1Z0-1127-25 Practice Test 41 — 15 Questions

Question 1

Which tokenization algorithm is commonly used in models like GPT and BERT and builds tokens by merging the most frequent pairs of characters or subwords iteratively?

Accepted Answer

Byte-Pair Encoding (BPE). Byte-Pair Encoding (BPE) is a subword tokenization method that starts with individual characters and merges the most frequent pairs iteratively until a vocabulary size is reached.

Answer

WordPiece

Answer

SentencePiece

Answer

Unigram tokenization

Question 2

A team is evaluating two embedding models for a similarity search task. Model A has a higher BERTScore on a reference dataset. Model B has a lower perplexity on the same dataset. Which model is likely better for retrieval?

Accepted Answer

Model A, because BERTScore directly measures semantic similarity of embeddings. For retrieval tasks, embedding quality is best measured by semantic similarity metrics like BERTScore, which correlate with how well embeddings capture meaning. Perplexity measures language model fluency, not embedding quality.

Answer

Both are equally good for retrieval

Answer

Model B, because lower perplexity indicates better language modeling, which improves retrieval

Answer

Neither metric is relevant for retrieval tasks

Question 3

A data scientist is building a text summarization system using an LLM. They want to evaluate the model's output against human-written summaries. Which TWO metrics are most appropriate for this evaluation? (Choose two.)

Accepted Answer

ROUGE. ROUGE is recall-oriented and measures overlap of n-grams, making it a standard metric for summarization. BERTScore measures semantic similarity using embeddings, which can capture meaning even when wording differs. BLEU is more for translation, perplexity measures fluency, and human evaluation is qualitative but not a metric.

Answer

Human evaluation rubrics

Answer

BLEU

Answer

Perplexity

Question 4

A developer is comparing different foundation models for a text completion API on OCI. Which TWO of the following are model families available through OCI Generative AI service? (Choose two.)

Accepted Answer

Meta Llama. OCI Generative AI offers models including Cohere Command/Embed and Meta Llama. Mistral and GPT are not mentioned in the context of OCI's available models, and BERT is an encoder-only model not typically offered as a generation model.

Answer

OpenAI GPT

Answer

BERT

Answer

Mistral

Question 5

A developer is debugging a RAG pipeline where the LLM frequently ignores retrieved documents and produces hallucinations. Which THREE factors could contribute to this problem?

Accepted Answer

The prompt does not explicitly instruct the model to base its answer on the provided context. Low chunk overlap can cause loss of context, improper prompt instructions may lead the model to ignore retrieved content, and a small top-K retrieval may miss relevant documents. A large embedding dimension improves retrieval precision, and a high temperature increases randomness but does not cause ignoring documents.

Answer

The generation temperature is set to 0

Answer

The embedding model's output dimension is too large

Question 6

Which TWO of the following sampling strategies introduce randomness into text generation?

Accepted Answer

Top-k sampling. Top-k sampling and top-p (nucleus) sampling introduce randomness by sampling from a restricted token set. Temperature modifies the probability distribution and controls randomness but is a generation parameter, not a standalone sampling strategy. Greedy decoding and beam search are deterministic or near-deterministic.

Answer

Beam search

Answer

Greedy decoding

Answer

Temperature sampling

Question 7

An organization plans to deploy a custom fine-tuned model for a real-time chat application requiring consistent low-latency responses. They expect high throughput during business hours. Which OCI Generative AI infrastructure choice best meets these requirements?

Accepted Answer

Provision a Dedicated AI Cluster with the required model units. A Dedicated AI Cluster provides reserved compute with low latency and high throughput, ideal for production workloads with custom models. Shared infrastructure may have variable performance.

Answer

Use the shared infrastructure on-demand endpoint

Answer

Use the OCI Generative AI Playground for production

Answer

Deploy the model on OCI Data Science using a custom container

Question 8

An organization wants to deploy a chatbot that uses a custom fine-tuned model. They have provisioned a Dedicated AI Cluster with 4 model units. During peak hours, they observe high latency and want to reduce it. What is the most cost-effective change?

Accepted Answer

Increase the number of model units in the dedicated cluster. Adding more model units increases throughput and reduces latency by distributing the load. This is the direct way to improve performance on a dedicated cluster.

