1Z0-1127-25 Practice Test 10 — 10 Questions

Question 1

A developer creates a prompt for a code generation task but the output often contains syntax errors. Which adjustment to the prompt is MOST likely to improve correctness?

Accepted Answer

Add a few examples of correct code for similar tasks in the prompt. Adding few-shot examples of correct code directly in the prompt provides the model with explicit patterns to follow, reducing syntactic drift. This technique, known as few-shot prompting, anchors the model's output to the demonstrated structure and syntax, which is far more effective than relying on the model's internal knowledge alone for code generation tasks.

Answer

Remove the output format specification to give the model freedom

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Increase the temperature to 0.9 for more creative solutions

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Set the max tokens to a very high value

Question 2

Which of the following best describes the role of the self-attention mechanism in a Transformer model?

Accepted Answer

It computes a weighted sum of all input token representations, where weights depend on pairwise compatibility between tokens. The self-attention mechanism computes a weighted sum of all input token representations, where the weights are determined by the pairwise compatibility (attention scores) between tokens. This allows each token to dynamically attend to every other token in the sequence, capturing global dependencies without the limitations of fixed local windows or recurrence.

Answer

It encodes the order of tokens in the sequence

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It applies a convolutional filter over local windows of tokens

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It replaces the need for positional encoding by using recurrence

Question 3

A developer wants to use LangChain to create an agent that can perform calculations and look up information from a database. Which tools should be provided to the agent?

Accepted Answer

Calculator tool and a custom tool for database queries. The agent needs to perform calculations and look up information from a database, so the correct toolset is a Calculator tool (for arithmetic) and a custom tool that wraps database queries (for the lookup). LangChain agents select tools based on the task, so providing exactly these two matches the stated capabilities.

Answer

Custom tool for database queries and a vector store tool

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Calculator tool and a retriever tool

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Web search tool and an LLM tool

Question 4

In LangChain, which component is responsible for connecting a language model to a retriever and a prompt template to answer questions based on retrieved documents?

Accepted Answer

RetrievalQA chain. RetrievalQA is the LangChain chain purpose-built to wire together a retriever, a prompt template, and an LLM so the model answers questions grounded in retrieved documents. It handles the retrieve-then-generate flow automatically: it fetches relevant chunks from the vector store, injects them into the prompt, and passes the augmented prompt to the LLM. This is the classic RAG pattern in LangChain.

Answer

LLMChain

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SequentialChain

Answer

AgentExecutor

Question 5

A developer needs to generate embeddings for text data using the OCI Generative AI service. Which API should they call to get vector representations of text?

Accepted Answer

cohere.embed. The cohere.embed API is specifically designed to convert text into vector embeddings, which are numerical representations that capture semantic meaning. This is the correct choice for generating embeddings for RAG or vector search workflows in OCI Generative AI.

Answer

cohere.generate

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cohere.summarize

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cohere.classify

Question 6

An organization is deploying a custom fine-tuned model for a real-time fraud detection application. The model must respond within 200ms and cannot share infrastructure with other customers. Which OCI GenAI infrastructure option should they choose?

Accepted Answer

A dedicated AI cluster with provisioned model units. A dedicated AI cluster with provisioned model units ensures exclusive infrastructure, meeting the requirement to not share resources with other customers. This option also provides predictable, low-latency inference (under 200ms) by allocating dedicated compute capacity for the fine-tuned model, which is critical for real-time fraud detection.

Answer

On-demand token-based inference

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Shared infrastructure with reserved capacity

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OCI Functions using the GenAI SDK

Question 7

A developer needs to integrate OCI Generative AI into a Python application. Which SDK should they use?

Accepted Answer

OCI Python SDK. The OCI Python SDK (Option B) is the correct choice because it provides the official set of libraries and tools for interacting with Oracle Cloud Infrastructure services, including the Generative AI service. This SDK handles authentication, request signing, and API calls specific to OCI, enabling seamless integration of OCI Generative AI into Python applications.

Answer

Boto3

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Google Cloud client

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OpenAI library

Question 8

You manage a generative AI model deployed on OCI Model Deployment that serves a chatbot application. The model is a 13B parameter LLM on a VM.GPU.A100.1 shape. Recently, you rolled out a new version of the model that is supposed to improve response quality. However, after the update, the application starts returning HTTP 500 errors and memory usage spikes. You need to update to the new version without causing downtime. The current deployment has 2 replicas with autoscaling enabled. Which strategy should you use to safely deploy the new model version?

Accepted Answer

Create a second deployment with the new model, test it, then shift traffic using a load balancer. It implements a blue/green deployment strategy: you create a second deployment with the new model, test it in isolation, and then shift traffic using a load balancer. This avoids downtime and allows you to validate the new model before exposing it to production traffic, which is critical given the observed HTTP 500 errors and memory spikes.

Answer

Directly update the existing model deployment with the new model artifact

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Stop the existing deployment, update the model artifact, then start the deployment

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Increase the number of replicas to 4, then update the model

Question 9

Which THREE factors should be considered when choosing between fine-tuning a model and using a pre-trained model with prompt engineering? (Select three.)

Accepted Answer

Size of available dataset. Option B is correct because fine-tuning typically requires a substantial labeled dataset, whereas prompt engineering can work with few or no examples, so the size of the available dataset directly determines which approach is feasible. Option D is correct because fine-tuning consumes significant compute for training and hosting, while prompt engineering mainly uses inference, making the available budget for compute resources a key decision factor. Option E is correct because if the task demands specialized domain-specific terminology or nuanced jargon, fine-tuning can embed that knowledge into the model, whereas prompt engineering must supply it through carefully crafted prompts. Option A is not a primary factor here because both approaches can be optimized for latency and response time depends more on deployment and model size than on the fine-tune-versus-prompt choice. Option C is not relevant because internet connectivity affects how a model is accessed or deployed, not the fundamental decision between fine-tuning and prompt engineering.

Answer

Required response time

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Internet connectivity

Question 10

A developer needs to build a chain that first summarizes a long document, then translates the summary into French. Which LangChain chain type allows executing these steps in sequence with the output of one step feeding into the next?

Accepted Answer

SequentialChain. SequentialChain in LangChain is designed to execute multiple chains in order, passing the output of one step as input to the next. It supports multiple inputs and outputs and is the canonical chain type for multi-step pipelines like summarize-then-translate. LLMChain is a single-step chain, and RetrievalQA/ConversationalRetrievalChain are retrieval-focused, not general sequential pipelines.

Answer

RetrievalQA

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LLMChain

Answer

ConversationalRetrievalChain