1Z0-1127-25 Practice Test 40 — 15 Questions

Question 1

A developer is using the OCI GenAI Chat API to build a multi-turn customer support chatbot. They want the assistant to always introduce itself as 'SupportBot' and never mention being an AI. How should they configure the API call?

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

Accepted Answer

Set the system message (preamble) to 'You are SupportBot. You must never mention that you are an AI.'. The OCI GenAI Chat API supports a 'system message' or 'preamble' parameter that sets the assistant's behavior and identity for the entire conversation. By setting the preamble to 'You are SupportBot. You must never mention that you are an AI.', the developer enforces the desired persona and restriction across all turns, ensuring the assistant introduces itself as SupportBot and avoids any reference to being an AI.

Answer

Use the Generate API instead and include the instruction in the prompt

Answer

Set the preamble override to a JSON object with the assistant's identity

Answer

Set the first user message to 'Introduce yourself as SupportBot and never say you are an AI.'

Question 2

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.

Answer

Batch inference job

Answer

OCI Functions

Answer

Serverless endpoint (standard)

Question 3

You need to convert a set of customer support tickets into vector embeddings for a similarity search application. Which OCI Generative AI model should you use?

Accepted Answer

Cohere Embed (e.g., embed-english-v3.0). The Cohere Embed models are designed for text-to-vector embedding. The other options are for text generation or reranking.

Answer

Cohere Rerank

Answer

Meta Llama 3

Answer

Cohere Command R

Question 4

An enterprise deployed a custom fine-tuned model for generating financial reports. After the first month, the model's outputs began to include outdated information and occasional factual errors. The team suspects data drift. What is the best course of action?

Accepted Answer

Retrain the model on the latest financial data and monitor for drift.. Data drift occurs when the input data distribution changes over time, causing the model's outputs to become outdated or inaccurate. Retraining the model on the latest financial data realigns it with the current data distribution, and ongoing monitoring helps detect future drift. Option A is incorrect because switching to a newer base model like Llama 3.1 without retraining does not incorporate the latest financial data and may not address domain-specific drift. Option B is incorrect because decreasing the temperature parameter reduces randomness in outputs but does not correct factual errors stemming from data drift. Option D is incorrect because increasing the max tokens value only allows longer responses and does not improve accuracy or address drift.

Answer

Switch to a newer base model like Llama 3.1 without retraining.

Answer

Decrease the temperature parameter to 0.1 to reduce model creativity.

Answer

Increase the max tokens value to allow longer responses.

Question 5

Which TWO techniques are commonly used to reduce the memory footprint of LLM inference?

Accepted Answer

Quantization. Quantization reduces the memory footprint by lowering the precision of model weights and activations from FP32 to lower bit-widths like INT8 or FP16, which directly decreases the memory required to store and compute with the model. KV cache optimization reduces memory usage by efficiently managing the key-value cache during autoregressive decoding, often through techniques like shared memory, pruning, or compression, which is critical for long-context inference.

Answer

Increasing batch size

Answer

Gradient checkpointing

Answer

Using full precision (FP32)

Question 6

An LLM is being used to answer customer queries about a product catalog. The answers are fluent but sometimes include plausible-sounding but incorrect product details. What is this phenomenon called, and which technique is most effective to mitigate it?

Accepted Answer

Hallucination; use Retrieval-Augmented Generation (RAG) with the catalog indexed. Hallucination is the generation of false information; RAG grounds responses in retrieved factual documents, reducing hallucinations.

Answer

Knowledge cutoff; fine-tune the model on the catalog

Answer

Bias amplification; increase temperature

Answer

Overfitting; reduce the model size

Question 7

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Train a custom model from scratch on the policy documents each month

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Fine-tune a base LLM on the policy documents monthly

Question 8

What is the primary difference between pre-training and fine-tuning in the context of large language models?

Accepted Answer

Pre-training trains from scratch, fine-tuning updates all weights on a new dataset. Pre-training trains a model on a large, general corpus to learn language representations; fine-tuning adapts the pre-trained model to a specific task or domain using a smaller labeled dataset.

