1Z0-1127-25 Practice Test 2 — 30 Questions

Question 1

Refer to the exhibit. A developer encounters this error. Which action should they take to resolve the issue?

Accepted Answer

Wait and retry after some time.. The error indicates a rate limit or throttling issue, typically returned by the OCI Generative AI service when the API request quota is exceeded. Waiting and retrying after the cooldown period allows the rate limit to reset, which is the correct resolution for transient throttling errors.

Answer

Change the model to cohere.command-light.

Answer

Increase the max-tokens value.

Answer

Decrease the temperature to 0.0.

Question 2

A company is deploying a chatbot powered by OCI Generative AI. They want to inject the conversation history into the model prompt to maintain context. However, they notice that after a long conversation, the model starts to ignore earlier messages. What is the most likely cause?

Accepted Answer

The model has a limited context window size.. The model's context window size limits the total number of tokens (input + output) it can process at once. When the conversation history grows beyond this limit, older messages are truncated or dropped, causing the model to lose context from earlier parts of the conversation. This is a fundamental constraint of transformer-based models like those used in OCI Generative AI.

Answer

The model's max_tokens limit is too low, truncating the prompt.

Answer

The top_p parameter is set to 1, causing deterministic output.

Answer

The temperature setting is too high, causing randomness.

Question 3

Which THREE components are essential for a production-grade generative AI deployment on OCI? (Select THREE)

Accepted Answer

OCI Logging for audit. A is correct because OCI Logging provides centralized audit logging for all API calls and resource changes in the generative AI deployment. This is essential for compliance, security monitoring, and troubleshooting in a production environment, as it captures detailed logs of model invocations, data access, and configuration changes.

Answer

OCI Vault for secrets

Answer

OCI Data Flow for data processing

Question 4

During deployment of a generative AI model, the inference endpoint returns high latency and timeouts. The model is deployed on a dedicated AI cluster with multiple nodes. What is the most likely cause?

Accepted Answer

The cluster nodes are configured with insufficient parallelism or the model is not properly parallelized across nodes. High latency and timeouts in a distributed AI inference deployment typically indicate that the model workload is not efficiently distributed across the cluster nodes. Option C is correct because insufficient parallelism—either due to misconfigured node resources (e.g., insufficient vCPUs, GPU cores, or memory bandwidth) or improper model sharding/parallelization—causes some nodes to become bottlenecks while others remain underutilized, leading to queuing delays and eventual timeouts.

Answer

The inference request batch size is too small

Answer

The model is too large for the cluster memory

Answer

The client-side network is slow

Question 5

Which TWO factors most directly impact the consistency of text generated by an LLM when the same prompt is used multiple times?

Accepted Answer

Top_p. Top_p (nucleus sampling) directly impacts consistency by controlling the cumulative probability threshold for token selection. A lower Top_p (e.g., 0.1) restricts the model to only the most probable tokens, reducing randomness and making outputs more deterministic across repeated prompts. This parameter, along with Temperature, is a primary lever for managing output variability in LLMs.

Answer

Batch size

Answer

Max_tokens

Answer

Seed

Question 6

A company wants to use OCI Generative AI to summarize customer reviews. Which model parameter should be adjusted to control the creativity of the summary?

Accepted Answer

Temperature. Temperature controls the randomness of token selection in the model's output distribution. A higher temperature (e.g., 0.9) makes the summary more creative and diverse, while a lower temperature (e.g., 0.1) makes it more deterministic and focused. For summarizing customer reviews, adjusting temperature directly influences how novel or conservative the generated text will be.

Answer

Frequency penalty

Answer

Top-k

Answer

Presence penalty

Question 7

Your team is deploying a generative AI model for a clinical decision support system. The model must meet HIPAA compliance requirements. You have trained a model using OCI Data Science and now need to deploy it so that patient data is protected. The application requires real-time inference. Which set of actions should you take to ensure compliance while maintaining low latency?

