1Z0-1127-25 Practice Test 17 — 15 Questions

Question 1

A company uses OCI GenAI to build a content moderation system that filters toxic language in user-generated comments. They have a small labeled dataset of 1,000 comments (500 toxic, 500 non-toxic) and need an efficient solution that balances accuracy, cost, and latency. They are considering different model options: fine-tuning a large LLM (e.g., Cohere Command), using a pre-trained LLM with prompting, fine-tuning a smaller BERT-based classifier, or building a rule-based system. The team has moderate ML experience and wants to deploy using OCI Data Science. Which approach is most efficient for this binary classification task?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Fine-tune a BERT-based classifier (e.g., 'bert-base-uncased') on the dataset.. Fine-tuning a BERT-based classifier (e.g., 'bert-base-uncased') is the most efficient approach because BERT is specifically designed for text classification tasks, requiring far fewer computational resources and lower latency than large LLMs. With only 1,000 labeled samples, BERT can achieve high accuracy through transfer learning, while keeping inference costs minimal—ideal for a production content moderation system on OCI Data Science.

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Develop a rule-based system using regular expressions and keyword lists.

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Use a pre-trained LLM with a toxic/non-toxic prompt.

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Fine-tune the Cohere Command model on the labeled dataset.

Question 2

A company needs to generate vector embeddings for a multilingual document set to support semantic search across English and French documents. Which embedding model should they use?

Accepted Answer

Cohere embed-multilingual-v3.0. Cohere embed-multilingual-v3.0 is specifically trained to produce embeddings that align semantically across more than 100 languages, including English and French, so semantically equivalent sentences in different languages map to nearby vectors. This enables cross-lingual semantic search where an English query can retrieve relevant French documents and vice versa. The other options are either English-only embedding models or generative LLMs not designed for embedding generation.

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Cohere embed-english-v3.0

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Cohere Command R

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Meta Llama 3 8B

Question 3

A data scientist is fine-tuning a model on OCI Generative AI to generate code comments. They use a dataset of 10,000 examples. After fine-tuning, the model generates comments that are too similar to the training data and lack generalization. What is the most likely cause?

Accepted Answer

Too many training epochs.. When a fine-tuned model generates outputs that are too similar to the training data and lack generalization, it is a classic sign of overfitting. Overfitting occurs when the model is trained for too many epochs, causing it to memorize the training examples rather than learning the underlying patterns. In OCI Generative AI, the fine-tuning process adjusts model weights iteratively, and excessive epochs lead to poor performance on unseen data.

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Incorrect tokenizer.

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Insufficient training data.

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Too high learning rate.

Question 4

A team is designing a text classification system using OCI Generative AI. They have a small labeled dataset of 200 examples per class. Which THREE techniques can help improve model performance without requiring additional labeled data?

Accepted Answer

Generate synthetic labeled examples using an LLM and include them in training. Option A is correct because using an LLM to generate synthetic labeled examples (data augmentation) expands the small 200-example-per-class training set without collecting new human-labeled data, which typically improves classifier robustness and performance. Option C is correct because extracting dense embeddings from a pretrained embedding model and training a lightweight classifier such as an SVM is a standard few-shot approach that leverages pretrained representations and works well with limited labeled data. Option E is correct because in-context learning places a few labeled examples directly in the prompt, letting a foundation model perform classification without any parameter updates or additional labeled data. Option B does not belong because enlarging the context window to hold all training examples is not a training technique and does not by itself improve a classifier's learned performance. Option D does not belong because fine-tuning a large decoder-only model on only 200 examples per class is prone to overfitting and is not a reliable way to boost performance without more labeled data.

Answer

Increase the context window of the model to include all training examples

Answer

Fine-tune a large decoder-only model on the 200 examples

Question 5

A developer makes an API call to generate text with top_p=1.5. What is the correct way to fix this error?

