1Z0-1127-25 Practice Questions
Prepare for 1Z0-1127-25 with more than an answer.
- Exam fee
- $245 USD
- Level
- Professional
- Valid for
- Non-Expiring
Domains covered on the exam 4
- Fundamentals of Large Language Models (LLMs)20%
- Using OCI Generative AI Service40%
- Implement RAG using OCI Generative AI service20%
- Using OCI Generative AI RAG Agents service20%
- 1
A company has created several custom models by fine-tuning a base model on different proprietary datasets. They want to deploy these models for inference on a single Dedicated AI Cluster to optimize resource utilization. Is this possible, and if so, how is it achieved?
Show answer details
Correct answer: D
This is the correct method. A Dedicated AI Cluster provides a pool of GPU resources. You can create multiple, distinct model endpoints, each associated with a different custom model, and deploy them all onto the same cluster. The cluster's resources will be shared among these endpoints, which is an efficient way to serve multiple models without provisioning separate hardware for each.
- 2
A Lead MLOps Engineer is designing the architecture for a large-scale language model deployment on Oracle Cloud Infrastructure. The system requires a model capable of handling long-context summarization tasks with high accuracy. While evaluating the Transformer architecture components, the engineer needs to select a mechanism that allows the model to weigh the importance of different words in the input sequence relative to each other, regardless of their distance. Which specific component of the Transformer architecture provides this capability?
Show answer details
Correct answer: B
The Self-Attention mechanism allows the model to look at other words in the input sequence to better understand the context of a specific word. It calculates attention scores that determine how much focus to place on other parts of the input, enabling the handling of long-range dependencies.
- 3
During the implementation of a chatbot using OCI Generative AI, a developer notices that the model occasionally generates plausible-sounding but factually incorrect information when asked about obscure historical events not covered in its training data. This phenomenon is known as hallucination. Which prompt engineering technique would be most effective in mitigating this by requiring the model to articulate its reasoning steps before providing a final answer?
Show answer details
Correct answer: B
Chain-of-Thought (CoT) prompting encourages the model to generate intermediate reasoning steps. This decomposition of complex problems helps the model stay grounded and reduces the likelihood of hallucinations by forcing a logical progression before the final output.
- 4
A solutions architect is configuring the inference parameters for a creative writing application using the OCI Generative AI service. The goal is to generate highly diverse and creative story concepts. The architect decides to adjust the sampling strategy. Which combination of parameter settings would best achieve maximum creativity and randomness in the output?
Show answer details
Correct answer: A
High Temperature increases the probability of selecting lower-likelihood tokens, introducing more randomness. High Top-p (nucleus sampling) includes a larger portion of the probability mass, allowing for a wider variety of token choices. Together, they maximize creativity.
- 5
Case Study: TechCorp requires a custom LLM for proprietary code analysis. They have a small dataset of 500 high-quality examples. They are considering Full Fine-Tuning versus Parameter-Efficient Fine-Tuning (PEFT) using T-Few.
Which statement accurately describes the resource implications and suitability of PEFT for this scenario compared to Full Fine-Tuning?
Show answer details
Correct answer: A
PEFT methods like T-Few update only a small subset of parameters (additive or selective), which drastically reduces the computational cost and memory footprint compared to updating all weights in Full Fine-Tuning. This is particularly beneficial for small datasets to avoid catastrophic forgetting and overfitting.
- 6
A data science team is developing a new LLM-based application to generate creative marketing slogans. They observe that the model's output is highly deterministic and lacks variety, often repeating the same few ideas. Which model parameter should they adjust to increase the creativity and randomness of the generated text?
Show answer details
Correct answer: B
The temperature parameter controls the randomness of the output. A lower temperature (e.g., 0.2) makes the model more deterministic and focused, while a higher temperature (e.g., 0.9) increases randomness, leading to more creative and diverse outputs. This is the correct parameter to adjust for the desired outcome.
- 7
A financial services company is building a chatbot to answer complex customer queries about mortgage applications. The queries often require multi-step reasoning. For example, a user might ask, 'If I have an income of $120,000, a credit score of 750, and $50,000 for a down payment, what is the maximum loan I can qualify for and what would my estimated monthly payment be?' Which prompt engineering technique is best suited for guiding the LLM to solve this problem accurately?
Show answer details
Correct answer: C
Chain-of-thought (CoT) prompting is specifically designed for problems requiring multi-step reasoning. It involves providing examples where the intermediate reasoning steps are explicitly written out. This guides the model to 'think step-by-step,' breaking down the complex problem into smaller, manageable parts, which significantly improves accuracy on arithmetic and logical reasoning tasks.
- 8
What is the primary function of a Dedicated AI Cluster within the OCI Generative AI service?
Show answer details
Correct answer: C
A Dedicated AI Cluster is a managed set of dedicated GPU resources. Its primary purpose is to provide the necessary computational power for the intensive tasks of fine-tuning a base model with custom data and for hosting custom model endpoints for dedicated, high-performance inference.
