Practice Free 1z0-1127-25 Oracle Cloud Infrastructure 2025 Generative AI Professional Exam Questions Answers With Explanation

Comprehensive and Detailed In-Depth Explanation:

In RAG, "Groundedness" assesses whether the response is factually correct and supported by retrieved data, while "Answer Relevance" evaluates how well the response addresses the user’s query. Option A captures this distinction accurately. Option B is off—groundedness isn’t just contextual alignment, and relevance isn’t about syntax. Option C swaps the definitions. Option D misaligns—groundedness isn’t solely data integrity, and relevance isn’t lexical diversity. This distinction ensures RAG outputs are both true and pertinent.

OCI 2025 Generative AI documentation likely defines these under RAG evaluation metrics.

Comprehensive and Detailed In-Depth Explanation:

For a chatbot needing to answer policy queries (requiring up-to-date, specific data) and retain chat history (context awareness), an LLM with RAG is ideal. RAG integrates external data (e.g., policy documents) via retrieval and supports memory for session-long context, making Option B correct. Option A (keyword search) lacks reasoning and context retention. Option C (standalone LLM) can’t dynamically fetch policy data. Option D (pre-trained LLM) is too vague and lacks RAG’s capabilities. RAG meets both requirements effectively.

OCI 2025 Generative AI documentation likely highlights RAG for dynamic, context-aware applications.

Comprehensive and Detailed In-Depth Explanation:

T-Few fine-tuning enhances efficiency by updating only a small subset of transformer layers or parameters (e.g., via adapters), reducing computational load—Option D is correct. Option A (adding layers) increases complexity, not efficiency. Option B (all layers) describes Vanilla fine-tuning. Option C (excluding layers) is false—T-Few updates, not excludes. This selective approach optimizes resource use.

OCI 2025 Generative AI documentation likely details T-Few under PEFT methods.

Comprehensive and Detailed In-Depth Explanation:

"k-shot prompting" (e.g., few-shot) involves providing k examples of a task in the prompt to guide the LLM’s output via in-context learning, without additional training. This makes Option B correct. Option A (k words) misinterprets—examples, not word count, matter. Option C (training) confuses prompting with fine-tuning. Option D (k outcomes) is unrelated—k refers to examples, not limits. k-shot leverages pre-trained knowledge efficiently.

OCI 2025 Generative AI documentation likely covers k-shot prompting under prompt engineering techniques.

Comprehensive and Detailed In-Depth Explanation:

Dot Product computes the raw similarity between two vectors, factoring in both magnitude and direction, while Cosine Distance (or similarity) normalizes for magnitude, focusing solely on directional alignment (angle), making Option C correct. Option A is vague—both measure similarity, not distinct content vs. topicality. Option B is false—both address semantics, not syntax. Option D is incorrect—neither measures word overlap or style directly; they operate on embeddings. Cosine is preferred for normalized semantic comparison.

OCI 2025 Generative AI documentation likely explains these metrics under vector similarity in embeddings.

Comprehensive and Detailed In-Depth Explanation:

Prompt injection (jailbreaking) attempts to bypass an LLM’s restrictions by crafting prompts that trick it into revealing restricted information or behavior. Option A asks the model to creatively circumvent its protocols, a classic jailbreaking tactic—making it correct. Option B is a hypothetical persuasion task, not a bypass. Option C tests privacy handling, not injection. Option D is a creative writing prompt, not an attempt to break rules. A seeks to exploit protocol gaps.

OCI 2025 Generative AI documentation likely addresses prompt injection under security or ethics sections.

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.

Comprehensive and Detailed In-Depth Explanation:

OCI Generative AI typically offers pretrained models for summarization (A), generation (B), and embeddings (D), aligning with common generative tasks. Translation models (C) are less emphasized in generative AI services, often handled by specialized NLP platforms, making C the NOT category. While possible, translation isn’t a core OCI generative focus based on standard offerings.

OCI 2025 Generative AI documentation likely lists model categories under pretrained options.

Comprehensive and Detailed In-Depth Explanation:

Retrievers in LangChain fetch relevant information (e.g., documents, embeddings) from external knowledge bases (like vector stores) to provide context for LLM responses, especially in RAG setups. This makes Option B correct. Option A (training) is unrelated—Retrievers operate at inference. Option C (task breakdown) pertains to prompting techniques, not retrieval. Option D (pipeline combination) describes chains, not Retrievers specifically. Retrievers enhance context awareness.

OCI 2025 Generative AI documentation likely defines Retrievers under LangChain components.

Comprehensive and Detailed In-Depth Explanation:

RAG integrates vector databases to retrieve real-time external data, augmenting the LLM’s pretrained knowledge with current, specific information, shifting response generation to a hybrid approach—Option B is correct. Option A is false—architecture remains neural; only data sourcing changes. Option C is incorrect—pretraining is still required; RAG enhances it. Option D is wrong—RAG improves, not limits, generation. This shift enables more accurate, up-to-date responses.

OCI 2025 Generative AI documentation likely details RAG’s impact under responsegeneration enhancements.

Comprehensive and Detailed In-Depth Explanation:

Temperature adjusts the softmax distribution in decoding. Increasing it (e.g., to 2.0) flattens the curve, giving lower-probability words a better chance, thus increasing diversity—Option C is correct. Option A exaggerates—top words still have impact, just less dominance. Option B is backwards—decreasing temperature sharpens, not broadens. Option D is false—temperature directly alters distribution, not speed. This controls output creativity.