Accepted Answer

Set top_p to a value between 0 and 1, e.g., 0.9. The `top_p` parameter, also known as nucleus sampling, must be a probability value between 0 and 1. Setting it to 1.5 is invalid because it exceeds the allowed range, which would cause the API to reject the request. The correct fix is to set `top_p` to a valid value such as 0.9, which restricts token selection to the smallest set whose cumulative probability exceeds that threshold.

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Remove the top_p parameter from the request

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Increase the temperature parameter to compensate

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Use the top_k parameter instead

Question 6

A company wants to translate legal documents from English to Spanish. They have a small parallel corpus of 500 sentence pairs. Which approach is MOST likely to yield the best translation quality?

Accepted Answer

Fine-tune an encoder-decoder model like T5 on the parallel corpus. Fine-tuning an encoder-decoder model like T5 on the 500 sentence pairs is the best approach because it leverages the model's pre-trained knowledge of language structure and translation patterns, then adapts it to the specific legal domain with a small but relevant parallel corpus. This transfer learning method requires far less data than training from scratch and typically outperforms zero-shot prompting for specialized, low-resource translation tasks.

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Use a zero-shot prompt with a decoder-only model like GPT

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Train a new model from scratch on the 500 sentence pairs

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Use a rule-based machine translation system

Question 7

A team is using OCI Generative AI Agents to build a customer support bot. The bot sometimes generates answers that contradict the knowledge base. What is the most likely cause?

Accepted Answer

The chunking strategy for the knowledge base does not capture enough context overlap.. When the chunking strategy lacks sufficient context overlap, the retrieved chunks may omit critical surrounding information, causing the generative AI model to infer missing details incorrectly and produce answers that contradict the knowledge base. In OCI Generative AI Agents, the chunking strategy determines how documents are split into smaller pieces for retrieval; without adequate overlap, the model loses the semantic continuity needed to stay faithful to the source material.

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The max tokens value is too low, truncating the response.

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The temperature parameter is set too high, causing the model to hallucinate.

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The model's repetition penalty is too high.

Question 8

An organization needs to deploy a fine-tuned model for real-time inference with strict latency requirements. They have provisioned a Dedicated AI Cluster with 2 model units. Which statement about this setup is accurate?

Accepted Answer

The cluster provides low-latency, dedicated inference for the fine-tuned model, and you are billed per model unit.. Dedicated AI Clusters provide low-latency dedicated inference, and model units are used to allocate capacity for running models, including custom fine-tuned ones. The cluster is specifically designed for hosting fine-tuned models with consistent performance.

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You must use the shared infrastructure for fine-tuned models; dedicated clusters are only for base models.

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The cluster automatically scales model units based on request load.

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The cluster can only host OCI’s built-in models, not custom fine-tuned models.

Question 9

An organization wants to use an LLM to summarize legal documents. Which consideration is most important for ensuring accurate summaries?

Accepted Answer

Fine-tune the model on a curated legal corpus. Fine-tuning on a curated legal corpus is the most important consideration because legal summarization demands domain-specific terminology, citation conventions, and reasoning patterns that general-purpose models often mishandle. Fine-tuning adapts the model's weights to legal language, improving factual fidelity and reducing hallucinations on statutes, case law, and contracts. While prompting and model size help, domain adaptation is the decisive factor for accuracy in a specialized field.

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Use the largest available general-purpose model

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Rely on zero-shot summarization with careful prompting

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Pre-train a new model from scratch on legal texts

Question 10

A company deploys a fine-tuned Llama 2 model using OCI Generative AI service. They want to ensure low-latency inference for a real-time chat application. Which deployment option should they use?

Accepted Answer

Dedicated AI cluster. A dedicated AI cluster provides reserved compute resources (GPUs) for low-latency, real-time inference by eliminating resource contention. This is essential for a fine-tuned Llama 2 model in a chat application where consistent sub-second response times are required, unlike shared or serverless options that introduce cold starts or queuing delays.

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Batch inference job

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OCI Functions

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Serverless endpoint (standard)

Question 11

Which TWO are best practices for prompt management in production environments?

