1Z0-1127-25 Practice Exam 2 — 20 Questions

Question 1

Which OCI Generative AI model is best suited for generating embeddings from text that can be used for semantic search across multiple languages?

Accepted Answer

Cohere embed-multilingual-v3.0. Embed-multilingual-v3.0 is designed for multilingual embedding tasks. The other models are primarily for text generation or English-only embeddings.

Answer

Cohere Command R+

Answer

Meta Llama 3 70B

Answer

Cohere Rerank

Question 2

Which component of the Transformer architecture allows the model to weigh the importance of different words in a sequence when processing a given word?

Accepted Answer

Self-attention mechanism. The self-attention mechanism is the core component of the Transformer architecture that computes attention scores between every pair of words in the input sequence. These scores determine how much each word should influence the representation of the current word, allowing the model to dynamically weigh the importance of different words regardless of their positional distance. This mechanism is what enables the Transformer to capture long-range dependencies and contextual relationships in parallel.

Answer

Feed-forward neural network

Answer

Positional encoding

Answer

Layer normalization

Question 3

A company wants to build a RAG-based assistant that answers queries using documents stored in OCI Object Storage. Which OCI Generative AI service should they use?

Accepted Answer

OCI Generative AI Agents. OCI Generative AI Agents provides a managed RAG service that can ingest documents from Object Storage and answer questions based on the ingested knowledge base.

Answer

OCI Generative AI Playground

Answer

OCI Generative AI Embedding API

Answer

OCI Generative AI Chat API

Question 4

What is the primary benefit of using a Dedicated AI Cluster for inference in OCI Generative AI?

Accepted Answer

Higher throughput and lower latency due to dedicated compute resources. A Dedicated AI Cluster provides exclusive, low-latency inference with reserved capacity, unlike shared infrastructure where resources are contended.

Answer

Ability to use any LLM model for free

Answer

Automatic model fine-tuning on the cluster

Answer

No need to create endpoints

Question 5

An organization requires low-latency inference for a custom fine-tuned model deployed on OCI Generative AI. The model must be isolated from other tenants. Which infrastructure choice meets these requirements?

Accepted Answer

Provision a dedicated AI cluster with the required model units. A dedicated AI cluster provides isolated, low-latency inference for custom models. Shared infrastructure does not guarantee isolation and may have higher latency due to contention.

Answer

Use the shared infrastructure endpoint for the fine-tuned model

Answer

Deploy the model on OCI Data Science model deployment

Answer

Use an on-premises GPU server and call OCI GenAI via API gateway

Question 6

An enterprise needs to deploy a custom fine-tuned model for real-time inference with strict latency requirements. They also need to manage costs by paying only for usage. Which three steps are required to achieve this? (Select THREE)

Accepted Answer

Create an endpoint using InferenceClient to point to the dedicated cluster. Option B is correct because creating an endpoint with InferenceClient (or the equivalent endpoint-creation API) is the required step to expose the fine-tuned model on the dedicated cluster so applications can send real-time inference requests to it. Option D is correct because a Dedicated AI Cluster with sufficient model units must be provisioned to provide the dedicated, predictable compute capacity needed to meet strict latency requirements. Option E is correct because the fine-tuned model must actually be hosted/deployed on that Dedicated AI Cluster; provisioning the cluster alone does not serve the model. Option A is not correct because on-demand token-based inference on shared infrastructure does not provide the dedicated capacity guarantees needed for strict latency SLAs. Option C is not correct because deleting the fine-tuning job after deployment is not a required step and does not affect inference costs in the way described; the deployed model and cluster are what incur usage costs.

Answer

Use the on-demand token-based inference on shared infrastructure

Answer

Delete the fine-tuning job after deployment to save costs

Question 7

A developer is troubleshooting an OCI Generative AI inference request that returns a 400 Bad Request error. Which three common causes could result in this error? (Choose three.)

Accepted Answer

Incorrect endpoint URL. Option A is correct because a 400 Bad Request is returned when the request is sent to an incorrect or malformed endpoint URL, such as a wrong region-specific hostname or a mistyped path, so the service cannot route or parse the request. Option C is correct because OCI Generative AI requires specific fields in the JSON body (for example modelId, compartmentId, and the chat or generateText payload); omitting required parameters causes the service to reject the request with 400. Option D is correct because exceeding the model's maximum token limit (input plus output tokens) violates the request constraints and produces a 400 Bad Request rather than a server-side 5xx error. Option B is not correct because an invalid API key typically yields 401 Unauthorized (or 403 Forbidden), not 400 Bad Request. Option E is not correct because network connectivity issues generally manifest as timeouts or connection errors, not an HTTP 400 response from the service.

Answer

Invalid API key in the request header

Answer

Network connectivity issues

Question 8

A data engineer is building a RAG application using OCI Generative AI Agents. They have documents stored in OCI Object Storage. Which resource must they create to make these documents searchable by the agent?

