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Free PDF 2025 1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Professional Perfect Reliable Test Topics
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Details |
| Topic 1 |
- Implement RAG Using OCI Generative AI Service: This section tests the knowledge of Knowledge Engineers and Database Specialists in implementing Retrieval-Augmented Generation (RAG) workflows using OCI Generative AI services. It covers integrating LangChain with Oracle Database 23ai, document processing techniques like chunking and embedding, storing indexed chunks in Oracle Database 23ai, performing similarity searches, and generating responses using OCI Generative AI.
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| Topic 2 |
- Using OCI Generative AI RAG Agents Service: This domain measures the skills of Conversational AI Developers and AI Application Architects in creating and managing RAG agents using OCI Generative AI services. It includes building knowledge bases, deploying agents as chatbots, and invoking deployed RAG agents for interactive use cases. The focus is on leveraging generative AI to create intelligent conversational systems.
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| Topic 3 |
- Fundamentals of Large Language Models (LLMs): This section of the exam measures the skills of AI Engineers and Data Scientists in understanding the core principles of large language models. It covers LLM architectures, including transformer-based models, and explains how to design and use prompts effectively. The section also focuses on fine-tuning LLMs for specific tasks and introduces concepts related to code models, multi-modal capabilities, and language agents.
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| Topic 4 |
- Using OCI Generative AI Service: This section evaluates the expertise of Cloud AI Specialists and Solution Architects in utilizing Oracle Cloud Infrastructure (OCI) Generative AI services. It includes understanding pre-trained foundational models for chat and embedding, creating dedicated AI clusters for fine-tuning and inference, and deploying model endpoints for real-time inference. The section also explores OCI's security architecture for generative AI and emphasizes responsible AI practices.
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>> Reliable 1Z0-1127-25 Test Topics <<
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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q15-Q20):
NEW QUESTION # 15
How does a presence penalty function in language model generation?
- A. It applies a penalty only if the token has appeared more than twice.
- B. It penalizes all tokens equally, regardless of how often they have appeared.
- C. It penalizes a token each time it appears after the first occurrence.
- D. It penalizes only tokens that have never appeared in the text before.
Answer: C
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.
NEW QUESTION # 16
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 days?
- A. 480 unit hours
- B. 20 unit hours
- C. 744 unit hours
- D. 240 unit hours
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, a dedicated AI cluster's usage is typically measured in unit hours, where 1 unit hour = 1 hour of cluster activity. For 10 days, assuming 24 hours per day, the calculation is: 10 days × 24 hours/day = 240 hours. Thus, Option B (240 unit hours) is correct. Option A (480) might assume multiple clusters or higher rates, but the question specifies one cluster. Option C (744) approximates a month (31 days), not 10 days. Option D (20) is arbitrarily low.
OCI 2025 Generative AI documentation likely specifies unit hour calculations under Dedicated AI Cluster pricing.
NEW QUESTION # 17
Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks?
- A. Each customer's GPUs are connected via a public Internet network for ease of access.
- B. The GPUs allocated for a customer's generative AI tasks are isolated from other GPUs.
- C. GPUs are used exclusively for storing large datasets, not for computation.
- D. GPUs are shared with other customers to maximize resource utilization.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In Dedicated AI Clusters (e.g., in OCI), GPUs are allocated exclusively to a customer for their generative AI tasks, ensuring isolation for security, performance, and privacy. This makes Option B correct. Option A describes shared resources, not dedicated clusters. Option C is false, as GPUs are for computation, not storage. Option D is incorrect, as public Internet connections would compromise security and efficiency.
OCI 2025 Generative AI documentation likely details GPU isolation under DedicatedAI Clusters.
NEW QUESTION # 18
What is prompt engineering in the context of Large Language Models (LLMs)?
- A. Training the model on a large dataset
- B. Adding more layers to the neural network
- C. Iteratively refining the ask to elicit a desired response
- D. Adjusting the hyperparameters of the model
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt engineering involves crafting and refining input prompts to guide an LLM to produce desired outputs without altering its internal structure or parameters. It's an iterative process that leverages the model's pre-trained knowledge, making Option A correct. Option B is unrelated, as adding layers pertains to model architecture design, not prompting. Option C refers to hyperparameter tuning (e.g., temperature), not prompt engineering. Option D describes pretraining or fine-tuning, not prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt engineering in sections on model interaction or inference.
NEW QUESTION # 19
When does a chain typically interact with memory in a run within the LangChain framework?
- A. Only after the output has been generated.
- B. Before user input and after chain execution.
- C. After user input but before chain execution, and again after core logic but before output.
- D. Continuously throughout the entire chain execution process.
Answer: C
Explanation:
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.
NEW QUESTION # 20
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