1Z0-1127-25 Practice Test 47 — 15 Questions

Question 1

A developer wants to integrate OCI GenAI into a Java application. Which SDK should they use?

Accepted Answer

OCI Java SDK.. The OCI Java SDK is the official Oracle Cloud Infrastructure software development kit for Java, providing typed clients for services including OCI Generative AI. A Java application integrating OCI GenAI must use the Java SDK to authenticate, construct requests, and call the GenAI inference endpoints. The other SDKs target different languages and cannot be used from Java.

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OCI JavaScript SDK.

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OCI Python SDK.

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OCI CLI.

Question 2

Refer to the exhibit. The output is very short and cuts off mid-sentence. Which parameter is most likely the cause?

Accepted Answer

Max-tokens is too low. The 'max-tokens' parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. When set too low, the model is forced to stop generating output before completing its thought, resulting in a truncated or cut-off sentence. This is the most direct cause of an output that ends abruptly mid-sentence.

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Temperature is too high

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Model ID incorrect

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Top-p is too high

Question 3

A company wants to use OCI Generative AI Agents to build a question-answering system over documents stored in OCI Object Storage. Which component acts as the knowledge source for the agent?

Accepted Answer

A knowledge base created from the Object Storage bucket. In OCI Generative AI Agents, a knowledge base is created from a data source such as an Object Storage bucket, and it serves as the repository of documents that the agent uses to answer questions. The knowledge base is the core component that enables retrieval-augmented generation (RAG) by indexing and retrieving relevant information.

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A Dedicated AI Cluster

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The OCI Generative AI Playground

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An endpoint created via the InferenceClient

Question 4

A prompt engineer is designing a system that generates SQL queries from natural language. The model sometimes produces unsafe queries (e.g., DROP TABLE). Which constraint in the system prompt would BEST mitigate this risk?

Accepted Answer

Include a constraint: 'You may only generate SELECT statements. Do not generate DDL or DML statements like DROP, DELETE, INSERT, or UPDATE.'. It explicitly prohibits the model from generating DDL (Data Definition Language) and DML (Data Manipulation Language) statements, directly addressing the risk of unsafe queries like DROP TABLE. By constraining the output to only SELECT statements, the prompt enforces a strict policy that prevents the model from producing destructive or modifying SQL commands, which is the most effective mitigation among the options.

Answer

Use a few-shot prompt with only safe SELECT examples

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Use role prompting: 'You are an expert SQL developer'

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Set frequency penalty to 1.0 to avoid repetitive unsafe patterns

Question 5

Refer to the exhibit. What is the solution?

Accepted Answer

Use a different base model that supports fine-tuning.. The exhibit indicates that the base model does not support fine-tuning, which is a prerequisite for adapting a large language model to a specific task or domain. Using a different base model that supports fine-tuning allows the model to be customized through supervised learning on task-specific data, enabling it to learn new patterns and improve performance. This is the correct solution because without fine-tuning capability, the model cannot be effectively adapted regardless of other hyperparameter adjustments.

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Change the learning rate.

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Increase the training epochs.

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Use a different compartment.

Question 6

A healthcare organization plans to deploy a RAG application on OCI that handles sensitive patient data. They require that all LLM inference and embedding processing happen within a controlled environment to avoid data leakage to public endpoints. Which OCI feature should they use?

Accepted Answer

OCI Dedicated AI Cluster. OCI Dedicated AI Cluster provides a fully isolated, single-tenant infrastructure for running AI workloads, including LLM inference and embedding processing. This ensures all data processing occurs within a controlled environment without traversing public endpoints, meeting the healthcare organization's requirement to prevent data leakage of sensitive patient data.

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OCI Data Labeling

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OCI Vault

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OCI Data Masking

Question 7

A financial institution uses OCI GenAI to power a customer support chatbot. The compliance team requires that responses are strictly consistent with regulatory guidelines and approved responses. The company has a curated set of question-answer pairs that cover common scenarios. They want to ensure that the chatbot never deviates from these approved answers. The data science team is considering various approaches to enforce this consistency. Which approach is most effective?

Accepted Answer

Fine-tuning the model on the curated dataset of question-answer pairs.. Fine-tuning the model on the curated dataset of question-answer pairs is the most effective approach to enforce strict consistency with approved responses. Fine-tuning adjusts the model's weights to learn the specific patterns and content of the curated answers, making it highly likely to reproduce them. This is more reliable than prompt-based methods for ensuring adherence to a fixed set of responses.

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Few-shot prompting with three example responses in every query.

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Using a large context window to include all regulatory guidelines in the prompt.

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Setting a low temperature (0.1) to make outputs deterministic.

Question 8

A team fine-tuned a Cohere Command R model using the T-Few technique on a dataset of JSONL prompt/completion pairs. After deployment, they observe that the model's responses are too repetitive. Which parameter adjustment in the OCI Generative AI Playground would BEST address this issue?

Accepted Answer

Increase frequency penalty. Repetitive output in large language models is directly countered by the frequency penalty, which penalizes tokens based on how often they have already appeared in the generated text, discouraging the model from repeating the same words or phrases. Increasing the frequency penalty makes the model less likely to loop on identical tokens, directly addressing the repetition symptom. This parameter is available in the OCI Generative AI Playground and is the most targeted adjustment for repetition.

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Increase max tokens

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Decrease temperature

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Add a stop sequence

Question 9

A prompt engineer is designing a ReAct pattern prompt to enable an LLM to use external tools. Which of the following is a key characteristic of the ReAct pattern?

