1Z0-1127-25 Practice Test 27 — 15 Questions

Question 1

Which of the following is an example of a prompt template?

Accepted Answer

"Write a product description for {product_name} targeting {audience}.". A template uses placeholders like {product_name} that are filled at runtime, making prompts reusable and dynamic.

Answer

"You are a helpful assistant."

Answer

"Translate the following English text to French: 'Hello'"

Answer

"Explain the concept of gravity."

Question 2

Which TWO of the following are true about positional encoding in transformer models?

Accepted Answer

It provides the model with information about the order of tokens in a sequence. Option B is correct because positional encoding exists specifically to inject sequence-order information into a transformer, which otherwise processes all tokens in parallel and has no inherent notion of token position. Option C is correct because the standard implementation adds the positional encoding vector element-wise to the token embedding vector before the combined representation is fed into the transformer layers (e.g., in the original 'Attention Is All You Need' architecture). Option A is incorrect because the canonical sinusoidal positional encodings are fixed deterministic functions of position, not learned per-position parameters, and even learned variants are not 'independent for each position in the training data.' Option D is incorrect because positional encoding does not replace self-attention; self-attention remains the core mechanism for contextual mixing, and positional encoding only supplements it with order information. Option E is incorrect because positional encoding is used in encoder-only, decoder-only, and encoder-decoder transformer architectures alike, not just BERT-style encoders.

Answer

It is learned independently for each position in the training data

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It replaces the need for self-attention

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It is only used in encoder-only architectures like BERT

Question 3

A company is using dedicated AI cluster for fine-tuning. Which TWO best practices help optimize cost?

Accepted Answer

Manually scale down the cluster when not in use.. Option B is correct because manually scaling down (or terminating) the dedicated AI cluster when it is not actively fine-tuning eliminates idle compute charges, which is the primary cost driver for dedicated clusters. Option E is correct because selecting the smallest model that still meets the task's quality requirements reduces GPU memory, compute time, and node count needed for fine-tuning, directly lowering cost. Option A is incorrect because a larger replica count increases the number of running nodes and thus raises cost without necessarily improving fine-tuning throughput. Option C is incorrect because a managed serving endpoint is for inference, not fine-tuning, so it does not address the training cost scenario. Option D is incorrect because leaving the cluster running continuously incurs charges even when idle, which is the opposite of a cost optimization best practice.

Answer

Use the largest replica count.

Answer

Use the managed serving endpoint instead.

Answer

Leave the cluster running continuously.

Question 4

An organization needs to select a tokenisation algorithm for a multilingual LLM that will process English, Chinese, and Korean text efficiently. Which tokenisation method is BEST suited for this requirement?

Accepted Answer

SentencePiece. SentencePiece is language-agnostic and treats input as a raw byte stream, making it highly effective for multilingual models covering English, Chinese, and Korean without language-specific pre-tokenization. It supports subword regularization and handles scripts without whitespace delimiters (Chinese, Korean) gracefully.

Answer

WordPiece

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Byte-Pair Encoding (BPE)

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Character-level tokenisation

Question 5

In OCI OpenSearch, a k-NN search query returns results with low precision. The index uses HNSW algorithm. The search parameters are: `k=10`, `ef_search=100`. To improve recall without significantly increasing latency, which parameter should be adjusted?

Accepted Answer

Increase `ef_search`. Increasing `ef_search` expands the search radius in the HNSW graph, allowing the algorithm to explore more candidate nodes during the search phase. This directly improves recall by reducing the chance of missing true nearest neighbors, while the impact on latency is typically sub-linear because HNSW's hierarchical structure limits the additional traversal cost.

Answer

Decrease `ef_search`

Answer

Decrease `k`

Answer

Increase `k`

Question 6

In a LangChain RAG pipeline using Oracle AI Vector Search, the developer wants to retrieve chunks that are both relevant and diverse to cover multiple aspects of a query. Which retrieval method should they configure on the retriever?

Accepted Answer

Maximal Marginal Relevance (MMR). Maximal Marginal Relevance (MMR) is the correct retrieval method because it explicitly balances relevance to the query with diversity among the retrieved chunks. In a LangChain RAG pipeline using Oracle AI Vector Search, MMR re-ranks the initial similarity results to minimize redundancy, ensuring the final set covers multiple aspects of the query rather than returning near-duplicate chunks.

Answer

Threshold-based search

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Random sampling of the top-k results

Answer

Similarity search with a high k value

Question 7

Which of the following is a known limitation of large language models?