Accepted Answer

Create a data source pointing to the Object Storage bucket. In OCI Generative AI Agents, a data source points to Object Storage buckets and indexes the documents. Knowledge bases combine data sources, but the first step is creating the data source.

Answer

Create a Dedicated AI Cluster

Answer

Create an endpoint

Answer

Create a fine-tuning job

Question 9

During iterative prompt refinement, a team evaluates two prompt variants on 100 test queries. Variant A scores 85% accuracy but occasionally generates offensive content. Variant B scores 80% accuracy with no safety issues. Which evaluation criterion should take priority for a customer-facing application?

Accepted Answer

Safety — offensive content is unacceptable in a customer-facing system. For a customer-facing application, safety must take priority over accuracy. Generating offensive content is unacceptable because it can damage brand reputation, violate terms of service, and create legal liability. While Variant A has higher accuracy (85% vs 80%), the 5% accuracy gain does not justify the risk of offensive output reaching customers. Safety is a non-negotiable requirement in customer-facing systems.

Answer

Accuracy — because it is highest and the offensive content can be filtered post-hoc

Answer

Cost — the variant with higher accuracy uses fewer tokens

Answer

Latency — because the variant with higher accuracy also has lower latency

Question 10

A developer is using the OCI Generative AI service API and receives a '400 Bad Request' with error 'Model not found'. What is the most likely cause?

Accepted Answer

The model ID is misspelled or does not exist.. The '400 Bad Request' with 'Model not found' error indicates that the model ID provided in the API request does not match any available model in the OCI Generative AI service. This is most commonly caused by a typo, incorrect model name, or referencing a model that has been deprecated or removed from the service catalog.

Answer

The API request lacks authentication.

Answer

The input exceeds the maximum token limit.

Answer

The endpoint region is incorrect.

Question 11

Which LangChain abstraction is responsible for storing and retrieving conversation history to maintain context across multiple turns in a chatbot?

Accepted Answer

Memory. In LangChain, the Memory abstraction is specifically designed to store and retrieve conversation history, enabling chatbots to maintain context across multiple turns. It provides mechanisms to persist and recall past interactions, which is essential for coherent multi-turn conversations. Therefore, Memory is the correct abstraction.

Answer

Agent

Answer

Chain

Answer

Model

Question 12

A team has fine-tuned a Cohere Command R model using T-Few on a dataset of 5,000 prompt/completion pairs. After deployment, they notice the model sometimes generates off-topic responses. Which action is most likely to improve response relevance without requiring new training data?

Accepted Answer

Set a preamble override with instructions to stay on topic. A preamble (system prompt) override lets you inject explicit behavioral instructions at inference time, such as 'Only answer questions related to the topic of customer support for product X.' This steers the model's generation without any retraining or new data, directly addressing off-topic drift. Cohere Command R supports a preamble parameter that is prepended to the conversation and strongly conditions the response.

Answer

Decrease the max_tokens

Answer

Increase the temperature to 1.5

Answer

Increase the frequency_penalty

Question 13

A prompt engineer is using the ReAct pattern to enable the model to reason and act (e.g., call tools). Which THREE components are essential in the prompt to implement ReAct correctly?

Accepted Answer

A list of available actions or tools with descriptions. Option C is correct because ReAct requires the model to know which external tools or actions it can invoke, so the prompt must enumerate the available actions/tools with descriptions that let the model select the right one. Option D is correct because the scratchpad is the working memory where the model records its intermediate Thought, Action, and Observation steps, enabling the interleaved reasoning-and-acting loop that defines ReAct. Option E is correct because few-shot exemplars demonstrating the Thought/Action/Observation cycle teach the model the exact output format and behavior to follow, which is essential for reliable ReAct execution. Option A is not required: while a stop sequence can help halt generation after an Action so the tool result can be injected, it is an implementation convenience rather than an essential ReAct component. Option B is incorrect because ReAct does not depend on a high temperature; in fact, lower temperatures are typically preferred for deterministic tool selection and structured reasoning.

Answer

A stop sequence after each action

Answer

A system prompt that sets a high temperature

Question 14

A practitioner is using a Cohere Command model on OCI for a translation task. They notice that the output is often incomplete and cuts off mid-sentence. Which parameter should they adjust to address this?

Accepted Answer

Max tokens. The 'Max tokens' parameter controls the maximum length of the generated output. When a model cuts off mid-sentence, it means the token limit has been reached before the model could complete its response. Increasing this value allows the model to generate more tokens, thus completing the translation.

Answer

Temperature

Answer

Frequency penalty

Answer

Top-p

Question 15

Users report that inference requests to the OCI Generative AI service are taking longer than expected. The application uses the on-demand endpoint. What is the most likely cause of the increased latency?

Accepted Answer

The on-demand endpoint experiences shared resource contention.. The on-demand endpoint in OCI Generative AI uses a shared infrastructure pool where compute resources are allocated dynamically. When multiple users or applications send concurrent inference requests, resource contention occurs, leading to queuing delays and increased latency. This is the most likely cause because the question specifies that users are experiencing slower-than-expected responses, and shared resource contention is a known behavior of on-demand endpoints.