OCI 2025 Generative AI documentation likely reiterates temperature effects under decoding parameters.

Comprehensive and Detailed In-Depth Explanation:

Accuracy in fine-tuning measures the proportion of correct predictions (e.g., matching expected outputs) out of all predictions made during evaluation, reflecting model performance—Option C is correct. Option A (total predictions) ignores correctness. Option B (incorrect proportion) is the inverse—error rate. Option D (layer depth) is unrelated to accuracy. Accuracy is a standard metric for generative tasks.

OCI 2025 Generative AI documentation likely defines accuracy under fine-tuning evaluation metrics.

Comprehensive and Detailed In-Depth Explanation:

PEFT (e.g., LoRA, T-Few) updates a small subset of parameters (often new ones) using labeled, task-specific data, unlike classic fine-tuning, which updates all parameters—Option A is correct. Option B reverses PEFT’s efficiency. Option C (no modification) fits soft prompting, not all PEFT. Option D (all parameters) mimics classic fine-tuning. PEFT reduces resource demands.

OCI 2025 Generative AI documentation likely contrasts PEFT and fine-tuning under customization methods.

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 (position-based) 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.

Comprehensive and Detailed In-Depth Explanation:

Embeddings in NLP are dense, numerical vectors that represent words, phrases, or sentences in a way that captures their semantic meaning and relationships (e.g., "king" and "queen" being close in vector space). This enables models to process text mathematically, making Option C correct. Option A is false, as embeddings simplify processing, not increase complexity. Option B relates to translation, not embeddings’ primary purpose. Option D is incorrect, as embeddings aren’t primarily for compression but for representation.

OCI 2025 Generative AI documentation likely covers embeddings under data preprocessing or vector databases.

Comprehensive and Detailed In-Depth Explanation:

In LangChain, PromptTemplate supports any number of input_variables (zero, one, or more), allowing flexible prompt design—Option C is correct. The example shows two, but it’s not a requirement. Option A (minimum two) is false—no such limit exists. Option B (single variable) is too restrictive. Option D (no variables) contradicts its purpose—variables are optional but supported. This adaptability aids prompt engineering.

OCI 2025 Generative AI documentation likely covers PromptTemplate under LangChain prompt design.

Comprehensive and Detailed In-Depth Explanation:

Soft prompting (e.g., prompt tuning) involves adding trainable parameters (soft prompts) to an LLM’s input while keeping the model’s weights frozen, adapting it to tasks without task-specific retraining. This is efficient when fine-tuning or large datasets aren’t feasible, making Option C correct. Option A suits full fine-tuning, not soft prompting, which avoids extensive labeled data needs. Option B could apply, but domain adaptation often requires more than soft prompting (e.g., fine-tuning). Option D describes continued pretraining, not soft prompting. Soft prompting excels in low-resource customization.

OCI 2025 Generative AI documentation likely discusses soft prompting under parameter-efficient methods.

Comprehensive and Detailed In-Depth Explanation:

In LangChain, a chain interacts with memory after receiving user input (to load prior context) but before execution (to inform the process), and again after the core logic (to update memory with new context) but before the final output. This ensures context continuity, making Option C correct. Option A is too late, missing pre-execution context. Option B is misordered. Option D overstates interaction, as it’s not continuous but at specific points. Memory integration is key for stateful chains.

OCI 2025 Generative AI documentation likely details memory interaction under LangChain workflows.

Comprehensive and Detailed In-Depth Explanation:

The RAG (Retrieval-Augmented Generation) Sequence model retrieves a set of relevant documents for a query from an external knowledge base (e.g., via a vector database) and uses them collectively with the LLM to generate a cohesive, informed response. This leverages multiple sources for better context, making Option B correct. Option A describes a simpler approach (e.g., RAG Token), not Sequence. Option C is incorrect—RAG considers the full query. Option D is false—query modification isn’t standard in RAG Sequence. This method enhances response quality with diverse inputs.

OCI 2025 Generative AI documentation likely details RAG Sequence under retrieval-augmented techniques.

Comprehensive and Detailed In-Depth Explanation:

LangChain is a Python library designed to simplify building applications with LLMs by providing tools for chaining operations, managing memory, and integrating external data (e.g., via RAG). This makes Option B correct. Options A, C, and D are incorrect, as LangChain is neither JavaScript, Java, nor Ruby-based, nor limited to summarization or generation alone—it’s broader in scope. It’s widely used for LLM-powered apps.

OCI 2025 Generative AI documentation likely introduces LangChain under supported frameworks.

Comprehensive and Detailed In-Depth Explanation:

Vanilla fine-tuning updates all model parameters, and with small datasets, it can overfit—memorizing the data rather than generalizing—leading to poor performance on unseen data. Option A is correct. Option B (underfitting) is unlikely with full updates—overfitting is the risk. Option C (data leakage) depends on data handling, not size. Option D (model drift) relates to deployment shifts, not training. Small datasets exacerbate overfitting in Vanilla fine-tuning.

OCI 2025 Generative AI documentation likely warns of overfitting under Vanilla fine-tuning limitations.