1Z0-1127-25 Practice Exam 51 — 20 Questions

Question 1

A developer is building a RAG application using OCI Generative AI. They notice that the generated responses often contain outdated information even though the knowledge base is updated daily. What is the most likely cause?

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

Accepted Answer

The vector database index is not rebuilt after data updates.. In a RAG pipeline, the vector database index is a static snapshot of the embedded knowledge base. When the knowledge base is updated daily, the index must be rebuilt or incrementally updated to reflect the new data. Without rebuilding, the retrieval step will still search the old index, returning outdated chunks and causing the LLM to generate stale responses.

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The embedding model is not fine-tuned on the latest data.

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The retrieval top-k is set too high.

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The chunk size is too small, causing loss of context.

Question 2

A healthcare startup is building a chatbot that retrieves patient treatment guidelines using OCI Generative AI Service and OCI OpenSearch. They require that all retrieved documents are from approved sources only and that the system can explain which source was used for each response. Which combination of features should they implement?

Accepted Answer

Add a metadata filter for source_type='approved' in the retrieval step and include document IDs in the context for the model.. It directly addresses both requirements: a metadata filter on `source_type='approved'` ensures only approved documents are retrieved from OpenSearch, and including document IDs in the context allows the model to cite the specific source for each response. This approach enforces access control at the retrieval layer while providing traceability, which is essential for compliance in healthcare applications.

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Rely on the vector search's cosine similarity to rank approved sources higher.

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Use prompt engineering to ask the model to ignore non-approved sources.

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Reduce the top-K value to limit the number of retrieved documents.

Question 3

A multinational corporation uses OCI Generative AI to power a customer support chatbot. The chatbot uses a fine-tuned model deployed on a dedicated AI cluster in the us-ashburn-1 region. The application is used globally, and users in Europe are experiencing high latency (over 2 seconds) compared to users in North America (under 500 ms). The company has a requirement to keep all data within the US due to compliance, so they cannot deploy in Europe. The latency is not due to network bandwidth but due to the inference time. The monitoring shows that the cluster is at 80% utilization during peak hours. The team wants to reduce the latency for European users without violating data residency. What is the best course of action?

Accepted Answer

Optimize the model using techniques like quantization or pruning to reduce inference time.. The latency issue is explicitly due to inference time, not network bandwidth or cluster utilization. Model optimization techniques like quantization (reducing precision of weights from FP32 to INT8) and pruning (removing redundant neurons) directly reduce the computational cost per inference, thereby lowering the response time without moving data or changing the deployment region. This approach satisfies the data residency constraint while addressing the root cause of high latency for European users.

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Implement an edge caching layer in Europe to serve common queries.

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Increase the number of nodes in the cluster to distribute the load.

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Deploy an additional endpoint in a European region and use a global load balancer.

Question 4

A data scientist is fine-tuning a model on OCI Generative AI with a custom dataset. They receive a "QuotaExceeded" error during training. What is the most likely cause?

Accepted Answer

Exceeded the training compute unit quota. The 'QuotaExceeded' error during fine-tuning on OCI Generative AI specifically indicates that the training job has consumed more compute units than allocated in the service limit. Fine-tuning requires dedicated training compute units (TCUs) which are a separate quota from inference or API calls. When this quota is exhausted, the service rejects new training jobs with this error.

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Exceeded the API call rate limit

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Exceeded the model storage limit

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Exceeded the data transfer out limit

Question 5

Refer to the exhibit. A developer runs this command and sees that the 'cohere.embed-english-v3.0' model is INACTIVE. What is the most likely cause?

Accepted Answer

The model has been deprecated and is no longer available.. The 'cohere.embed-english-v3.0' model is listed as INACTIVE because Oracle Cloud Infrastructure (OCI) has deprecated it, meaning it is no longer available for inference. When a model is deprecated, its status changes to INACTIVE, and any attempt to invoke it will fail, even if the region, policies, and compartment permissions are correctly configured.

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The model is not supported in the current region.

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The API call lacks the required OCI policy for the model.