Answer

The inference model is not fine-tuned for the use case.

Answer

The selected model is too large for the use case.

Answer

The API request timeout is set too low.

Question 16

A developer integrates OCI GenAI into a mobile app to provide product descriptions. The responses sometimes include explanations or questions instead of the requested format. The developer is using a simple prompt: 'Describe product X.' The app expects a single paragraph. Which corrective action should the developer take?

Accepted Answer

Add a structured prompt with format instructions and an example.. Adding a structured prompt with format instructions and an example directly addresses the issue of the model producing off-format responses. By explicitly specifying the expected output (e.g., 'Provide a single paragraph description without questions or explanations') and including a few-shot example, the developer constrains the model's generation to match the desired format, leveraging prompt engineering to guide the LLM's behavior without changing model parameters or architecture.

Answer

Lower the temperature to 0 to make responses deterministic.

Answer

Increase the max tokens to allow longer responses.

Answer

Switch to a different model with better language understanding.

Question 17

Which THREE factors are important when designing a multi-turn conversational agent using OCI Generative AI Agents?

Accepted Answer

Manage the context window size to avoid truncating important earlier messages.. Option B is correct because a multi-turn agent must track the growing token count of the conversation and manage the context window (the model's maximum input tokens) so that earlier turns are summarized, trimmed, or otherwise preserved rather than silently truncated, which would break conversational continuity. Option C is correct because production conversational agents need guardrails — content moderation and intent filtering — to detect and block sensitive topics, prompt-injection, or harmful intents before they reach the model or the user. Option E is correct because session management is what persists conversation history across turns, allowing the agent to reference prior user messages and maintain coherent multi-turn context instead of treating each request as stateless. Option A is not correct because response length should be tuned to the task, not maximized; overly long answers waste tokens, increase latency and cost, and can degrade user experience. Option D is not correct because disabling logging does not meaningfully reduce inference latency or cost and removes the observability needed to debug, audit, and improve the agent.

Answer

Always generate the longest possible response to be thorough.

Answer

Disable logging to reduce latency and cost.

Question 18

A developer is using tree-of-thought prompting to solve a complex planning problem. Which THREE characteristics are typical of tree-of-thought?

Accepted Answer

It explores multiple reasoning branches in parallel. Option A is correct because tree-of-thought (ToT) prompting explicitly expands a problem into multiple candidate reasoning paths at each step, evaluating several 'thoughts' in parallel rather than committing to one. Option C is correct because ToT maintains a search tree over partial solutions, so it can backtrack from dead-end or low-scoring branches and pursue alternatives. Option E is correct because ToT relies on an evaluator or heuristic (e.g., a value estimate or voting score) to rank and prune unpromising branches, keeping the search tractable. Option B is wrong because a single linear chain describes chain-of-thought prompting, not ToT. Option D is wrong because ToT returns the best-scoring path found by search, not necessarily a majority-vote single answer.

Answer

It generates a single linear chain of reasoning

Answer

It always returns a single final answer via majority vote

Question 19

A healthcare startup is building a chatbot that retrieves patient treatment guidelines using OCI Generative AI Service and OCI OpenSearch. They require that all retrieved documents are from approved sources only and that the system can explain which source was used for each response. Which combination of features should they implement?

Accepted Answer

Add a metadata filter for source_type='approved' in the retrieval step and include document IDs in the context for the model.. It directly addresses both requirements: a metadata filter on `source_type='approved'` ensures only approved documents are retrieved from OpenSearch, and including document IDs in the context allows the model to cite the specific source for each response. This approach enforces access control at the retrieval layer while providing traceability, which is essential for compliance in healthcare applications.

Answer

Rely on the vector search's cosine similarity to rank approved sources higher.

Answer

Use prompt engineering to ask the model to ignore non-approved sources.

Answer

Reduce the top-K value to limit the number of retrieved documents.

Question 20

Which THREE are common prompt failures that can degrade model performance?

Accepted Answer

Prompt injection vulnerabilities that allow malicious input to override instructions. Option B is correct because prompt injection is a well-known prompt failure in which untrusted input (e.g., user-supplied text or retrieved documents) contains instructions that override or subvert the system prompt, causing the model to ignore its intended task and degrade performance or safety. Option C is correct because ambiguous instructions leave the model without a single clear interpretation, so it may pick an unintended meaning, produce inconsistent outputs, or fail the task even though it followed the prompt literally. Option D is correct because insufficient context means the model lacks the facts, constraints, or background needed to answer correctly, leading to hallucination, guessing, or off-target responses. Option A is not a prompt failure: output token count is a generation/configuration limit, and short outputs are not inherently degraded—truncation is a parameter issue, not a prompt defect. Option E is not a prompt failure either: temperature is a sampling hyperparameter that controls randomness, so while a high value can reduce determinism, it is a model setting rather than a flaw in the prompt itself.

Answer

Using too few tokens in the output

Answer

Setting temperature too high