1Z0-1127-25 Practice Test 21 — 15 Questions

Question 1

A prompt engineer is refining a prompt that currently produces outputs with excessive repetition. Which TWO parameters can be adjusted to reduce repetition? (Choose two.)

Accepted Answer

Frequency penalty. Frequency penalty (B) is correct because it penalizes tokens proportionally to how many times they have already appeared in the generated text, directly discouraging the model from repeating the same words or phrases. Presence penalty (C) is also correct because it applies a flat penalty to any token that has already appeared at least once, pushing the model toward introducing new tokens and thus reducing repetition. Temperature (A) only controls randomness/creativity of sampling and does not specifically target repeated tokens, so it is not the intended answer. Max tokens (D) merely caps output length and does not address repetition within the generated content. Top-p (E) is a nucleus sampling parameter that limits the cumulative probability mass considered, but it does not penalize tokens for recurring, so it does not directly reduce repetition.

Answer

Temperature

Answer

Max tokens

Answer

Top-p

Question 2

Based on the exhibit, what is the primary action the developer must take to successfully make the inference request?

Accepted Answer

Reduce max_new_tokens to 2000 to stay within the context length.. The error indicates that the combined length of the input prompt and the requested max_new_tokens exceeds the model's context length limit (e.g., 4096 tokens). Reducing max_new_tokens to 2000 ensures the total token count stays within the allowed context window, allowing the inference request to succeed. This is a fundamental constraint in transformer-based LLMs where the model cannot generate beyond its fixed context size.

Answer

Increase max_new_tokens to 5000 to get a longer response.

Answer

Ignore the error and retry the request.

Answer

Switch to a model in a different region.

Question 3

An AI engineer is deploying a RAG pipeline using OCI Generative AI. They notice the generated answers sometimes include information not present in the retrieved documents. What is the MOST likely cause?

Accepted Answer

The generation model is hallucinating because it does not rely solely on the retrieved context. The generation model in a RAG pipeline is designed to leverage retrieved context but can still produce outputs not grounded in that context, a phenomenon known as hallucination. This occurs when the model relies on its parametric knowledge or statistical patterns rather than strictly adhering to the retrieved documents, especially if the prompt does not enforce strict grounding or the model's training biases override the context.

Answer

The chunking strategy splits sentences across chunks

Answer

The embedding model is too small to represent document semantics

Answer

The context window of the generation model is exceeded

Question 4

A developer is using LangChain's ConversationBufferMemory to store chat history. They notice that after many turns, the prompt becomes too large and exceeds the model's context window. What is the BEST memory type to use for this scenario?

Accepted Answer

ConversationSummaryMemory. ConversationSummaryMemory periodically summarizes the conversation, keeping the prompt size manageable. It retains the gist of the conversation while reducing token usage.

Answer

ConversationEntityMemory

Answer

ConversationBufferMemory with a large max_token_limit

Answer

ConversationStringBufferMemory

Question 5

A developer notices that an LLM-based question-answering system sometimes provides answers that are correct but from an outdated version of the knowledge base. The system uses RAG with a vector database updated daily. What is the MOST likely root cause?

Accepted Answer

The embedding model was not re-run on the updated documents, so the index contains old embeddings. The core issue is that the vector database index still contains old embeddings. Even though the knowledge base documents are updated daily, if the embedding model is not re-run on those updated documents, the vector representations in the index remain stale. When the RAG system retrieves, it fetches these outdated embeddings, leading to correct but outdated answers. This is a classic index synchronization problem in RAG pipelines.

Answer

The retrieval top-k parameter is set too high

Answer

The chunking strategy splits documents into too-small pieces

Answer

The LLM's training data has a knowledge cutoff date

Question 6

Which input type should be used with the Cohere Embed API when generating embeddings for a query in a semantic search system?

Accepted Answer

search_query. The Cohere Embed API uses the `search_query` input type specifically for embedding search queries in a semantic search system. This tells the model to optimize the embedding for matching against documents that were embedded with the `search_document` type, ensuring proper alignment in the vector space for retrieval tasks.

Answer

search_document

Answer

clustering

Answer

classification

Question 7

A developer wants to compare two sentences for semantic similarity using embeddings. Which distance or similarity metric is most commonly used for dense vector representations?

Accepted Answer

Cosine similarity. Cosine similarity measures the cosine of the angle between two vectors, focusing on orientation rather than magnitude. For dense embeddings from models like Word2Vec, BERT, or sentence transformers, cosine similarity is the standard metric because it captures semantic similarity regardless of vector length.

Answer

Jaccard similarity

Answer

Manhattan distance

Answer

Euclidean distance

Question 8

An application uses ConversationalRetrievalChain with a vector store retriever. Users report that the chatbot sometimes provides answers that are not grounded in the retrieved documents. Which step in the RAG pipeline is most likely the cause?

