1Z0-1127-25 Practice Questions 1 — 50 Questions

Question 1

Which THREE techniques effectively reduce query latency in a RAG system?

Accepted Answer

Pre-compute embeddings for all documents. Pre-computing embeddings for all documents eliminates the need to generate embeddings at query time, which is a computationally expensive step. By storing pre-computed vector representations, the system can directly perform similarity searches against the index, significantly reducing latency.

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Use a larger generation model

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Increase the number of shards

Question 2

A prompt engineer notices that the model sometimes generates outputs that include parts of the system prompt or user message verbatim. This is likely a symptom of which common prompt failure?

Accepted Answer

Prompt injection vulnerabilities. Prompt injection vulnerabilities can cause the model to leak or repeat the prompt itself. This is a known failure mode where the model confuses the input with output.

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Ambiguous instructions

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Conflicting requirements

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Insufficient context

Question 3

Which model family is NOT currently available in OCI Generative AI service?

Accepted Answer

OpenAI GPT-4. OpenAI GPT-4 is not available in OCI Generative AI service because OCI's native generative AI offerings are built on open-source and partner models like Meta Llama, Anthropic Claude, and Cohere, but not on OpenAI's proprietary models. OCI Generative AI service provides access to models hosted on OCI, and OpenAI GPT-4 is only accessible via Azure OpenAI Service or direct OpenAI API, not through OCI's managed service.

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Meta Llama

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Anthropic Claude

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Cohere

Question 4

A financial services company is deploying a RAG system for regulatory compliance queries. The system uses OCI Data Science to run a custom embedding model fine-tuned on regulatory documents. The index in OpenSearch uses cosine similarity and HNSW algorithm. Users report that queries containing synonyms to regulatory terms (e.g., "AML" vs "Anti-Money Laundering") often fail to retrieve relevant documents. Which combination of improvements would be MOST effective? (Assume budget and latency constraints)

Accepted Answer

Implement a hybrid search combining keyword and vector search. Hybrid search (combining keyword (BM25) and vector search) catches exact synonym matches from text. Query expansion helps but may not be as reliable. Fine-tuning on synonyms is possible but time-consuming. Increasing HNSW m slightly improves recall but does not address synonym gap.

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Increase the `m` parameter in HNSW to improve recall

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Fine-tune the embedding model further on a dataset of synonyms

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Use query expansion with a thesaurus before embedding

Question 5

During fine-tuning of a large language model on OCI, you notice that the model's performance on the validation set is not improving after several epochs, but the training loss continues to decrease. What is the most likely cause?

Accepted Answer

The model is overfitting to the training data.. When training loss decreases but validation performance stagnates or worsens, the model is overfitting to the training data. It memorizes the training examples but fails to generalize. A high learning rate might cause divergence, not this pattern. Too small training data can contribute to overfitting but is not the direct symptom. An unrepresentative validation set could cause mismatch, but the described pattern is classic overfitting.

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The learning rate is too high.

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The validation set is not representative.

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The training data is too small.

Question 6

Which three characteristics of LLMs can lead to hallucinations? (Select THREE)

Accepted Answer

Ability to generate plausible-sounding text. LLMs are trained to generate text that is statistically plausible and coherent, but they lack mechanisms to verify factual accuracy. This means they can produce sentences that sound convincing and grammatically correct while being entirely false, which is a direct cause of hallucinations.

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Overconfidence in predictions

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Large vocabulary size

Question 7

A company wants to use OCI Generative AI to summarize customer feedback. They need low latency and high throughput. Which configuration should they choose?

Accepted Answer

Dedicated AI cluster with base model. Dedicated AI clusters provide guaranteed compute resources (GPUs) with no multi-tenant contention, ensuring low latency and high throughput for inference workloads. Using a base model avoids the additional overhead of fine-tuning inference, which can introduce latency due to custom weight loading and optimization steps. This combination is optimal for real-time summarization of customer feedback where response time and volume are critical.

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Serverless endpoint with fine-tuned model

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Dedicated AI cluster with fine-tuned model

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Serverless endpoint with base model

Question 8

Which TWO of the following are sources of training data for fine-tuning a model in OCI Generative AI?