Accepted Answer

Deploy in a private VCN subnet, use a service gateway, store keys in OCI Vault, and enable OCI Logging and OCI Audit. Deploying the model in a private VCN subnet ensures the inference endpoint is not exposed to the internet, meeting HIPAA's requirement for network isolation. Using a service gateway allows private connectivity to OCI services without traversing the internet, while storing encryption keys in OCI Vault enables customer-managed key control for data at rest. Enabling OCI Logging and OCI Audit provides the necessary audit trail for compliance, and the private subnet with service gateway keeps latency low by avoiding internet hops.

Answer

Use OCI Functions with API Gateway and allow anonymous access

Answer

Deploy in a public subnet with HTTPS and enable OCI Audit

Answer

Use OCI Data Flow for batch inference and store results in Object Storage with SSE

Question 8

A developer runs an OCI GenAI chat request with system prompt "You are a sarcastic assistant." The output is offensive. How can the developer enforce safety policies?

Accepted Answer

Use the OCI GenAI content moderation filter.. The OCI GenAI content moderation filter is specifically designed to enforce safety policies by detecting and blocking offensive, harmful, or policy-violating content in both input prompts and model outputs. By enabling this filter, the developer can prevent the model from generating offensive responses even when a system prompt like 'You are a sarcastic assistant' encourages undesirable behavior.

Answer

Change model to LLAMA.

Answer

Increase maxTokens.

Answer

Set temperature to 0.

Question 9

A developer is testing a RAG application using OCI Generative AI. They receive an error: 'The model cohere.command-r-plus-v1:0 is not supported in this region.' What is the most likely cause?

Accepted Answer

The model is not available in the selected OCI region.. The error message explicitly states that the model 'cohere.command-r-plus-v1:0' is not supported in the region. OCI Generative AI models are region-specific; each model is deployed only in certain OCI regions (e.g., us-ashburn-1, eu-frankfurt-1). If the selected region does not host that model, the API returns this error regardless of endpoint formatting, tenancy configuration, or model name spelling.

Answer

The endpoint URL is incorrectly formatted.

Answer

The tenancy is in a different availability domain.

Answer

The model name has a typo.

Question 10

A financial institution uses OCI GenAI to power a customer support chatbot. The compliance team requires that responses are strictly consistent with regulatory guidelines and approved responses. The company has a curated set of question-answer pairs that cover common scenarios. They want to ensure that the chatbot never deviates from these approved answers. The data science team is considering various approaches to enforce this consistency. Which approach is most effective?

Accepted Answer

Fine-tuning the model on the curated dataset of question-answer pairs.. Fine-tuning the model on the curated dataset of approved responses teaches the model to output similar responses for related questions, ensuring consistency. Option A is wrong because few-shot prompting may fail for unseen variations and does not guarantee strict adherence. Option C is wrong because using a large context window does not enforce specific content. Option D is wrong because setting a low temperature reduces randomness but does not guarantee the model will choose approved responses.

Answer

Few-shot prompting with three example responses in every query.

Answer

Using a large context window to include all regulatory guidelines in the prompt.

Answer

Setting a low temperature (0.1) to make outputs deterministic.

Question 11

A manufacturing company uses OCI OpenSearch to build a RAG application that retrieves procedural documents. After deployment, queries often return outdated procedures even though the vector index was refreshed. What is the most likely cause?

Accepted Answer

The full-text search index is not synchronized with the vector index after updates.. In a RAG application using OCI OpenSearch, the vector index and full-text search index are separate. When procedural documents are updated, the full-text search index may reflect changes immediately, but the vector index requires explicit re-indexing or synchronization to update embeddings. If the vector index is not refreshed after updates, queries can still retrieve outdated vector representations, leading to outdated results despite the index being refreshed.

Answer

The embedding model was fine-tuned on outdated data.

Answer

The BM25 scoring algorithm prioritizes older documents due to term frequency.

Answer

The chunk overlap percentage is too high, causing duplicate context.

Question 12

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, works directly on raw text without requiring pre-tokenisation (e.g., whitespace splitting), and handles languages like Chinese and Korean where word boundaries are not obvious. BPE and WordPiece typically require pre-tokenisation, making them less suitable for CJK languages.

Answer

WordPiece

Answer

Byte-Pair Encoding (BPE)

Answer

Character-level tokenisation

Question 13

A developer notices that the ConversationalRetrievalChain in their LangChain application is not retaining context from previous turns in the conversation. Which component is most likely missing or misconfigured?