Accepted Answer

They sometimes produce plausible but incorrect information (hallucinations). A well-documented limitation of large language models (LLMs) is their tendency to generate plausible-sounding but factually incorrect or nonsensical information, commonly referred to as 'hallucinations'. This occurs because LLMs are trained to predict the next token based on statistical patterns in their training data, not to verify facts or maintain a ground-truth database. This limitation is a core challenge in deploying LLMs for tasks requiring high factual accuracy, such as legal or medical advice.

Answer

They can generate text in multiple languages

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They support few-shot learning via in-context examples

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They can be fine-tuned for domain-specific tasks

Question 8

Refer to the exhibit. A developer runs the command and receives the error. What is the issue?

Accepted Answer

The max-tokens value exceeds the allowed range.. The error returned by the API indicates that the max-tokens parameter is outside the permitted range for the model. Each model enforces a maximum completion-token limit (for example, 4096 or 16384 depending on the model), and requesting more than that limit causes a validation error before any inference runs. The fix is to lower max-tokens to a value within the model's documented range.

Answer

The message is too short.

Answer

The chat-id is invalid.

Answer

The endpoint is incorrect.

Question 9

Which LangChain memory type stores the entire conversation history as a list of messages and is best for simple, short conversations?

Accepted Answer

ConversationBufferMemory. ConversationBufferMemory stores the entire conversation history as a list of messages and passes all of it to the LLM on each turn. It is the simplest memory type and is best for short conversations where context fits within the model's token limit. It does not summarize, window, or truncate.

Answer

ConversationTokenBufferMemory

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ConversationSummaryMemory

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ConversationBufferWindowMemory

Question 10

When chunking a large Python code repository for a RAG application, which chunking strategy is best suited to preserve code semantics and functionality?

Accepted Answer

Semantic chunking based on function and class definitions. Semantic chunking based on function and class definitions preserves code semantics by ensuring that logically cohesive units (e.g., a complete function or class) remain intact. This prevents the RAG system from retrieving fragmented code that would lose context or break syntax, which is critical for accurate code generation or understanding.

Answer

Fixed-size chunking with 512 tokens

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Sentence-level chunking

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Character-level chunking

Question 11

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.

Answer

Fine-tune a base model on the 20 examples.

Answer

Use a more detailed system prompt describing the brand tone.

Answer

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

Question 12

A team is building a code generation assistant that should output Python code without any explanations. They want to ensure the model only returns the code block. Which prompt strategy is MOST effective?

Accepted Answer

Set the system prompt to 'You are a code generator. Output only Python code. Do not include any explanations.' and the user message to 'Generate code for...'. Setting the system prompt to explicitly instruct the model to output only Python code without explanations directly enforces the desired behavior at the highest priority level of the prompt hierarchy. This approach leverages the system prompt's role as a persistent instruction that overrides user messages, ensuring the model strictly returns code blocks without extraneous text.

Answer

Use chain-of-thought prompting to reason about the code before writing it

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Use a zero-shot prompt: 'Write Python code for the following task:'

Answer

Set max tokens to a low value to limit output length

Question 13

A company deployed OCI Generative AI for a customer service chatbot. They are using the Cohere command model. The chatbot is generating responses that are too brief and often cut off mid-sentence. They have limited budget. What should they do?

Accepted Answer

Increase max tokens to 1024.. The chatbot's responses are too brief and cut off mid-sentence, which indicates the model is hitting the maximum token limit for generation. Increasing the max tokens to 1024 allows the Cohere command model to produce longer, complete responses without truncation. This is the most direct and cost-effective fix for the described symptom.

Answer

Decrease the temperature to 0.2.

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Increase the temperature to 0.9.

Answer

Use a different base model like Llama.

Question 14

A developer needs to generate embeddings for a set of search queries to be used in a semantic search system. Which input type should they specify when calling the Embedding API?

Accepted Answer

search_query. The Embedding API in OCI Generative AI requires specifying the input type as 'search_query' when generating embeddings for search queries in a semantic search system. This input type optimizes the embedding model to produce vectors that are specifically tuned for query-side representation, ensuring better alignment with document embeddings during similarity search.

Answer

clustering

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search_document

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classification

Question 15

A developer wants to call the OCI Generative AI service from a Python application running on an OCI Compute instance. Which method is the most secure for authenticating the API calls?

Accepted Answer

Use instance principals with a dynamic group and policy. Instance principals allow the Compute instance to authenticate to OCI services without storing any credentials on the instance. By assigning a dynamic group and policy, the instance obtains a temporary security token from the OCI metadata service, which is the most secure method for programmatic access from within OCI.

Answer

Use a resource principal

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Use the OCI CLI with a config file containing credentials

Answer

Use an API signing key stored on the instance