1Z0-1127-25 Practice Test 31 — 15 Questions

Question 1

A developer is using the OCI Generative AI Playground to test a Cohere Command R model. They want to reduce repetitiveness in the generated responses. Which parameter should they increase?

Accepted Answer

Frequency penalty. The frequency penalty parameter in Cohere Command R penalizes tokens that have already appeared in the generated text, reducing the likelihood of repetition. Increasing it makes the model less likely to reuse the same tokens, directly addressing repetitiveness. Temperature and Top P affect randomness and diversity but do not specifically target repeated tokens.

Answer

Max tokens

Answer

Top P

Answer

Temperature

Question 2

When tuning the temperature parameter for a text generation task, which effect does setting temperature to 0.1 have compared to 0.9?

Accepted Answer

It makes outputs more focused and deterministic. Temperature scales the logits before the softmax function, controlling the randomness of the probability distribution. A low temperature like 0.1 sharpens the distribution, making the highest-probability token much more likely to be chosen, resulting in focused and deterministic outputs. In contrast, a high temperature like 0.9 flattens the distribution, increasing randomness and diversity. Thus, setting temperature to 0.1 makes outputs more focused and deterministic compared to 0.9.

Answer

It increases the maximum number of tokens generated

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It reduces the vocabulary considered at each step

Answer

It increases randomness, producing more diverse outputs

Question 3

What is the main advantage of using Byte-Pair Encoding (BPE) over word-level tokenization?

Accepted Answer

It can represent any word as a sequence of subword tokens, including rare or unseen words. Byte-Pair Encoding (BPE) is a subword tokenization algorithm that iteratively merges the most frequent pairs of bytes or characters into new tokens. Its main advantage over word-level tokenization is that it can represent any word, including rare or unseen words, as a sequence of subword tokens, thereby eliminating the out-of-vocabulary (OOV) problem. This allows models like GPT and BERT to handle arbitrary input without requiring a fixed word vocabulary.

Answer

It produces fixed-length token sequences

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It reduces the number of tokens by merging all letters into single tokens

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It eliminates the need for a vocabulary altogether

Question 4

Which of the following is a common prompt injection vulnerability?

Accepted Answer

User input that contains 'Ignore previous instructions' followed by malicious commands. Prompt injection occurs when untrusted user input is interpreted by the model as instructions rather than data, allowing an attacker to override the system prompt. The classic payload 'Ignore previous instructions' followed by malicious commands directly hijacks the model's instruction-following behavior, which is the defining characteristic of a prompt injection vulnerability. This is a well-documented OWASP LLM Top 10 risk (LLM01).

Answer

Including too many few-shot examples

Answer

Setting temperature too high

Answer

Using a system prompt that is too long

Question 5

Which prompting technique involves generating multiple independent reasoning paths and then selecting the most common answer?

Accepted Answer

Self-consistency prompting. Self-consistency prompting generates multiple independent reasoning paths (typically via chain-of-thought sampling at higher temperature) and then selects the most common final answer through majority voting. This ensemble-like approach improves accuracy on reasoning tasks by reducing the impact of any single flawed reasoning chain. It was introduced by Wang et al. (2022) as an enhancement to chain-of-thought prompting.

Answer

Chain-of-thought prompting

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Few-shot prompting

Answer

Zero-shot prompting

Question 6

A prompt engineer wants the LLM to adopt the persona of a 'friendly customer support agent' for all interactions. Which approach is most effective?

Accepted Answer

Set the persona in the system prompt (or preamble) before the conversation begins. The system prompt (or preamble) is the correct place to set a persistent persona because it is processed before the conversation and conditions all subsequent turns. Placing the persona there ensures the model maintains the 'friendly customer support agent' behavior consistently without the user having to repeat it. This is the standard pattern for durable behavioral instructions in LLM applications.

Answer

Include the persona instruction in every user message

Answer

Use a high temperature to encourage friendly language

Answer

Fine-tune the model on customer support dialogues

Question 7

Which evaluation metric is designed to measure the overlap of n-grams between a generated summary and a reference summary, focusing on recall of content words?

Accepted Answer

ROUGE. ROUGE-N measures n-gram recall (how many n-grams from the reference appear in the generated text). BLEU measures n-gram precision. BERTScore leverages contextual embeddings. Perplexity measures likelihood under the model.

Answer

Perplexity

Answer

BERTScore

Answer

BLEU

Question 8

A company is deploying a LangChain agent for internal data analysis. The agent uses a ReAct pattern with tools including a Python REPL and a SQL query tool. What is the PRIMARY security concern when exposing this agent to non-technical users?

Accepted Answer

Arbitrary code execution via the Python REPL tool. The Python REPL tool allows the agent to execute arbitrary Python code on the host system. When exposed to non-technical users, an attacker can craft prompts that cause the agent to run malicious code, leading to full remote code execution, data exfiltration, or system compromise. This is the most severe risk because it bypasses all data access controls and directly impacts the underlying infrastructure.