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The compartment does not have access to the model.

Question 6

Which TWO of the following are benefits of using OCI Generative AI service compared to self-hosting an LLM?

Accepted Answer

Built-in content safety filters. OCI Generative AI service includes built-in content safety filters that automatically detect and block harmful or inappropriate content (e.g., hate speech, violence, sexual content) without requiring manual configuration. This is a key advantage over self-hosting, where you must implement and maintain your own content moderation pipeline.

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Lower latency always

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No data egress costs

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Full control over model weights

Question 7

Which TWO of the following are required to fine-tune a model using OCI Generative AI Service?

Accepted Answer

A training dataset in the required format. A is correct because fine-tuning a model in OCI Generative AI Service requires a training dataset in the required format (JSONL with prompt-completion pairs) to provide the task-specific examples that adjust the model's weights. B is correct because you must specify the base model identifier (e.g., 'cohere.command-light-14-07-2024') to indicate which pre-trained model to fine-tune, as the service uses this to load the correct architecture and initial parameters.

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A compartment with sufficient quota

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An OCI API key

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A dedicated AI cluster

Question 8

A data scientist is using the OCI Generative AI SDK to create embeddings for a large corpus of legal documents. They want to perform semantic search. Which endpoint should they use?

Accepted Answer

/v1/embed. The /v1/embed endpoint is specifically designed to generate vector embeddings from input text, which are numerical representations that capture semantic meaning. For semantic search over a large corpus of legal documents, embeddings must be created to enable similarity comparisons, making this the correct choice.

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/v1/classify

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/v1/generate

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/v1/chat

Question 9

A developer is using the OCI Generative AI API to generate text. The responses are often too short and incomplete. Which parameter adjustment is most likely to produce longer, more complete responses?

Accepted Answer

Increase the max_tokens parameter.. The max_tokens parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. By increasing max_tokens, the model is allowed to produce longer sequences, which directly addresses the issue of responses being too short and incomplete. In the OCI Generative AI API, this is the primary parameter for capping output length.

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Decrease the max_tokens parameter.

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Increase the top_p parameter.

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Decrease the frequency_penalty parameter.

Question 10

Which THREE are valid considerations when designing a RAG pipeline that uses OCI Generative AI and OCI OpenSearch? (Choose three.)

Accepted Answer

The quality of the text extraction from OCI Document Understanding directly impacts retrieval accuracy.. OCI Document Understanding performs text extraction from documents (e.g., PDFs, images). If the extraction is poor (e.g., missing text, OCR errors), the resulting chunks will be inaccurate, directly degrading the quality of vector embeddings and thus retrieval accuracy in the RAG pipeline.

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OCI OpenSearch only supports Euclidean distance for vector similarity.

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Each document must be converted to a single vector for efficient retrieval.

Question 11

A financial company deploys a generative AI model for document analysis. They need to ensure that the model does not expose sensitive information in its responses. Which OCI service should they use to implement content filtering?

Accepted Answer

OCI AI Content Moderation. OCI AI Content Moderation is the correct service because it provides pre-trained models and APIs specifically designed to detect and filter sensitive content such as personally identifiable information (PII), profanity, and other unsafe text in generative AI outputs. This allows the financial company to enforce content safety policies on document analysis responses, preventing exposure of sensitive information.

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OCI Data Safe

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OCI Vault

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OCI WAF

Question 12

Which THREE of the following are known limitations of large language models that practitioners must consider?

Accepted Answer

Hallucination of facts not present in the input.. Large language models (LLMs) are prone to hallucination, where they generate plausible-sounding but factually incorrect information that was not present in the input. This occurs because LLMs are next-token predictors without a built-in fact-checking mechanism, and they can invent details, citations, or events to maintain coherence. Practitioners must implement retrieval-augmented generation (RAG) or external verification to mitigate this risk.

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Limited to processing only one language at a time.

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Inability to process inputs longer than a few hundred tokens.

Question 13

Refer to the exhibit. What is the primary reason the response is incomplete?