Accepted Answer

OCI Object Storage bucket. OCI Object Storage is a supported source for training data when fine-tuning models in OCI Generative AI. The service can directly access data stored in Object Storage buckets via service-level integrations, allowing you to reference large datasets without local uploads.

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OCI File Storage

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OCI Database

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OCI Streaming

Question 9

Which three statements about transformer architecture are correct? (Choose three.)

Accepted Answer

The softmax function is used in the attention mechanism to normalize attention scores.. The softmax function is applied to the raw attention scores (the dot products between queries and keys) to convert them into a probability distribution that sums to 1. This normalization allows the model to assign a relative weight to each token in the sequence, ensuring that the weighted sum of values is stable and interpretable.

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The feed-forward network applies a different set of weights for each token position.

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The encoder-decoder structure is used in GPT models.

Question 10

A developer is getting a 401 Unauthorized error when calling the OCI Generative AI inference API. What is the most likely cause?

Accepted Answer

The request is missing or has an invalid authentication signature. A 401 Unauthorized error specifically indicates a failure in authentication, not authorization or resource availability. The OCI Generative AI inference API requires every request to include a valid signature based on the OCI Signature Version 1 algorithm (RFC 2104 HMAC-SHA256). If the request is missing the Authorization header or the signature is malformed (e.g., incorrect key ID, mismatched signing string, or expired timestamp), the API gateway rejects it with a 401 response.

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The API endpoint has reached its rate limit

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The model does not support the requested parameters

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The model is not deployed

Question 11

Which two are essential components of the Transformer architecture? (Select TWO)

Accepted Answer

Self-attention mechanism. The self-attention mechanism is essential because it allows each token in the input sequence to attend to every other token, capturing long-range dependencies without the sequential bottleneck of RNNs. This mechanism computes attention scores using queries, keys, and values, enabling parallel processing and forming the core of the Transformer's ability to model context.

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Pooling layers

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Recurrent connections

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Convolutional layers

Question 12

A company wants to use OCI Generative AI service to generate marketing copy that adheres to brand guidelines. Which technique should they use?

Accepted Answer

Fine-tune the model with brand-specific data. Fine-tuning a pre-trained model with brand-specific data (Option D) is the correct approach because it adjusts the model's weights to align with the company's unique brand guidelines, tone, and vocabulary. This supervised learning process ensures the generated marketing copy consistently adheres to specific requirements, unlike prompt engineering which relies on ephemeral instructions that may not reliably enforce brand constraints.

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Use model distillation

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Use prompt engineering with a pre-trained model

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Use knowledge distillation

Question 13

An application mixes RAG with other data sources. The vector search returns too many irrelevant chunks. What is the best approach to filter them?

Accepted Answer

Use a reranker model. A reranker model (Option A) is the best approach because it takes the initial set of retrieved chunks and re-orders them based on semantic relevance to the query, effectively filtering out irrelevant chunks. Unlike simple vector similarity, a reranker uses cross-encoding to evaluate the query-chunk pair as a whole, which significantly improves precision when mixing RAG with other data sources.

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Use exact search instead of ANN

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Reduce the number of retrieved chunks

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Increase chunk size

Question 14

A data scientist is using a large language model to summarize customer support tickets. The model occasionally generates summaries that include hallucinated details not present in the original ticket. Which technique would best reduce hallucinations while maintaining summary quality?

Accepted Answer

Implement retrieval-augmented generation (RAG) to ground the model in relevant documents.. Retrieval-Augmented Generation (RAG) reduces hallucinations by grounding the model's output in external, verifiable documents retrieved from a knowledge base. Instead of relying solely on the model's parametric memory, RAG fetches relevant context (e.g., the original ticket) at inference time, ensuring the summary is factually aligned with the source. This maintains summary quality because the model can still generate fluent text while being constrained to the retrieved evidence.

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Use a longer system prompt instructing the model to be factual.

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Fine-tune the model on a large corpus of general text to improve its knowledge.

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Increase the temperature parameter to 0.9 to encourage more deterministic outputs.

Question 15

A company runs batch inference jobs daily using the OCI Generative AI service. The current cost is higher than expected. Which change would most effectively reduce cost while maintaining throughput?

