1Z0-1127-25 Practice Test 28 — 15 Questions

Question 1

A developer uses a Cohere Command model with a preamble: 'You are a helpful assistant that answers questions based on the provided document.' They also include a user message with a query and a relevant document excerpt. However, the model often ignores the document and uses its own knowledge. Which improvement is MOST effective?

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

Accepted Answer

Use the document-grounded generation format with and tags around the document. Using the document-grounded generation format with and tags around the document is the most effective improvement because it explicitly signals to the model that the enclosed text is the source document to ground its response on. This format is recommended by Cohere for retrieval-augmented generation (RAG) to ensure the model prioritizes the provided document over its parametric knowledge.

Answer

Increase the temperature to 0.7 to reduce reliance on parametric knowledge

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Set frequency penalty to 0.5

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Remove the preamble and rely solely on the user message

Question 2

A company uses OCI Generative AI to power a chatbot for customer support. They notice that the model's responses sometimes contain factual inaccuracies. Which strategy would best reduce hallucination?

Accepted Answer

Implementing Retrieval-Augmented Generation (RAG).. Retrieval-Augmented Generation (RAG) grounds the model's responses in retrieved factual information, directly reducing hallucination. Increasing temperature increases randomness, fine-tuning on a larger corpus may not fix factual accuracy, and reducing max tokens does not affect correctness.

Answer

Increasing the temperature parameter.

Answer

Reducing the max token limit.

Answer

Fine-tuning the model on a larger general corpus.

Question 3

Which tokenization algorithm is commonly used by models like GPT and BERT, and works by merging frequently occurring character pairs iteratively?

Accepted Answer

Byte-Pair Encoding (BPE). Byte-Pair Encoding (BPE) starts with characters and merges the most frequent pairs to create subword units. WordPiece uses a similar likelihood-based approach. SentencePiece is a framework that can use BPE or unigram.

Answer

Morpheme-based tokenization

Answer

SentencePiece

Answer

WordPiece

Question 4

Refer to the exhibit. A user in group GenAIUsers tries to use the `oci generative-ai model chat` command but gets 'not authorized'. Why?

Accepted Answer

The statement should be 'use' instead of 'read'.. The OCI IAM policy for Generative AI requires the 'use' verb to allow inference operations like chat, not the 'read' verb. The 'read' verb only grants permission to view metadata and list resources, not to invoke the model for chat completions. The error 'not authorized' occurs because the policy statement incorrectly uses 'read' instead of 'use', which is the required permission for the `oci generative-ai model chat` command.

Answer

The policy is not active yet.

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The command requires managing the model.

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The group is not in the correct compartment.

Question 5

A company is building a RAG application using OCI Generative AI and wants to store embeddings for document retrieval. Which OCI service is most appropriate for storing and querying vector embeddings?

Accepted Answer

OCI Search with OpenSearch. OCI Search with OpenSearch is the most appropriate service for storing and querying vector embeddings because it natively supports k-nearest neighbor (k-NN) search and vector field types, enabling efficient similarity search over high-dimensional embeddings. This makes it ideal for RAG applications where document retrieval relies on comparing query embeddings against stored document embeddings.

Answer

OCI MySQL Database

Answer

OCI Data Flow

Answer

OCI Object Storage

Question 6

A data scientist is evaluating a summarization model on a news article dataset. They compute ROUGE-L and BLEU scores. The ROUGE-L score is high, but the BLEU score is low. Which of the following best explains this discrepancy?

Accepted Answer

The summaries are very short and use different vocabulary from the reference. ROUGE-L measures longest common subsequence, which captures fluency and structural similarity, while BLEU measures n-gram precision, which is sensitive to vocabulary overlap and length. A high ROUGE-L with low BLEU suggests the summaries are structurally similar but use different words and are shorter, leading to low n-gram precision.

Answer

The BLEU score is unreliable for summarization tasks

Answer

The model is overfitting to the training data

Answer

The summaries are too long and contain many irrelevant n-grams

Question 7

An OCI Generative AI user wants to host a fine-tuned model with guaranteed low latency for a production application. Which option should they choose?

Accepted Answer

Provision a Dedicated AI Cluster with model units for the fine-tuned model. A Dedicated AI Cluster with model units is the only OCI Generative AI hosting option that provides guaranteed, isolated compute capacity for a fine-tuned model, ensuring consistent low latency and throughput for production workloads. Model units let you size the cluster to your performance requirements, and the fine-tuned model is hosted on that dedicated infrastructure rather than sharing capacity with other tenants.

Answer

Use the Chat API with a base model

Answer

Use the on-demand shared inference endpoint

Answer

Use the OCI Generative AI Playground

Question 8

A developer wants to create a LangChain chain that takes a user's question, retrieves relevant documents from a vector store, and then generates an answer using an LLM. Which chain is MOST appropriate for this task?

