1Z0-1127-25 Practice Test 2 — 30 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

Answer

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

Answer

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.

Answer

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.

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 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

Question 11

Which of the following is a known limitation of large language models where the model generates plausible-sounding but factually incorrect information?

Accepted Answer

Hallucination. Hallucination refers to the phenomenon where a large language model generates output that is fluent and plausible but factually incorrect or nonsensical. This is a known limitation because models are trained to predict the next word based on patterns, not to verify facts.

Answer

Bias

Answer

Context window limitation

Answer

Knowledge cutoff

Question 12

An OCI user observes that their embedding model returns vectors that are not normalized, and they want to compute cosine similarity between two text embeddings. What should they do?

Accepted Answer

Normalize the vectors to unit length, then compute the dot product. Cosine similarity measures the cosine of the angle between two vectors, which is equivalent to the dot product of the vectors after they have been normalized to unit length (L2 norm = 1). Option C correctly describes this process: first normalize each embedding vector to unit length, then compute the dot product. This is the standard approach because raw embedding vectors from models like OCI's AI services may not be unit vectors, and the dot product alone does not account for magnitude differences.

Answer

Compute the Euclidean distance between the vectors

Answer

Compute the L1 norm of the difference

Answer

Compute the dot product directly

Question 13

A company needs to evaluate a text summarization model. They have reference summaries and want a metric that measures overlap of n-grams. Which metric is MOST appropriate?

Accepted Answer

ROUGE. ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is the most appropriate metric because it measures the overlap of n-grams between the generated summary and reference summaries, directly aligning with the company's requirement. Unlike BLEU, which focuses on precision, ROUGE emphasizes recall, making it better suited for summarization tasks where capturing all key content from the reference is critical.

Answer

BLEU

Answer

Perplexity

Answer

BERTScore

Question 14

A company wants to use OCI Generative AI Agents to create a RAG-powered customer support system. Which THREE components are essential for the agent to work?

Accepted Answer

A knowledge base. Option A (a knowledge base) is essential because OCI Generative AI Agents uses the knowledge base to store and retrieve the vectorized chunks of enterprise data that ground the RAG responses. Option C (a data source such as an OCI Object Storage bucket) is required because the agent ingests the source documents from that data source to build and populate the knowledge base. Option D (a base LLM such as Cohere Command R) is necessary because the agent relies on an underlying foundation model to reason over the retrieved context and generate the final natural-language answer. Option B is incorrect because a Dedicated AI Cluster is only needed for hosting custom fine-tuned or dedicated model deployments, not for a standard RAG agent using an on-demand base model. Option E is incorrect because OCI Generative AI Agents automatically handles embedding generation with managed embedding models, so a customer-supplied fine-tuned embedding model is not a required component.

Answer

A Dedicated AI Cluster

Answer

A fine-tuned embedding model

Question 15

What is a recommended practice to prevent the LLM from generating information not present in the retrieved context when building a RAG application?

Accepted Answer

Using a system message that says 'Use only the provided context to answer.'. Explicitly instructing the LLM via a system message to 'Use only the provided context to answer' directly constrains the model to rely solely on the retrieved documents, reducing the risk of hallucination. This prompt engineering technique leverages the model's instruction-following capability to suppress parametric knowledge and enforce grounded generation.

Answer

Setting the temperature to 0.

Answer

Including few-shot examples in the prompt.

Answer

Increasing the topK value.

Question 16

An OCI practitioner observes that an LLM consistently generates incorrect answers for questions about recent events (last 6 months). The model was fine-tuned on company data but not retrained recently. What is the MOST likely root cause?

Accepted Answer

The model's knowledge cutoff date is before the recent events. The model's knowledge cutoff date determines the temporal boundary of its training data. Since the LLM was fine-tuned on company data but not retrained, it lacks exposure to events occurring after its cutoff, making it unable to generate accurate answers about recent events. This is the most direct and common cause for such temporal knowledge gaps in LLMs.

Answer

The context window is too short

Answer

The sampling strategy is too greedy

Answer

The fine-tuning dataset was too small

Question 17

An application uses Oracle AI Vector Search with a VECTOR data type column. The team wants to perform a similarity search to find the top 5 most similar vectors to a query vector. Which SQL clause is necessary to achieve this?

Accepted Answer

ORDER BY VECTOR_DISTANCE(embedding, :query_vec) FETCH FIRST 5 ROWS ONLY. Oracle AI Vector Search uses the VECTOR_DISTANCE function to compute the distance between vectors, and the FETCH FIRST 5 ROWS ONLY clause limits the result set to the top 5 most similar vectors. This combination performs an exact nearest neighbor search, which is the standard SQL approach for similarity queries on a VECTOR column.

Answer

WHERE embedding SIMILAR TO :query_vec TOP 5

Answer

ORDER BY VECTOR_COMPARE(embedding, :query_vec) LIMIT 5

Answer

WHERE KNN(embedding, :query_vec, 5)

Question 18

When building a RAG application for document retrieval, which chunking strategy is recommended to maximize retrieval accuracy?

Accepted Answer

Use overlapping chunks with a sliding window. Overlapping chunks with a sliding window ensure that context is preserved across chunk boundaries, which is critical for retrieval accuracy in RAG applications. When a query spans the boundary between two fixed-size chunks, the overlap captures the relevant context in both chunks, reducing the risk of missing key information. This strategy directly addresses the limitation of fixed-size token chunks that may split semantically related content.

Answer

Use fixed-size token chunks with no overlap

Answer

Use random splitting points

Answer

Use entire documents as single chunks

Question 19

An OCI administrator is configuring access control for OCI Generative AI. Which three IAM components are required to allow a group of data scientists to call the GenerateText API? (Choose three.)

Accepted Answer

An IAM group for the data scientists. Option A is correct because IAM authorization in OCI is always granted to groups, not directly to individual users, so the data scientists must be placed in an IAM group that the policy can reference. Option C is correct because a policy statement must grant the verb (for example manage or use) on the ai-services-generative-ai-family resource in the relevant compartment, which is what authorizes calls to the GenerateText API. Option E is correct because OCI policies are scoped to a compartment, so the Generative AI resources must reside in (or be covered by) a compartment for the policy to apply. Option B is incorrect because a local peering gateway is a VCN networking component for connecting VCNs and has nothing to do with IAM API authorization. Option D is incorrect because dynamic groups are used to authorize OCI compute instances and other resources as principals, not human data scientists who authenticate as users.

Answer

A local peering gateway

Answer

A dynamic group

Question 20

A company uses OCI Generative AI to generate product descriptions in XML format. The engineer wants to improve adherence to the XML schema. Which THREE prompt components are most critical? (Select three.)

Accepted Answer

Output format specification: 'Use the following XML schema: ...'. Option C is correct because explicitly specifying the XML schema in the prompt (e.g., 'Use the following XML schema: ...') gives the model the exact structure, element names, and nesting to follow, which is the most direct way to enforce schema adherence. Option D is correct because a clear task instruction such as 'Generate a product description as XML' defines the goal and output type, anchoring the model on producing XML rather than prose. Option E is correct because few-shot examples of valid XML product descriptions demonstrate the schema in practice, letting the model pattern-match correct tags, attributes, and formatting, which strongly improves structured output fidelity. Option A is not appropriate because a temperature of 0.9 increases randomness and creativity, which would reduce deterministic adherence to a strict schema; lower temperature (e.g., 0 or near 0) is preferred for structured output. Option B, while potentially useful for content relevance, is not critical for schema adherence because company background does not constrain XML structure, tags, or validity.

Answer

Setting temperature to 0.9

Answer

Context/background about the company's product line