Accepted Answer

Switch from on-demand to dedicated AI cluster with batch endpoint.. Switching from on-demand to a dedicated AI cluster with a batch endpoint reduces cost because dedicated clusters provide reserved capacity at a lower per-token rate compared to on-demand pay-per-token pricing, and batch endpoints allow you to process multiple inference requests in a single job, amortizing overhead and reducing idle time. This combination directly addresses the high cost of per-request on-demand pricing while maintaining the same throughput for daily batch jobs.

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Reduce the max token limit for all requests.

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Use a larger model to reduce retries.

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Increase the number of parallel requests to improve efficiency.

Question 16

Which THREE techniques are commonly used to improve the quality of text generation?

Accepted Answer

Temperature scaling. Temperature scaling is correct because it controls the randomness of token probability distributions by dividing logits before softmax; lower temperatures (e.g., 0.1) make the model more deterministic, while higher temperatures (e.g., 1.5) increase diversity. This directly influences the quality of generated text by balancing coherence and creativity.

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Greedy decoding

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Random sampling

Question 17

A team has deployed a generative AI model using OCI Data Science model deployment. The endpoint is behind a load balancer. Users report that after 5 minutes of inactivity, the first request takes over 30 seconds to respond, while subsequent requests are fast. What is the most likely cause and solution?

Accepted Answer

The model deployment has an idle timeout that scales down to zero; configure a minimum number of instances or use a warm-up request. The described behavior—first request after 5 minutes of inactivity taking over 30 seconds, with subsequent requests fast—is a classic symptom of an idle timeout that scales the model deployment to zero instances. OCI Data Science model deployments support auto-scaling with an idle timeout (default 5 minutes) that can reduce the number of instances to zero when no requests are received. When a new request arrives, it must wait for a new instance to spin up, causing the delay. The solution is to configure a minimum number of instances (e.g., 1) to keep the model warm, or use a warm-up request to prevent the idle timeout from triggering.

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The load balancer is scaling based on CPU utilization; increase the CPU threshold

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The VCN has a network latency issue; use a different availability domain

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The inference code has a lazy initialization; pre-load the model in the deployment script

Question 18

A company is deploying a large language model for a customer service chatbot. The model needs to understand industry-specific jargon and maintain low latency. Which approach best balances these requirements?

Accepted Answer

Fine-tune a small open-source LLM on domain-specific data. Fine-tuning a small open-source LLM on domain-specific data is the best approach because it adapts the model to understand industry-specific jargon while keeping the model small enough to maintain low latency. Unlike larger models, a fine-tuned small model can run efficiently on local hardware, reducing inference time and avoiding the overhead of external API calls or large model sizes.

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Employ retrieval-augmented generation (RAG) with a general model

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Rely solely on prompt engineering with a general model

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Use a large general-purpose LLM with zero-shot prompting

Question 19

A developer is using OCI Generative AI Service to generate product descriptions. The outputs are often too generic and lack brand-specific tone. The developer has a small set of 20 high-quality example descriptions. What is the most efficient approach to improve output quality?

Accepted Answer

Use few-shot prompting by including the 20 examples in the prompt.. Few-shot prompting is the most efficient approach when you have a small set of high-quality examples (20 in this case). It allows the model to infer the desired tone and style directly from the provided examples without requiring any training or fine-tuning, which would be inefficient and potentially ineffective with such a small dataset. In OCI Generative AI Service, few-shot prompting leverages the model's in-context learning capability to adapt its output to the brand-specific tone.

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Fine-tune a base model on the 20 examples.

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Use a more detailed system prompt describing the brand tone.

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Use chain-of-thought prompting to guide the model step by step.

Question 20

A company is deploying a large language model in a customer-facing chatbot. The model's responses must be both accurate and safe. Which combination of techniques should be employed?

Accepted Answer

Use retrieval-augmented generation (RAG) for factual accuracy and a content safety filter for safe outputs.. RAG grounds the model's responses in a verified external knowledge base, reducing hallucinations and improving factual accuracy, while a content safety filter (e.g., a classifier or guardrail) actively blocks toxic or unsafe outputs before they reach the user. This combination addresses both accuracy and safety independently, unlike a single system prompt which is easily bypassed.

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Use only a system prompt instructing the model to be accurate and safe.

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Use a high temperature for creativity and a safety classifier for blocking toxic outputs.

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Fine-tune the model on all historical chat logs and use a high temperature.