Accepted Answer

The LLM prompt does not instruct the model to base its answer solely on the provided context. The ConversationalRetrievalChain in LangChain relies on the LLM prompt to instruct the model to base its answer solely on the provided context. If the prompt does not include such an instruction, the LLM may generate answers using its pre-trained knowledge rather than the retrieved documents, leading to ungrounded responses. This is a common oversight in RAG pipeline design where the prompt template fails to enforce context-only generation.

Answer

The chunk_size in the text splitter is too large

Answer

The embedding model is not compatible with the retriever

Answer

The retriever is returning irrelevant documents

Question 9

A data scientist is using the OCI Generative AI service to generate text completions. The API calls are returning HTTP 400 errors with the message 'Invalid model parameters'. What is the most likely cause?

Accepted Answer

One or more model parameters (e.g., temperature, top_p) are outside the accepted range. The HTTP 400 error with 'Invalid model parameters' directly indicates that one or more of the parameters sent in the API request (such as temperature, top_p, max_tokens, or stop sequences) are outside the acceptable range defined by the OCI Generative AI service. For example, temperature must be between 0 and 1, and top_p between 0 and 1, and sending a value like 2.0 for temperature would trigger this error. The other options (expired key, rate limit, incorrect endpoint) would produce different HTTP status codes or error messages.

Answer

The API key is expired

Answer

The request exceeds the rate limit

Answer

The endpoint URL is incorrect

Question 10

A team fine-tuned a model using T-Few and validated it. They now want to deploy this fine-tuned model to a dedicated AI cluster for low-latency inference. What must they do FIRST?

Accepted Answer

Create a dedicated AI cluster with a specified number of model units. To deploy a fine-tuned model to a dedicated AI cluster for low-latency inference, you must first create the dedicated AI cluster and specify the number of model units. This cluster provides isolated compute resources that ensure consistent, low-latency performance, unlike the shared serverless endpoint. The fine-tuned model is then deployed onto this cluster, not copied to Object Storage first.

Answer

Copy the fine-tuned model to an Object Storage bucket

Answer

Increase the temperature parameter in the inference request

Answer

Delete the base model to free up capacity

Question 11

Which of the following best describes the role of positional encoding in the Transformer architecture?

Accepted Answer

To provide information about the order of tokens in the input sequence. Positional encoding is essential in the Transformer architecture because the self-attention mechanism processes all tokens in parallel and has no inherent sense of order. By adding sinusoidal or learned positional vectors to the input embeddings, the model gains information about the relative or absolute position of each token in the sequence, enabling it to understand word order and sequence structure.

Answer

To compress the input sequence length

Answer

To increase the dimensionality of the hidden states

Answer

To reduce the effect of vanishing gradients

Question 12

In the OCI Generative AI Playground, a developer wants to control how creative the model responses are. Which parameter should they adjust?

Accepted Answer

Temperature. Temperature controls randomness; higher values produce more creative outputs. The other parameters control other aspects of generation.

Answer

Presence penalty

Answer

Max tokens

Answer

Stop sequences

Question 13

A prompt engineer wants the LLM to output a list of countries in a specific JSON format with fields 'country_code' and 'name'. Which prompt component should be used to define this structure?

Accepted Answer

Output format specification. The output format specification is the prompt component explicitly designed to define the structure, schema, or layout of the LLM's response. In this scenario, the prompt engineer needs the LLM to output a list of countries with specific JSON fields ('country_code' and 'name'), which is a direct instruction about the format of the output, not the task itself. This component ensures the LLM adheres to a precise data structure, such as JSON, XML, or a table, which is critical for downstream parsing or integration.

Answer

Constraints

Answer

Context/background

Answer

Task instruction

Question 14

During iterative refinement, a prompt engineer tests two prompt variants on the same 100 inputs and measures accuracy. Variant A yields 85% accuracy, Variant B yields 82%. However, Variant B's outputs are more concise and preferred by users. What should the engineer do NEXT?

Accepted Answer

Define clear evaluation criteria that balance accuracy and conciseness, then re-evaluate. The engineer should first define evaluation criteria that explicitly balance accuracy and conciseness, then re-evaluate both variants against those criteria. Without agreed criteria, choosing between a 3-point accuracy gap and better user preference is arbitrary and not reproducible.

Answer

Select Variant B because user preference outweighs the small accuracy difference

Answer

Select Variant A because accuracy is the primary metric

Answer

Run a larger A/B test with statistical significance check before deciding

Question 15

Which OCI Generative AI service component is specifically designed to convert text into vector representations for use in semantic search and clustering?

Accepted Answer

Embedding. Embedding is the correct answer because it is the OCI Generative AI service component that converts text into dense vector representations (embeddings). These numerical vectors capture semantic meaning, enabling similarity comparisons for tasks like semantic search and clustering, where documents with similar vectors are considered contextually related.

Answer

Chat

Answer

Summarisation

Answer

Agents