Accepted Answer

The max-tokens limit is too low.. The response is incomplete because the max-tokens limit is too low, causing the model to truncate its output before completing the full answer. When the token budget is exhausted, the generation stops mid-sentence or mid-thought, leaving the response unfinished regardless of prompt length or other parameters.

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The temperature is not set.

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The model-id is incorrect.

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The prompt is too short.

Question 14

An architect needs to ensure that an LLM deployed in OCI does not reveal sensitive information in its outputs. Which technique should be used?

Accepted Answer

Output filtering via custom inference wrapper. Output filtering via a custom inference wrapper allows the architect to inspect and sanitize the model's generated text before it reaches the user, preventing the leakage of sensitive information such as PII, credentials, or internal data. This technique operates at the application layer, intercepting the LLM's response and applying rules or regex patterns to redact or block prohibited content, which is essential for compliance and data security in production deployments.

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Limiting max tokens

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OCI Data Safe masking

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Input sanitization

Question 15

A developer receives the above error when querying a RAG application. What is the most likely cause and recommended action?

Accepted Answer

The API rate limit has been exceeded; wait for the retry period and implement exponential backoff.. The 429 (Too Many Requests) error indicates the API rate limit has been exceeded. In OCI Generative AI services, rate limits are enforced per tenancy and per region; the recommended action is to wait for the retry-after period and implement exponential backoff to avoid overwhelming the service.

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The model is deprecated; update to the latest model.

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The endpoint URL is incorrect; verify the OCI region endpoint.

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The request payload is malformed; check the input format.

Question 16

Which parameter controls the randomness of the model's output by scaling the probability distribution before sampling?

Accepted Answer

temperature. Temperature scales the logits before softmax, affecting creativity. Higher values increase randomness; lower values make output more deterministic.

Answer

top-k

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frequency_penalty

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top-p

Question 17

Which TWO of the following are best practices for building a RAG pipeline in OCI?

Accepted Answer

Use overlapping chunks. Overlapping chunks ensure that context is not lost at chunk boundaries, which is critical for retrieval accuracy in RAG pipelines. By including overlapping text segments, the embedding model can capture semantic continuity, reducing the risk of missing relevant information when a query spans chunk edges.

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Always use exact vector search for accuracy

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Avoid storing metadata alongside vectors

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Use a single large chunk for each document

Question 18

A research institution uses OCI Data Flow to process large-scale document corpora for a RAG system. They want to minimize latency for end-user queries. Which architecture decision would most effectively reduce query latency?

Accepted Answer

Precompute embeddings offline using OCI Data Flow and store them in an OCI OpenSearch index.. Precomputing embeddings offline with OCI Data Flow and storing them in an OCI OpenSearch index eliminates the need to generate embeddings at query time, which is the primary source of latency. This approach shifts the computationally expensive embedding generation to a batch process, allowing queries to perform only a fast vector similarity search against the precomputed index, drastically reducing end-user response time.

Answer

Embed documents on-the-fly during query time to ensure freshness.

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Use a larger, more accurate embedding model.

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Increase the number of Spark workers for parallel processing of queries.

Question 19

An OCI GenAI model generates English to French translation. Which metric is most appropriate to evaluate its quality?

Accepted Answer

BLEU. BLEU (Bilingual Evaluation Understudy) is the standard metric for machine translation tasks because it measures the n-gram overlap between the generated translation and one or more reference translations, directly assessing fluency and adequacy. For English-to-French translation, BLEU correlates well with human judgment of translation quality, making it the most appropriate choice.

Answer

Perplexity

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ROUGE

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F1 score

Question 20

A company wants to use OCI Generative AI to analyze legal documents and extract key clauses. Which model type is best suited for this task?

Accepted Answer

Cohere Summarize. Cohere Summarize is specifically designed to condense long documents into concise summaries, making it ideal for extracting key clauses from legal documents. Unlike other Cohere models, Summarize focuses on distilling the most important information from text, which aligns with the task of identifying and extracting critical clauses.

Answer

Cohere Command (generate)

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Cohere Chat

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Cohere Embed