1Z0-1127-25 Practice Test 18 — 15 Questions

Question 1

Which prompting technique involves providing the model with a small set of input-output examples within the prompt to guide its behavior?

Accepted Answer

Few-shot prompting. Few-shot prompting includes examples of desired input-output pairs, helping the model infer the task without fine-tuning.

Answer

Tree-of-thought prompting

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Chain-of-thought prompting

Answer

Zero-shot prompting

Question 2

A company is using OCI Generative AI for a RAG-based code assistant. They index source code repositories into a vector store. Developers report that the assistant often suggests deprecated APIs or outdated code snippets, even though the latest code is in the repository. The index was built a week ago and has not been updated. They plan to set up incremental updates. However, they notice that even after re-indexing the latest commits, the issue persists. What is the most likely oversight?

Accepted Answer

The vector store is not configured to overwrite existing vectors for updated documents.. The most likely oversight is that the vector store is not configured to overwrite existing vectors for updated documents. When re-indexing, if the system appends new vectors without deleting or updating the old ones, the retrieval may still surface deprecated code snippets from the outdated vectors. This leads to the assistant suggesting obsolete APIs despite the latest code being indexed.

Answer

The retrieval top-k is set too low, missing some relevant snippets.

Answer

The chunking strategy splits code at function boundaries, losing import statements.

Answer

The embedding model is not fine-tuned on code; it was trained on natural language.

Question 3

A team is designing a production-grade LangChain agent that uses multiple tools, including a custom SQL query tool and a web search tool. They need to ensure the agent handles errors gracefully and logs all actions. Which three practices should they implement? (Choose THREE.)

Accepted Answer

Set max_iterations in AgentExecutor to limit the number of reasoning steps and avoid infinite loops. Option B is correct because setting max_iterations on the AgentExecutor caps the agent's reasoning/action loop, preventing runaway or infinite loops that would otherwise exhaust resources or hang the production agent. Option C is correct because wrapping each tool's execution in try-except blocks lets the custom SQL tool and web search tool catch exceptions (e.g., SQL syntax errors or network timeouts) and return meaningful error strings the agent can reason about, enabling graceful failure instead of crashing. Option E is correct because a custom callback handler implementing hooks like on_tool_start, on_tool_end, and on_tool_error provides structured logging of every tool's inputs and outputs, which is the standard LangChain mechanism for monitoring and auditing agent actions. Option A is not appropriate because ZERO_SHOT_REACT_DESCRIPTION is a basic single-shot ReAct prompt strategy and does not by itself add error handling or logging, and it may be less suited to a multi-tool production agent than more capable agent types. Option D is not appropriate because verbose=True only prints reasoning traces to standard output for debugging; it is not a durable logging or monitoring solution and would expose internal reasoning to end users rather than record tool inputs/outputs for observability.

Answer

Use AgentType.ZERO_SHOT_REACT_DESCRIPTION to simplify the agent's reasoning

Answer

Set verbose=True in the AgentExecutor to show all reasoning steps to end users

Question 4

A data scientist wants to fine-tune a Cohere Command R model using the T-Few technique. They have prepared a dataset in JSONL format with prompt/completion pairs. Which step is REQUIRED before creating the fine-tuning job?

Accepted Answer

Upload the dataset to an OCI Object Storage bucket. Before creating a fine-tuning job in OCI Generative AI, the training dataset must reside in an OCI Object Storage bucket, because the fine-tuning service reads the JSONL file directly from Object Storage. The console and API both require a bucket and object namespace/name as input to the fine-tuning job configuration. No other preprocessing or labeling service is a prerequisite for T-Few fine-tuning.

Answer

Register the dataset in OCI Data Labeling

Answer

Deploy a dedicated AI cluster to host the base model

Answer

Create an OCI Functions endpoint for dataset preprocessing

Question 5

Which TWO of the following are valid approaches to serve a RAG application in OCI with low latency?

