF-IDF). This makes Option C correct. Option A (language complexity) is
unrelated to simple keyword search. Option B (multimedia) isn’t considered in text-based
keyword methods. Option D (length) may influence scoring indirectly but isn’t the primary
metric. Keyword search prioritizes exact matches.
OCI 2025 Generative AI documentation likely contrasts keyword search with semantic
search under retrieval methods.
Which is a key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service?
A. Reduced model complexity
B. Enhanced generalization to unseen data
C. Increased model interpretability
D. Faster training time and lower cost
Explanation:
Comprehensive and Detailed In-Depth Explanation:
T-Few, a Parameter-Efficient Fine-Tuning method, updates fewer parameters than Vanilla
fine-tuning, leading to faster training and lower computational costs—Option D is correct.
Option A (complexity) isn’t directly affected—structure remains. Option B (generalization)
may occur but isn’t the primary advantage. Option C (interpretability) isn’t a focus.
Efficiency is T-Few’s hallmark.
OCI 2025 Generative AI documentation likely compares T-Few and Vanilla under finetuning
benefits.
An LLM emits intermediate reasoning steps as part of its responses. Which of the following techniques is being utilized?
A. In-context Learning
B. Step-Back Prompting
C. Least-to-Most Prompting
D. Chain-of-Thought
Explanation:
Comprehensive and Detailed In-Depth Explanation:
Chain-of-Thought (CoT) prompting encourages an LLM to emit intermediate reasoning
steps before providing a final answer, improving performance on complex tasks by
mimicking human reasoning. This matches the scenario, making Option D correct. Option
A (In-context Learning) involves learning from examples in the prompt, not necessarily
reasoning steps. Option B (Step-Back Prompting) involves reframing the problem, not
emitting steps. Option C (Least-to-Most Prompting) breaks tasks into subtasks but doesn’t
focus on intermediate reasoning explicitly. CoT is widely recognized for reasoning tasks.
OCI 2025 Generative AI documentation likely covers Chain-of-Thought under advanced
prompting techniques.
What is the purpose of frequency penalties in language model outputs?
A. To ensure that tokens that appear frequently are used more often
B. To penalize tokens that have already appeared, based on the number of times they have been used
C. To reward the tokens that have never appeared in the text
D. To randomly penalize some tokens to increase the diversity of the text
Explanation:
Comprehensive and Detailed In-Depth Explanation:
Frequency penalties reduce the likelihood of repeating tokens that have already appeared
in the output, based on their frequency, to enhance diversity and avoid repetition. This
makes Option B correct. Option A is the opposite effect. Option C describes a different
mechanism (e.g., presence penalty in some contexts). Option D is inaccurate, as penalties
aren’t random but frequency-based.
OCI 2025 Generative AI documentation likely covers frequency penalties under output
control parameters.
Below is the next batch of 10 questions (11–20) from your list, formatted as requested with
detailed explanations. These answers are based on widely accepted principles in
generative AI and Large Language Models (LLMs), aligned with what is likely reflected in
the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical
errors have been corrected for clarity.
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
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 improvement in accuracy achieved by the model during training on the useruploaded dataset
D. The level of incorrectness in the model’s predictions, with lower values indicating better performance
Explanation:
Comprehensive and Detailed In-Depth Explanation:
Loss measures the discrepancy between a model’s predictions and true values, with lower
values indicating better fit—Option D is correct. Option A (accuracy difference) isn’t
loss—it’s a derived metric. Option B (error percentage) is closer to error rate, not loss.
Option C (accuracy improvement) is a training outcome, not loss’s definition. Loss is a
fundamental training signal.
OCI 2025 Generative AI documentation likely defines loss under fine-tuning metrics.
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training data. How many unit hours are required for fine-tuning if the cluster is active for 10 hours?
A. 25 unit hours
B. 40 unit hours
C. 20 unit hours
D. 30 unit hours
Explanation:
Comprehensive and Detailed In-Depth Explanation:
In OCI, unit hours typically equal actual hours of cluster activity unless specified otherwise
(e.g., per GPU scaling). For 10 hours of activity, it’s 10 hours × 1 unit/hour = 10 unit hours,
but options suggest a multiplier (common in cloud pricing). Assuming a standard 2-
unit/hour rate (e.g., for GPU clusters), it’s 10 × 2 = 20 unit hours—Option C fits best.
Options A, B, and D imply inconsistent rates (2.5, 4, 3).
OCI 2025 Generative AI documentation likely specifies unit hour rates under Dedicated AI
Cluster pricing.
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. Picking a word based on its position in a sentence structure
C. Choosing the word with the highest probability at each step of decoding
D. Using a weighted random selection based on a modulated distribution
Explanation:
Comprehensive and Detailed In-Depth Explanation:
Greedy decoding selects the word with the highest probability at each step, aiming for
locally optimal choices without considering future tokens. This makes Option C correct.
Option A (random selection) describes sampling, not greedy decoding. Option B (positionbased)
isn’t how greedy decoding works—it’s probability-driven. Option D (weighted
random) aligns with top-k or top-p sampling, not greedy. Greedy decoding is fast but can
lack diversity.
OCI 2025 Generative AI documentation likely explains greedy decoding under decoding
strategies.
When should you use the T-Few fine-tuning method for training a model?
A. For complicated semantic understanding improvement
B. For models that require their own hosting dedicated AI cluster
C. For datasets with a few thousand samples or less
D. For datasets with hundreds of thousands to millions of samples
Explanation:
Comprehensive and Detailed In-Depth Explanation:
T-Few is ideal for smaller datasets (e.g., a few thousand samples) where full fine-tuning
risks overfitting and is computationally wasteful—Option C is correct. Option A (semantic
understanding) is too vague—dataset size matters more. Option B (dedicated cluster) isn’t
a condition for T-Few. Option D (large datasets) favors Vanilla fine-tuning. T-Few excels in
low-data scenarios.
OCI 2025 Generative AI documentation likely specifies T-Few use cases under fine-tuning
guidelines.
Which statement best describes the role of encoder and decoder models in natural language processing?
A. Encoder models and decoder models both convert sequences of words into vector representations without generating new text.
B. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
C. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to generate a sequence of words.
D. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
Explanation:
Comprehensive and Detailed In-Depth Explanation:
In NLP (e.g., transformers), encoders convert input text into a vector representation
(encoding meaning), while decoders generate text from such vectors (e.g., in translation or
generation). This makes Option C correct. Option A is false—decoders generate text.
Option B reverses roles—encoders don’t predict next words, and decoders don’t encode.
Option D oversimplifies—encoders handle text, not just numbers. This is the foundation of
seq2seq models.
OCI 2025 Generative AI documentation likely explains encoder-decoder roles under model
architecture.
How does a presence penalty function in language model generation?
A. It penalizes all tokens equally, regardless of how often they have appeared.
B. It penalizes only tokens that have never appeared in the text before.
C. It applies a penalty only if the token has appeared more than twice.
D. It penalizes a token each time it appears after the first occurrence.
Explanation:
Comprehensive and Detailed In-Depth Explanation:
A presence penalty reduces the probability of tokens that have already appeared in the
output, applying the penalty each time they reoccur after their first use, to discourage
repetition. This makes Option D correct. Option A (equal penalties) ignores prior
appearance. Option B is the opposite—penalizing unused tokens isn’t the intent. Option C
(more than twice) adds an arbitrary threshold not typically used. Presence penalty
enhances output variety.
OCI 2025 Generative AI documentation likely details presence penalty under generation
control parameters.
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