Accepted Answer

Maintain a prompt library with reusable templates. Option B is correct because maintaining a prompt library with reusable templates lets teams standardize, share, and iterate on prompts across applications, reducing duplication and drift in production. Option C is correct because storing prompts in a version-controlled repository provides change history, peer review, rollback capability, and traceability, which are essential for auditing and safely evolving prompts in production. Option A is not a best practice because system prompts are a key mechanism for defining model behavior, safety constraints, and role, so avoiding them weakens control rather than simplifying management. Option D is wrong because hard-coded strings make prompts difficult to update, test, and version independently of code deployments. Option E is wrong because different use cases require tailored prompts; reusing one prompt everywhere degrades quality and flexibility.

Answer

Avoid using system prompts to keep prompts simple

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Keep all prompts as hard-coded strings in the application code

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Use the same prompt for all use cases to reduce complexity

Question 12

An organization wants to ensure that prompts submitted to an LLM do not contain sensitive customer data. Which practice is most effective?

Accepted Answer

Sanitize user inputs by removing sensitive information before including them in the prompt. Sanitizing user inputs by removing sensitive information before including them in the prompt is the most effective practice because it prevents sensitive data from ever reaching the LLM. This is a proactive data minimization approach aligned with privacy principles (e.g., GDPR, CCPA) and directly addresses the risk of data leakage. By scrubbing PII, credentials, or other confidential details at the input stage, the organization ensures that even if the model logs or memorizes prompts, no sensitive data is exposed. Other options do not directly prevent sensitive data from being included in prompts.

Answer

Use a low temperature to avoid generating sensitive data

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Increase the max tokens to allow the model to ignore sensitive data

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Implement a prompt injection detection system that blocks malicious prompts

Question 13

A developer is using a Cohere Command model via OCI Generative AI. They want the model to generate responses strictly in JSON format for a specific task, but the model sometimes outputs additional explanatory text. Which prompt engineering technique is MOST effective?

Accepted Answer

Add a preamble: 'You are a JSON generator. Output only valid JSON. Do not include any other text.'. A system-level preamble that explicitly instructs the model to act as a JSON generator and to output only valid JSON is the most effective technique because it sets a persistent behavioral constraint at the start of the prompt. Cohere Command models, like most instruction-tuned LLMs, weight system/role instructions heavily and will suppress extraneous explanatory text when told to do so. This directly addresses the root cause (the model's tendency to add commentary) rather than trying to constrain it after the fact.

Answer

Include a single example of a JSON output in the user message

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Set the temperature to 0.0 to make the model deterministic

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Use a stop sequence '}' to force the model to stop after the JSON

Question 14

A healthcare organization is using OCI Generative AI to analyze medical records. They must comply with HIPAA. They have set up a dedicated AI cluster with private endpoints. However, they are concerned about model hallucinations that could lead to incorrect medical advice. They want to minimize hallucinations while maintaining usefulness. Which approach is most effective?

Accepted Answer

Implement a retrieval-augmented generation (RAG) pipeline with a verified medical knowledge base.. A Retrieval-Augmented Generation (RAG) pipeline grounds the model's responses in a verified medical knowledge base, directly reducing hallucinations by ensuring outputs are based on retrieved facts rather than purely generative predictions. This approach maintains usefulness by allowing the model to reference authoritative sources, which is critical for HIPAA compliance and accurate medical advice.

Answer

Use a smaller model to reduce complexity.

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Increase the temperature to encourage diverse outputs.

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Reduce max tokens to force shorter responses.

Question 15

A data scientist wants to fine-tune a generative AI model on proprietary customer data. What is a best practice for preparing the training dataset?

Accepted Answer

Curate a dataset of domain-specific examples with clear input-output pairs.. Fine-tuning a generative AI model on proprietary data requires a curated, domain-specific dataset with clear input-output pairs. This ensures the model learns the desired task (e.g., summarization, classification) without introducing noise or irrelevant patterns, which is critical for OCI Generative AI Service fine-tuning where data quality directly impacts model performance.

Answer

Randomly sample 1000 records from production logs.

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Use the same dataset as the base model's pre-training data.

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Use the largest available public dataset from the internet.