Accepted Answer

Implement a caching layer for frequently asked questions.. Option D is correct because implementing a caching layer for frequently asked questions avoids repeated embedding lookups and LLM inference calls, returning pre-computed responses from a fast store (e.g., OCI Cache with Redis or an in-memory cache) and thereby cutting end-to-end latency for common queries. Option E is correct because using an LLM that supports streaming responses (e.g., token-by-token delivery over SSE or chunked HTTP) reduces perceived latency by letting the user see output immediately rather than waiting for the full completion to be generated. Option A is not valid because pre-computing embeddings and answers for all possible questions is computationally infeasible and cannot cover the open-ended query space of a RAG application. Option B does not reduce latency for a given user, since deploying the vector store across multiple regions adds replication and consistency overhead and only helps if users are geographically distributed, not as a general low-latency approach. Option C is incorrect because increasing chunk size does not reduce the number of retrievals and typically harms retrieval precision while adding tokens to the prompt, increasing LLM latency.

Answer

Pre-compute embeddings and answers for all possible questions.

Answer

Deploy the vector store on multiple regions to reduce network latency.

Answer

Increase the chunk size to reduce the number of retrievals.

Question 6

During fine-tuning of a large language model on OCI, you notice that the model's performance on the validation set is not improving after several epochs, but the training loss continues to decrease. What is the most likely cause?

Accepted Answer

The model is overfitting to the training data.. When training loss continues to decrease while validation performance stalls or worsens, the model is overfitting to the training data: it is memorizing training examples rather than learning generalizable patterns. This divergence between training and validation metrics is the classic signature of overfitting. The most likely cause is overfitting, and remedies include regularization, dropout, early stopping, or more data.

Answer

The learning rate is too high.

Answer

The validation set is not representative.

Answer

The training data is too small.

Question 7

A healthcare company is using OCI GenAI to generate patient summaries from clinical notes. The model output sometimes includes hallucinated medical facts, such as incorrect dosages or diagnoses, which could be dangerous. The team needs to improve factual accuracy while maintaining data privacy. They have a large collection of internal medical knowledge bases (clinical guidelines, drug databases) that are stored in OCI Object Storage. The current implementation uses a zero-shot prompt with the base Cohere Command model. The data science team has limited GPU resources and wants to avoid building a complex pipeline. Which course of action best addresses the hallucination problem?

Accepted Answer

Implement a RAG pipeline that retrieves relevant documents from the internal knowledge bases and includes them in the prompt.. A Retrieval-Augmented Generation (RAG) pipeline directly addresses hallucination by grounding the model's output in verified, internal medical knowledge bases stored in OCI Object Storage. This approach retrieves relevant clinical guidelines or drug database entries and includes them in the prompt, providing factual context without requiring fine-tuning or complex GPU-intensive pipelines. It also preserves data privacy by keeping sensitive medical data within OCI and avoids exposing it to external model training.

Answer

Increase the temperature parameter to 0.9 to encourage more deterministic outputs.

Answer

Use prompt engineering to add 'Only provide facts that are absolutely certain.'

Answer

Fine-tune the Cohere model on a publicly available medical dataset like PubMed.

Question 8

A developer runs an OCI GenAI chat request with system prompt "You are a sarcastic assistant." The output is offensive. How can the developer enforce safety policies?

Accepted Answer

Use the OCI GenAI content moderation filter.. The OCI GenAI content moderation filter is specifically designed to enforce safety policies by detecting and blocking offensive, harmful, or policy-violating content in both input prompts and model outputs. By enabling this filter, the developer can prevent the model from generating offensive responses even when a system prompt like 'You are a sarcastic assistant' encourages undesirable behavior.

Answer

Change model to LLAMA.

Answer

Increase maxTokens.

Answer

Set temperature to 0.

Question 9

A team wants to use the Embedding API to convert product descriptions into vectors for a semantic search application. They have descriptions in English and Spanish. Which embedding model should they use?

Accepted Answer

embed-multilingual-v3.0. The team needs to embed text in both English and Spanish into a shared vector space for semantic search. Cohere's embed-multilingual-v3.0 is specifically trained on 100+ languages and produces cross-lingually aligned embeddings, meaning an English query can retrieve Spanish product descriptions and vice versa. embed-english-v3.0 only handles English, so Spanish text would be poorly represented or unsupported.

Answer

embed-english-v3.0

Answer

Cohere Command R

Answer

Cohere Rerank

Question 10

In few-shot prompting, what is the primary purpose of including examples in the prompt?