Answer

Pre-training uses a smaller dataset, fine-tuning uses a larger dataset

Answer

Pre-training produces embeddings, fine-tuning produces text generation

Answer

Pre-training is unsupervised, fine-tuning is always supervised

Question 9

A prompt engineer wants to use chain-of-thought prompting to improve arithmetic reasoning. Which TWO steps are essential for implementing this technique?

Accepted Answer

Provide a few-shot example that includes step-by-step reasoning. Chain-of-thought requires the model to output intermediate reasoning steps, and providing an example with reasoning helps guide the model.

Answer

Add a stop sequence after the final answer

Answer

Use a large max_tokens value to allow long output

Answer

Set temperature to 0.0 for deterministic reasoning

Question 10

A team is building a code generation assistant and needs to choose between fine-tuning a base LLM or using in-context learning with a few examples. They have 500 high-quality code examples. The assistant must generate code for a wide variety of tasks. Which approach is BETTER and why?

Accepted Answer

In-context learning, because it allows the model to adapt to each task dynamically without risking catastrophic forgetting. Fine-tuning with 500 examples may lead to overfitting or catastrophic forgetting, especially when the tasks are diverse. In-context learning with a few examples per task is more flexible and leverages the model's pre-trained knowledge. The small dataset size makes fine-tuning risky.

Answer

Fine-tuning, because it reduces inference cost compared to providing examples each time

Answer

Fine-tuning, because it permanently encodes the examples into the model weights

Answer

In-context learning, because it requires no additional training infrastructure

Question 11

An OCI user wants to generate embeddings for a large corpus of technical documents to enable semantic search. Which type of model should they use?

Accepted Answer

An embedding model like Cohere Embed. Embedding models are specifically designed to produce dense vector representations that capture semantic meaning. They are distinct from generation models. For semantic search, embeddings from an embedding model are compared using cosine similarity.

Answer

A summarization model

Answer

A classification model

Answer

A generation model like Cohere Command

Question 12

A machine learning engineer is evaluating the performance of a translation model using BLEU score. Which THREE statements about BLEU are correct? (Choose three.)

Accepted Answer

BLEU includes a brevity penalty to penalize outputs that are too short. BLEU is a precision-based metric (not recall). It uses modified n-gram precision with a brevity penalty. It correlates reasonably well with human judgment at the corpus level but has known limitations such as not capturing semantic equivalence.

Answer

BLEU measures recall of n-grams by comparing the output to the reference

Answer

BLEU is a recall-oriented metric

Question 13

A developer is building a text generation application using OCI Generative AI and wants to control the creativity of the output. Which THREE sampling parameters can they adjust? (Choose three.)

Accepted Answer

Top-k. Temperature scales the logits before softmax. Top-k limits the sampling pool to the k most likely tokens. Top-p (nucleus) sampling selects from tokens whose cumulative probability exceeds p. Beam search is a decoding strategy (not a sampling parameter), max tokens controls output length, and frequency penalty reduces repetition.

Answer

Max tokens

Answer

Beam search width

Question 14

Which OCI Generative AI model family is optimized for generating text embeddings that capture semantic meaning for tasks like clustering and classification?

Accepted Answer

Cohere Embed. Cohere Embed models are specifically designed for embedding text into vectors. Cohere Command and Meta Llama are generative models, not embedding models.

Answer

Cohere Rerank

Answer

Meta Llama 3

Answer

Cohere Command R

Question 15

A data scientist is comparing BLEU, ROUGE, and BERTScore to evaluate a summarization model. The client cares most about whether the summary captures all key facts from the source document. Which metric is most aligned with this requirement?

Accepted Answer

ROUGE. ROUGE is recall-oriented, measuring how much of the reference content appears in the generated summary, which aligns with capturing key facts. BLEU is precision-oriented, and BERTScore measures semantic similarity but is not specifically recall-focused.

Answer

Perplexity

Answer

BERTScore

Answer

BLEU