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Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?
A. Chain-of-Thought
B. In-Context Learning
C. Least-to-Most Prompting
D. Step-Back Prompting
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Chain-of-Thought (CoT) prompting explicitly instructs an LLM to provide intermediate reasoning steps, enhancing complex task performance-Option B is correct. Option A (Step-Back) reframes problems, not emits steps. Option C (Least-to-Most) breaks tasks into subtasks, not necessarily showing reasoning. Option D (In-Context Learning) uses examples, not reasoning steps. CoT improves transparency and accuracy.
OCI 2025 Generative AI documentation likely covers CoT under advanced prompting techniques.
NEW QUESTION # 41
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?
A. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
B. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
C. Dot Product measures the magnitude and direction of vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.
D. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
Answer: C
Explanation:
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.
NEW QUESTION # 42
What do prompt templates use for templating in language model applications?
A. Python's class and object structures
B. Python's str.format syntax
C. Python's list comprehension syntax
D. Python's lambda functions
Answer: B
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 # 43
What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?
A. Explicitly providing k examples of the intended task in the prompt to guide the model's output
B. Limiting the model to only k possible outcomes or answers for a given task
C. The process of training the model on k different tasks simultaneously to improve its versatility
D. Providing the exact k words in the prompt to guide the model's response
Answer: A
Explanation:
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.
NEW QUESTION # 44
What is the purpose of memory in the LangChain framework?
A. To store various types of data and provide algorithms for summarizing past interactions
B. To perform complex calculations unrelated to user interaction
C. To retrieve user input and provide real-time output only
D. To act as a static database for storing permanent records
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, memory stores contextual data (e.g., chat history) and provides mechanisms to summarize or recall past interactions, enabling coherent, context-aware conversations. This makes Option B correct. Option A is too limited, as memory does more than just input/output handling. Option C is unrelated, as memory focuses on interaction context, not abstract calculations. Option D is inaccurate, as memory is dynamic, not a static database. Memory is crucial for stateful applications.
OCI 2025 Generative AI documentation likely discusses memory under LangChain's context management features.
NEW QUESTION # 45
......
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