Accepted Answer

RetrievalQAChain. RetrievalQAChain is specifically designed for question-answering over a vector store: it takes a user query, retrieves relevant documents from the vector store, and passes them as context to an LLM to generate an answer. This matches the developer's requirement exactly, without any extra conversational memory or multi-step orchestration.

Answer

ConversationalRetrievalChain

Answer

SequentialChain

Answer

LLMChain

Question 9

A company uses RAG (Retrieval-Augmented Generation) with OCI OpenSearch and OCI Generative AI. The system retrieves irrelevant documents. What is the first step to debug?

Accepted Answer

Check the embeddings quality. When RAG retrieves irrelevant documents, the most common root cause is poor embedding quality—vectors that fail to capture semantic similarity between the query and the documents. Checking embeddings (e.g., cosine similarity scores, dimensionality, or model used) is the logical first step before adjusting retrieval parameters or the LLM itself.

Answer

Use a different LLM

Answer

Increase the number of retrieved documents

Answer

Lower the temperature

Question 10

A developer notices that the RAG system returns irrelevant chunks when the user query contains typos or abbreviations. Which technique would BEST improve retrieval robustness for such queries?

Accepted Answer

Implement query rewriting or expansion using a language model before embedding.. Query rewriting or expansion using a language model (LLM) directly addresses typos and abbreviations by generating a corrected or enriched query before embedding. This improves the semantic alignment between the user's intent and the vector search, ensuring that even noisy input retrieves relevant chunks. Techniques like spelling correction or synonym expansion at query time are far more effective than post-retrieval fixes or parameter tuning.

Answer

Decrease the chunk size to focus on smaller units.

Answer

Increase the number of retrieved chunks to cover more variations.

Answer

Use a spell-checker on the retrieved chunks.

Question 11

In the self-attention mechanism, what is the role of the 'scaling factor' (division by sqrt(d_k)) in the softmax computation?

Accepted Answer

To prevent the softmax from saturating and producing small gradients. Scaling prevents the dot products from growing too large in magnitude, which would push softmax into regions with extremely small gradients.

Answer

To make the attention mechanism translation invariant

Answer

To increase the variance of attention scores

Answer

To ensure the sum of attention weights equals 1

Question 12

A company uses OCI Generative AI service for customer support summarization. They notice the model frequently misses key details and generates hallucinations. What should they do first?

Accepted Answer

Adjust the prompt to be more specific and include few-shot examples.. Adjusting the prompt to be more specific and including few-shot examples is the first and most effective step to reduce hallucinations and improve detail capture in OCI Generative AI. Prompt engineering directly guides the model's attention and output format without altering core parameters or models, addressing the root cause of missing key details.

Answer

Increase the temperature parameter.

Answer

Use a different base model.

Answer

Increase the max tokens.

Question 13

Which prompt pattern is most suitable when a user needs to generate a structured report following a specific template every time?

Accepted Answer

Template pattern. The template pattern is designed for generating structured output that follows a specific template every time. It provides a predefined format with placeholders that the model fills in, ensuring consistency. This is ideal for reports that must adhere to a standard structure.

Answer

Recipe pattern

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ReAct pattern

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Role prompting

Question 14

A data scientist is using OCI Generative AI Service to generate product descriptions. They notice that the output often repeats phrases. Which parameter adjustment would MOST directly address this issue?

Accepted Answer

Increase the frequency penalty. The frequency penalty directly reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. In OCI Generative AI Service, this parameter subtracts a fixed value from the log-probability of each token each time it is generated, making repeated tokens less likely to be chosen again. This is the most direct mechanism to address repetitive output.

Answer

Increase the temperature

Answer

Increase the max tokens

Answer

Decrease the top-p value

Question 15

A company is using OCI Generative AI service to power a customer support chatbot. They observe that the chatbot sometimes provides outdated information because the model was trained on data up to 2022. They want to incorporate real-time knowledge without retraining the model. Which approach should they use?

Accepted Answer

Implement a Retrieval-Augmented Generation (RAG) pattern using OCI OpenSearch.. Retrieval-Augmented Generation (RAG) allows the model to access real-time information from an external knowledge base, such as OCI OpenSearch, without retraining. This pattern retrieves relevant documents or data at inference time and injects them into the prompt, enabling the model to answer with up-to-date context. It directly addresses the need for real-time knowledge while keeping the base model static.

Answer

Increase the max-tokens parameter to allow longer responses.

Answer

Use prompt engineering to instruct the model to ignore old information.

Answer

Fine-tune the model with recent data from 2023 onwards.