Answer

Reduce the max_tokens parameter in the application

Answer

Switch to shared infrastructure to get more capacity

Answer

Enable response streaming

Question 9

A data scientist is evaluating the cost of deploying a fine-tuned model for a high-volume production application. They need low latency but are cost-sensitive. Which TWO considerations should they evaluate when choosing between on-demand (shared) and dedicated cluster pricing?

Accepted Answer

The cost per model unit per hour for a dedicated cluster. The cost of model units per hour and the expected token volume help determine whether dedicated or on-demand is more economical. Cluster provisioning time and streaming availability are not direct cost factors.

Answer

Whether the model supports streaming responses

Answer

The number of days required to provision the dedicated cluster

Answer

The availability of the model in the OCI Generative AI Playground

Question 10

A company wants to process a large batch of documents to generate summaries using OCI Generative AI. They need the most cost-effective option without compromising on summary quality. Which approach should they use?

Accepted Answer

Use the on-demand (shared) inference endpoint with the Summarisation API. On-demand pricing is pay-per-token, suitable for batch workloads without dedicated capacity; it's cost-effective for sporadic or large batch jobs.

Answer

Use OCI Generative AI Agents with a knowledge base

Answer

Provision a dedicated AI cluster for the batch job

Answer

Fine-tune a model on summarization and deploy it on a dedicated cluster

Question 11

A developer receives the above error when trying to send a request to a model endpoint. What is the most likely reason?

Accepted Answer

The endpoint was deleted by an administrator. The error message indicates that the model endpoint is not found. In OCI Generative AI, when an administrator deletes an endpoint, subsequent requests to that endpoint's URL return a 404 Not Found error. This is the most likely reason because the endpoint resource no longer exists in the tenancy, and the request cannot be routed to any model.

Answer

The network connection to OCI is down

Answer

The API key is invalid

Answer

The model is still being deployed

Question 12

A startup wants to minimize costs when using OCI Generative AI service for a chatbot application that experiences sporadic usage. Which deployment strategy is most cost-effective?

Accepted Answer

Use the serverless on-demand API without dedicated endpoints. Serverless on-demand pricing charges only for usage, ideal for sporadic workloads. Option A is wrong because dedicated endpoints incur hourly costs regardless of usage. Option C is wrong because pre-built models may also have per-request costs but dedicated endpoints are not cost-effective. Option D is wrong because running models on OCI Compute adds management overhead and costs.

Answer

Use a pre-built model with a dedicated endpoint

Answer

Provision a dedicated endpoint for low latency

Answer

Deploy the model on OCI Compute with autoscaling

Question 13

A developer is tuning parameters for a text-generation model and wants to reduce the likelihood of the same phrase appearing repeatedly. Which parameter should be adjusted?

Accepted Answer

Frequency penalty. Frequency penalty reduces the model's tendency to repeat tokens or phrases by penalizing tokens that have already appeared in the generated text.

Answer

Top-k

Answer

Stop sequences

Answer

Presence penalty

Question 14

A company has fine-tuned a custom Llama 3 model using OCI Data Science for a chatbot. They now need a production-grade inference endpoint with auto-scaling. Which OCI service should they use?

Accepted Answer

OCI Generative AI Service. OCI Generative AI Service provides a fully managed, production-grade inference endpoint with built-in auto-scaling for custom models like fine-tuned Llama 3. It abstracts infrastructure management, offers serverless deployment, and integrates with OCI Data Science for model import, making it the ideal choice for a chatbot requiring scalable inference.

Answer

OCI Functions

Answer

OCI Data Science Model Deployment

Answer

OCI Kubernetes Engine (OKE)

Question 15

A team is deploying an LLM-based application that must adhere to strict data residency requirements. All processing must occur within a specific OCI region. Which OCI service should they use to host and serve the LLM?

Accepted Answer

OCI Data Science with a model deployment endpoint. OCI Data Science with a model deployment endpoint is the correct choice because it provides a managed infrastructure for hosting and serving LLMs within a specific OCI region, ensuring all processing and data remain within that region to meet strict data residency requirements. The model deployment endpoint runs on dedicated compute resources in the chosen region, allowing low-latency inference while adhering to regional data boundaries.

Answer

OCI Streaming

Answer

OCI Functions

Answer

OCI Object Storage