Accepted Answer

To provide a template for the desired output format and reasoning pattern. Examples guide the model on the desired input-output pattern, improving task performance without fine-tuning.

Answer

To reduce the need for a system prompt

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To increase the model's vocabulary

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To decrease the computational cost of inference

Question 11

An architect is optimizing an LLM application that processes long documents. The model has a 4096 token limit, but the documents are often 8000 tokens. They are using a chunking strategy. However, model responses sometimes miss key information that spans across chunks. Which technique most directly addresses this issue?

Accepted Answer

Use overlapping chunks to maintain context continuity.. Overlapping chunks ensure that tokens at the boundaries of one chunk are also present at the start of the next, preserving context continuity. This prevents the model from losing information that spans across chunk boundaries, which is a common issue when processing documents longer than the model's 4096-token context window.

Answer

Randomly select parts of the document to include.

Answer

Increase the max_tokens parameter for longer outputs.

Answer

Use a model with a larger context window.

Question 12

A user repeatedly gets the same phrase output by the model. Which parameter adjustment is MOST likely to reduce such repetitive patterns?

Accepted Answer

Increase frequency penalty. Frequency penalty penalizes tokens that have already appeared in the generated text, directly discouraging the model from repeating the same phrases. Increasing it reduces verbatim repetition by lowering the probability of previously used tokens. This is the most targeted parameter for the described symptom of repeated identical output.

Answer

Decrease max tokens

Answer

Increase temperature

Answer

Increase top-p

Question 13

A team fine-tuned a Cohere Command R model in OCI GenAI and validated it. They now need to deploy it for production inference with a dedicated endpoint. What is the correct sequence of steps?

Accepted Answer

Provision a Dedicated AI Cluster, deploy the model to the cluster, then create an endpoint. In OCI Generative AI, the correct sequence for deploying a fine-tuned model to a dedicated endpoint is: first provision a Dedicated AI Cluster (which provides the isolated compute infrastructure), then deploy the model to that cluster, and finally create an endpoint that exposes the deployed model for inference. This ensures the model is hosted on dedicated resources before the endpoint is created.

Answer

Create an endpoint, deploy the model, then provision a Dedicated AI Cluster

Answer

Deploy the model to a shared endpoint, then provision a Dedicated AI Cluster for scaling

Answer

Create an endpoint, then provision a Dedicated AI Cluster, then deploy the model

Question 14

A retail company uses OCI Generative AI Service to build a RAG chatbot for product recommendations. The chatbot should consider both the user's query and the retrieved product descriptions. Which component of the RAG pipeline is responsible for combining these inputs before sending to the LLM?

Accepted Answer

Prompt template. The prompt template is the component in a RAG pipeline that structures the final input to the LLM by combining the user's query with the retrieved product descriptions. It defines the format and instructions (e.g., 'Based on these product descriptions, recommend...') that the LLM uses to generate a coherent response. Without a prompt template, the raw query and documents would be sent without context, leading to poor or irrelevant outputs.

Answer

Reranker

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Document retriever

Answer

Embedding model

Question 15

Which TWO are valid methods to monitor the performance of a generative AI model deployed on OCI Data Science?

Accepted Answer

Use OCI Monitoring service to track custom metrics like latency and throughput. Option B is correct because OCI Monitoring supports custom metrics, so you can publish model-specific metrics such as inference latency and throughput via the Monitoring API/CLI and build alarms on them. Option C is correct because OCI Logging can collect and store inference logs (e.g., via the Logging service and agents or the Logging API), which lets you inspect request/response data and analyze model behavior over time. Option A is not a monitoring method per se: OCI Notifications only delivers messages/alerts that must first be triggered by an alarm or event, and it does not itself detect model drift. Option D is not a valid monitoring method: OCI Events reacts to state changes in OCI resources, not to model accuracy metrics, so it cannot trigger retraining on low accuracy by itself. Option E is not a monitoring method for model performance: OCI Audit records control-plane/API activity for compliance and security, not inference latency, throughput, or model quality.

Answer

Use OCI Notifications to receive alerts on model drift

Answer

Use OCI Events service to trigger retraining on low accuracy

Answer

Use OCI Audit service to review API call logs