Courseiva
Knowledge + Practice
CertificationsVendorsCareer RoadmapsLabs & ToolsStudy GuidesGlossaryPractice Questions
C
Courseiva

Free IT certification practice questions with explained answers for CCNA, CompTIA, AWS, Azure, Google Cloud, and more.

Certification Practice Questions

CCNA practice questionsSecurity+ SY0-701 practice questionsAWS SAA-C03 practice questionsAZ-104 practice questionsAZ-900 practice questionsCLF-C02 practice questionsA+ Core 1 practice questionsGoogle Cloud ACE practice questionsCySA+ CS0-003 practice questionsNetwork+ N10-009 practice questions
View all certifications →

Product

CertificationsCertification PathsExam TopicsPractice TestsExam Dumps vs Practice TestsStudy HubComparisons

Company

AboutContactEditorial PolicyQuestion Writing PolicyTrust Center

Legal

Privacy PolicyTerms of Service

Courseiva is a free IT certification practice platform offering original exam-style practice questions, detailed explanations, topic-based practice, mock exams, readiness tracking, and study analytics for Cisco, CompTIA, Microsoft, AWS, and other technology certifications.

© 2026 Courseiva. Courseiva is operated by JTNetSolutions Ltd. All rights reserved.

Courseiva is an independent certification practice platform and is not affiliated with, endorsed by, or sponsored by Cisco, Microsoft, AWS, CompTIA, Google, ISC2, ISACA, or any other certification vendor. Vendor names and certification marks are used only to identify the exams learners are preparing for.

← Fundamentals of Large Language Models practice sets

1Z0-1127 Fundamentals of Large Language Models • Complete Question Bank

1Z0-1127 Fundamentals of Large Language Models — All Questions With Answers

Complete 1Z0-1127 Fundamentals of Large Language Models question bank — all 0 questions with answers and detailed explanations.

128
Questions
Free
No signup
Certifications/1Z0-1127/Practice Test/Fundamentals of Large Language Models/All Questions
Question 1mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A company is deploying a large language model for a customer service chatbot. The model needs to understand industry-specific jargon and maintain low latency. Which approach best balances these requirements?

Question 2hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

A data scientist observes that their fine-tuned LLM performs well on training data but generates repetitive and dull responses in production. What is the most likely cause and best solution?

Question 3easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

An organization wants to use an LLM to summarize legal documents. Which consideration is most important for ensuring accurate summaries?

Question 4mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A developer is building a code generation assistant. The model occasionally produces syntactically correct but semantically wrong code. Which technique directly addresses semantic correctness?

Question 5hardmultiple choice
Read the full NAT/PAT explanation →

A company fine-tunes an LLM on internal support tickets. After deployment, the model hallucinates company-specific product names. What is the most effective mitigation?

Question 6easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A team wants to evaluate an LLM's performance on a text classification task. Which metric is most appropriate for a balanced dataset?

Question 7mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

An LLM-based application must comply with data privacy regulations by not memorizing personally identifiable information (PII). Which technique best reduces memorization of PII?

Question 8hardmulti select
Read the full Fundamentals of Large Language Models explanation →

Which TWO factors most significantly influence the computational cost of fine-tuning a large language model?

Question 9mediummulti select
Read the full Fundamentals of Large Language Models explanation →

Which TWO techniques are commonly used to reduce the memory footprint of LLM inference?

Question 10easymulti select
Read the full Fundamentals of Large Language Models explanation →

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

Question 11easymultiple choice
Read the full NAT/PAT explanation →

A data scientist is using a large language model to summarize customer support tickets. The model occasionally generates summaries that include hallucinated details not present in the original ticket. Which technique would best reduce hallucinations while maintaining summary quality?

Question 12mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

An enterprise is deploying a chat application using a large language model. Users report that the model sometimes generates toxic or biased responses. Which best practice should be applied to mitigate this issue?

Question 13hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

A team is fine-tuning a large language model for a domain-specific Q&A application. After fine-tuning, they observe that the model performs well on the training distribution but struggles with out-of-distribution (OOD) questions. Which approach would best improve OOD robustness?

Question 14easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A developer is using a large language model to generate code snippets. The model often produces code that is syntactically correct but functionally incorrect. What is the most effective way to improve the functional correctness of the generated code?

Question 15mediummultiple choice
Read the full NAT/PAT explanation →

A company is deploying a large language model in a customer-facing chatbot. The model's responses must be both accurate and safe. Which combination of techniques should be employed?

