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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q13-Q18):
NEW QUESTION # 13
Given the following code:
PromptTemplate(input_variables=["human_input", "city"], template=template) Which statement is true about PromptTemplate in relation to input_variables?
- A. PromptTemplate is unable to use any variables.
- B. PromptTemplate requires a minimum of two variables to function properly.
- C. PromptTemplate supports any number of variables, including the possibility of having none.
- D. PromptTemplate can support only a single variable at a time.
Answer: C
Explanation:
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.
NEW QUESTION # 14
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
Answer: D
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.
NEW QUESTION # 15
What do prompt templates use for templating in language model applications?
- A. Python's lambda functions
- B. Python's class and object structures
- C. Python's list comprehension syntax
- D. Python's str.format syntax
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates in LLM applications (e.g., LangChain) typically use Python's str.format() syntax to insert variables into predefined string patterns (e.g., "Hello, {name}!"). This makes Option B correct. Option A (list comprehension) is for list operations, not templating. Option C (lambda functions) defines functions, not templates. Option D (classes/objects) is overkill-templates are simpler constructs. str.format() ensures flexibility and readability.
OCI 2025 Generative AI documentation likely mentions str.format() under prompt template design.
NEW QUESTION # 16
What do embeddings in Large Language Models (LLMs) represent?
- A. The color and size of the font in textual data
- B. The semantic content of data in high-dimensional vectors
- C. The frequency of each word or pixel in the data
- D. The grammatical structure of sentences in the data
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Embeddings in LLMs are high-dimensional vectors that encode the semantic meaning of words, phrases, or sentences, capturing relationships like similarity or context (e.g., "cat" and "kitten" being close in vector space). This allows the model to process and understand text numerically, making Option C correct. Option A is irrelevant, as embeddings don't deal with visual attributes. Option B is incorrect, as frequency is a statistical measure, not the purpose of embeddings. Option D is partially related but too narrow-embeddings capture semantics beyond just grammar.
OCI 2025 Generative AI documentation likely discusses embeddings under data representation or vectorization topics.
NEW QUESTION # 17
Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship. What is the nature of these relationships, and why arethey crucial for language models?
- A. Semantic relationships; crucial for understanding context and generating precise language
- B. Hierarchical relationships; important for structuring database queries
- C. Temporal relationships; necessary for predicting future linguistic trends
- D. Linear relationships; they simplify the modeling process
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vector databases store embeddings that preserve semantic relationships (e.g., similarity between "dog" and "puppy") via their positions in high-dimensional space. This accuracy enables LLMs to retrieve contextually relevant data, improving understanding and generation, making Option B correct. Option A (linear) is too vague and unrelated. Option C (hierarchical) applies more to relational databases. Option D (temporal) isn't the focus-semantics drives LLM performance. Semantic accuracy is vital for meaningful outputs.
OCI 2025 Generative AI documentation likely discusses vector database accuracy under embeddings and RAG.
NEW QUESTION # 18
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