1Z0-1127-25 Practice Test 4 — 10 Questions

Question 1

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

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Model

Question 2

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

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Increase the temperature to 1.5

Answer

Increase the frequency_penalty

Question 3

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 4

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 5

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 6

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.

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Increase the max tokens to allow longer responses.

Answer

Switch to a different model with better language understanding.

Question 7

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 8

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 9

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.

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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 10

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