Answer

Model hallucination when generating SQL queries

Answer

Prompt injection leading to incorrect SQL queries

Answer

Data leakage through the SQL query tool

Question 9

Your team has deployed a fine-tuned GPT-2 model on OCI Model Deployment for a simple text generation API. The model performs text completion for short prompts (e.g., 50 tokens). The endpoint is working but response times are over 10 seconds for these short prompts. The model size is approximately 500MB and you used a VM.Standard.E3.Flex shape (2 OCPU, 16GB RAM). The deployment is in a single replica with no autoscaling. You have verified that the network latency is minimal (<5ms). The model was trained in OCI Data Science using a GPU shape, but during deployment you selected a CPU shape to reduce cost. The model is a transformer-based neural network. You've also confirmed that the deployment is healthy and there are no errors in the logs. The memory usage is within limits. What is the most likely cause of the high latency?

Accepted Answer

Missing GPU acceleration for inference. GPT-2 is a transformer-based neural network that relies heavily on matrix multiplications, which are far more efficiently executed on GPUs due to their parallel architecture. Even though the model is only 500MB, CPU inference for transformer models is notoriously slow because CPUs process sequential operations, while GPUs can parallelize the attention mechanism and feed-forward layers. The 10-second latency for a 50-token prompt is a classic symptom of missing GPU acceleration, as the CPU shape (2 OCPU) lacks the specialized tensor cores needed for fast transformer inference.

Answer

High network latency between the client and the model endpoint

Answer

Model is too large for the VM.Standard.E3.Flex shape

Answer

Insufficient CPU resources for the model size

Question 10

Refer to the exhibit. A user runs this command and gets an error: 'InvalidParameter: unit-shape 'GPU.1' is not supported in this region. Supported shapes: GPU.2, GPU.4'. What should the user do?

Accepted Answer

Use unit-shape GPU.2. The error message explicitly states that 'GPU.1' is not supported in the current region and lists 'GPU.2' and 'GPU.4' as supported shapes. Option A correctly instructs the user to use a supported shape, such as GPU.2, which resolves the invalid parameter error without changing other configurations.

Answer

Increase unit count

Answer

Change compartment

Answer

Use a different region

Question 11

Which TWO components are essential in a Retrieval-Augmented Generation (RAG) pipeline?

Accepted Answer

Chunking the documents into smaller pieces. Chunking splits documents, embedding converts chunks to vectors, retrieval fetches relevant chunks, and generation produces the answer.

Answer

Fine-tuning the LLM on the documents

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Knowledge distillation

Answer

Beam search decoding

Question 12

A team is building a legal document summarization system using OCI Generative AI. They need to ensure high accuracy and adherence to strict output formatting. Which THREE practices should they adopt? (Choose three.)

Accepted Answer

Define a system prompt specifying the role of a legal expert and constraints. Option B is correct because a system prompt that assigns the model the role of a legal expert and states the task constraints anchors the model's behavior, improving domain accuracy and reducing off-task or non-compliant output. Option C is correct because explicitly specifying the required summary structure (for example, section headings, bullet order, or JSON/field layout) in the prompt is the primary way to enforce strict output formatting in OCI Generative AI. Option E is correct because few-shot examples of correctly formatted legal summaries demonstrate the desired style, level of detail, and exact structure, which measurably improves adherence to formatting and accuracy requirements. Option A is not appropriate because stop sequences are useful controls for terminating generation at defined tokens; disabling them does not improve accuracy or formatting and can allow unwanted trailing content. Option D is not appropriate because a temperature of 0.9 increases randomness and creative variation, which is the opposite of what a high-accuracy, strictly formatted legal summarization task requires.

Answer

Disable stop sequences to allow complete summaries

Answer

Set the temperature to 0.9 to encourage creative variations

Question 13

Which component of the Transformer architecture allows the model to focus on different parts of the input sequence when generating each output token?

Accepted Answer

Self-attention mechanism. Self-attention computes attention scores between all pairs of positions, enabling the model to weigh the importance of different input tokens.

Answer

Positional encoding

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Feed-forward network

Answer

Layer normalization

Question 14

Refer to the exhibit. A data scientist runs this inference request and receives a response that is incomplete and seems to stop mid-sentence. Which parameter should be adjusted to allow the model to generate longer outputs?

Accepted Answer

maxTokens. The `maxTokens` parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. When a response stops mid-sentence, it indicates the token limit was reached before the model could complete its output. Increasing `maxTokens` allows the model to generate longer, complete responses.

Answer

temperature

Answer

topP

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frequencyPenalty

Answer

presencePenalty

Question 15

A troubleshooting scenario: A RAG system returns no results for certain queries. The index exists and has documents. Which TWO are likely causes?

Accepted Answer

The query embedding dimension does not match the index dimension. Option B is correct because a vector index is built with a fixed dimensionality (e.g., 1536 for text-embedding-ada-002); if the query embedding is produced by a model with a different dimension, the similarity search cannot compute distances against the stored vectors and returns no matches. Option E is correct because embeddings from different models occupy incompatible vector spaces, so even with matching dimensions, a query embedded with a different model than the one used at index time yields meaningless similarity scores and effectively no relevant results. Option A is not a cause of empty results: brute-force (exact) search still returns nearest neighbors, and small index size only affects performance, not whether results are returned. Option C is not a cause: an over-length query is typically truncated or rejected by the embedding model, but if an embedding is produced it will still be searched. Option D is not a cause of no results for existing documents: an unrefreshed index would simply omit newly added documents, while the scenario states the index exists and contains documents.

Answer

The search algorithm is set to exact (brute-force) and index is small

Answer

The query is too long for the embedding model

Answer

The index has not been refreshed after adding new documents