Accepted Answer

A memory component like ConversationBufferMemory. ConversationalRetrievalChain requires a Memory component to store and retrieve chat history. Without Memory, the chain treats each query independently. The retriever, document splitter, and embeddings are responsible for retrieval and storage, not conversation history.

Answer

A document splitter to chunk the history

Answer

A retriever with appropriate search parameters

Answer

An embedding model to vectorize the history

Question 14

A team fine-tuned a Cohere Command R model using the T-Few technique on a dataset of JSONL prompt/completion pairs. After deployment, they observe that the model's responses are too repetitive. Which parameter adjustment in the OCI Generative AI Playground would BEST address this issue?

Accepted Answer

Increase frequency penalty. Increasing the frequency penalty discourages the model from repeating tokens that have already appeared, reducing repetitive outputs. Temperature and max tokens address randomness and length, not repetition specifically. Stop sequences are for terminating generation early.

Answer

Increase max tokens

Answer

Decrease temperature

Answer

Add a stop sequence

Question 15

Which THREE are essential steps in the prompt engineering process for an LLM?

Accepted Answer

Test the prompt with a variety of input examples. Testing the prompt with a variety of input examples is essential to evaluate the LLM's generalization, robustness, and sensitivity to different phrasing or contexts. This step helps identify edge cases, biases, or inconsistencies in the model's responses before deployment.

Answer

Fine-tune the model on a domain corpus

Answer

Quantize the model to INT8

Question 16

An organization needs to deploy a model that can both understand and generate text, such as for a translation task where the input is in English and output is in French. Which model architecture is most suitable?

Accepted Answer

Encoder-decoder (e.g., T5). Encoder-decoder architectures like T5 are designed for sequence-to-sequence tasks. The encoder processes the input sequence, and the decoder generates the output sequence, making it ideal for translation.

Answer

Decoder-only (e.g., GPT)

Answer

Encoder-only (e.g., BERT)

Answer

Mixture of Experts (MoE)

Question 17

Which THREE factors should be considered when choosing between fine-tuning a model and using a pre-trained model with prompt engineering? (Select three.)

Accepted Answer

Size of available dataset. The size of the available dataset is a critical factor: fine-tuning requires a sufficiently large, labeled dataset (typically thousands of examples) to adjust model weights effectively, while prompt engineering can work with zero or few examples. If the dataset is too small, fine-tuning risks overfitting and poor generalization, making prompt engineering the safer choice.

Answer

Required response time

Answer

Internet connectivity

Question 18

A data scientist in group DataScientists uses the OCI Generative AI SDK to start a fine-tuning job in compartment AIResources. They receive the error shown. What is the most likely cause?

Accepted Answer

The fine-tuning job requires additional IAM policies for accessing the training data in Object Storage.. The error message indicates a permissions issue related to accessing training data in Object Storage. When using the OCI Generative AI SDK to start a fine-tuning job, the data scientist's IAM policies must explicitly grant read access to the bucket and objects containing the training data. Without these policies, the API call fails even if the user is in the correct group and the compartment exists.

Answer

The compartment AIResources does not exist.

Answer

The fine-tuning API is not yet available in that region.

Answer

The data scientist is not in the DataScientists group.

Question 19

In few-shot prompting, what is the primary purpose of including examples in the prompt?

Accepted Answer

To provide a template for the desired output format and reasoning pattern. Examples guide the model on the desired input-output pattern, improving task performance without fine-tuning.

Answer

To reduce the need for a system prompt

Answer

To increase the model's vocabulary

Answer

To decrease the computational cost of inference

Question 20

Refer to the exhibit. A user receives this error when calling the OCI Gen AI inference endpoint. What is the most likely cause?

Accepted Answer

The model is not deployed in the region. The error indicates that the model is not available in the specified region. OCI Gen AI models are deployed regionally, and each region supports only a specific subset of models. If the user calls an endpoint in a region where the requested model has not been deployed, the service returns an error because the model's inference endpoint does not exist in that region's routing table.

Answer

The region name is misspelled

Answer

The API key is expired

Answer

The model name is incorrect