Accepted Answer

The model produces a chain of thought followed by an action, then observes the result and continues reasoning. The ReAct (Reasoning + Acting) pattern interleaves chain-of-thought reasoning with discrete actions (tool calls). The model first thinks step-by-step, then emits an action, receives an observation from the environment, and uses that observation to continue reasoning. This loop repeats until the model decides it has enough information to produce a final answer. Option B accurately captures this iterative think-act-observe cycle, which is the defining characteristic of ReAct.

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The model uses a single tool call to answer the question

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The model generates a final answer without any intermediate steps

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The model is fine-tuned specifically for tool use

Question 10

A company is deploying a fine-tuned Cohere model on OCI Generative AI service for real-time inference. They need to ensure low latency even during demand spikes. Which configuration should they prioritize?

Accepted Answer

Use a dedicated AI cluster for the endpoint.. A dedicated AI cluster provides isolated compute resources (GPUs) that are not shared with other tenants or workloads, ensuring consistent low latency even under demand spikes. This is critical for real-time inference because shared endpoints can experience resource contention and throttling during high traffic, while a dedicated cluster guarantees predictable performance.

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Enable model caching on the endpoint.

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Use streaming responses.

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Increase the max tokens parameter.

Question 11

A data scientist is designing a prompt to generate a structured report with sections for Summary, Findings, and Recommendations. Which output format specification in the prompt would be MOST effective?

Accepted Answer

"Provide the output in JSON format with keys: 'summary', 'findings', and 'recommendations'.". Specifying JSON with explicit keys ('summary', 'findings', 'recommendations') gives the model an unambiguous, machine-parseable schema that enforces the exact three-section structure. This is the most effective format specification because it constrains the output to a deterministic structure that downstream code can consume directly, eliminating ambiguity about section names or ordering.

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"Write a report with three sections: Summary, Findings, Recommendations."

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"Return the report in bullet points."

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"Output the report as a markdown document with headings."

Question 12

A financial services firm uses an OCI Generative AI Cohere Command model to summarize quarterly earnings transcripts. Analysts report that summaries are accurate but frequently 900+ words when the downstream report template only allows 250 words. The team's prompt currently reads: 'Summarize the following transcript.' Which prompt change MOST reliably constrains output length while preserving the key financial figures?

Accepted Answer

Add 'Limit the summary to approximately 250 words, and prioritize revenue, EPS, and guidance figures.' and set max_tokens to a value that caps generation.. Combining an explicit word-count instruction with the max_tokens generation parameter is the reliable way to bound summary length while directing the model to keep high-value content. Instructions alone can be ignored; the parameter alone can truncate mid-thought. Together they enforce the limit and preserve the financial figures the analysts need.

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Add a stop sequence containing the word 'summary' so generation halts after the first paragraph.

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Increase temperature to 1.0 so the model varies its output and naturally produces shorter summaries.

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Switch to a larger context window model so the transcript fits entirely and the model self-compresses the content.

Question 13

A developer is using the OCI Generative AI Playground with a Meta Llama 3.1 70B model to draft marketing copy. They need the same prompt to yield noticeably different creative variations each time it is submitted, while still remaining coherent. Which parameter change should they make first?

Accepted Answer

Increase the temperature value in the model parameters.. Temperature directly controls sampling randomness by rescaling the model's probability distribution before a token is chosen. A higher temperature flattens that distribution so less probable words become more likely, which yields diverse creative drafts from an identical prompt. Length limits, repetition penalties, and candidate-pool truncation affect other behaviours and do not drive variation across runs.

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Increase the frequency penalty value in the model parameters.

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Increase the max tokens value in the model parameters.

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Increase the top k value in the model parameters.

Question 14

A data engineer is building a classification pipeline on OCI Generative AI using a Meta Llama chat model. They need the model to always respond with exactly one of four labels: BILLING, TECHNICAL, ACCOUNT, or OTHER. During testing, the model sometimes returns sentences like 'This seems like a billing issue.' Which prompt engineering technique is MOST reliable for forcing a single-token label output?

Accepted Answer

Add a system instruction telling the model to answer only with the label, and end the prompt with 'Label:' so the model continues with the label token.. Constraining output to a fixed label set is best achieved by combining a system-level instruction with a primed assistant prefix such as 'Label:'. The prefix acts as a completion cue, so the model continues with the label token instead of a full sentence. Sampling tweaks, token limits, and streaming do not enforce vocabulary constraints.

Answer

Enable streaming responses so the model emits the label first and the rest of the sentence can be ignored.

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Increase the top_p value to 0.99 so the model samples more broadly and eventually picks the correct label.

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Set the max_tokens parameter to 1 so only the first token of the response is returned.

Question 15

A support automation team feeds 40 pages of product documentation into the prompt of an OCI Generative AI Cohere Command R model and asks a single question about a clause buried on page 32. The model answers confidently but incorrectly. The team wants to keep using the same model and improve accuracy without changing the underlying documents. Which change is MOST appropriate?

Accepted Answer

Retrieve only the most relevant passages and place them near the end of the prompt before the question.. Accuracy with long inputs improves when the relevant evidence is retrieved and positioned close to the query, countering the lost-in-the-middle attention pattern. Retrieval plus strategic placement gives the model a short reasoning path to the grounded clause. Decoding parameters and polite instructions about reading carefully do not change how attention is distributed across a long context.

Answer

Raise the max tokens value so the model has room to reason through all 40 pages before answering.

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Increase the temperature so the model explores a wider set of interpretations of the documentation.

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Add a system instruction telling the model to read the entire documentation carefully before answering.