Question 16hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

A research team is experimenting with few-shot prompting to improve a model's performance on a complex reasoning task. They find that the model's performance degrades when the few-shot examples are too similar to each other. What is the likely cause and best remedy?

Question 17mediummulti select
Read the full Fundamentals of Large Language Models explanation →

Which TWO of the following are common applications of large language models in enterprise settings?

Question 18hardmulti select
Read the full Fundamentals of Large Language Models explanation →

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

Question 19mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

Refer to the exhibit. A developer ran the OCI CLI command shown and received the JSON output. What does the output indicate about the model's confidence and why?

Exhibit

Refer to the exhibit.

```
oci ai language text-classification --text "The product is amazing!"
{
  "data": {
    "labels": [
      {"name": "positive", "score": 0.98},
      {"name": "negative", "score": 0.01},
      {"name": "neutral", "score": 0.01}
    ]
  }
}
```
Question 20hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

Refer to the exhibit. A developer runs the OCI CLI command and receives the output. However, the text "Hello, how are you?" is actually a mix of English and French words. Why does the model assign only 0.03 to French?

Network Topology
oci ai language detect-languagetext "HelloRefer to the exhibit.```"data": {"languages": [
Question 21hardmultiple choice
Read the full NAT/PAT explanation →

You are a machine learning engineer at a large e-commerce company. You have been tasked with deploying a large language model to power a customer service chatbot that handles product returns and refunds. The model will answer customer queries based on a knowledge base of return policies and FAQs. The company has strict requirements: (1) responses must be factually accurate and grounded in the knowledge base, (2) the system must be cost-effective, and (3) latency should be under 2 seconds per response. You decide to use a pre-trained LLM from OCI Data Science and implement retrieval-augmented generation (RAG). You have two options for the retriever: a dense embedding-based retriever (e.g., using OCI AI Language embeddings) or a sparse keyword-based retriever (e.g., BM25). You also need to decide on the generation model size: a 7B parameter model or a 70B parameter model. You run a pilot test: with the dense retriever + 7B model, average latency is 1.8 seconds and accuracy is 85%. With the sparse retriever + 7B model, latency is 1.2 seconds but accuracy drops to 75%. With the 70B model (any retriever), latency exceeds 5 seconds. Which combination should you choose to meet all requirements?

Question 22mediummultiple choice
Read the full NAT/PAT explanation →

A healthcare startup is building an AI assistant to help doctors draft clinical notes from patient-physician conversations. They have a large language model that is fine-tuned on medical data. During testing, they notice the model occasionally generates plausible-sounding but incorrect medical recommendations. The startup wants to deploy the assistant to assist doctors, not replace them. They have the following options: (A) Deploy the model as-is and rely on doctors to catch errors, (B) Add a disclaimer that the model may make mistakes, (C) Implement a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base before presenting to doctors, (D) Reduce the model's temperature to 0 to ensure deterministic outputs. Which option best balances safety and utility?

Question 23hardmultiple choice
Read the full NAT/PAT explanation →

A healthcare startup is building a chatbot to answer patient inquiries using a large language model (LLM) deployed on OCI Data Science AI Quick Actions. The chatbot must comply with HIPAA regulations, so all patient data must remain within the OCI tenancy and never be sent to third-party APIs. The team has fine-tuned a Llama 2 7B model on de-identified medical records using OCI Data Science notebooks. The model is deployed as a managed endpoint via AI Quick Actions. Early testing shows that the chatbot sometimes generates responses containing specific patient names or dates of birth that were present in the fine-tuning dataset. Moreover, the model occasionally hallucinates medication dosages that are not medically accurate. Which course of action should the team take to address both issues while maintaining HIPAA compliance?

Question 24easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A company wants to build a customer support chatbot using OCI Generative AI. They have a large number of historical support tickets. Which approach is most effective for leveraging this data to improve the chatbot's responses?

Question 25easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

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?

Question 26hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

During fine-tuning of a large language model on OCI, you notice that the model's performance on the validation set is not improving after several epochs, but the training loss continues to decrease. What is the most likely cause?

Question 27easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A user wants to use OCI Generative AI to generate marketing copy. They want the output to be more creative and varied. Which parameter should they adjust?

Question 28mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

An organization is concerned about the safety of generated content. Which OCI feature allows them to define custom policies to block inappropriate outputs?

Question 29hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

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?

Question 30easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

Which of the following best describes the role of attention in transformer models?

Question 31mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A company wants to deploy a private instance of a large language model on OCI for sensitive data processing. What is the recommended approach?

Question 32hardmultiple choice
Read the full NAT/PAT explanation →

During inference with OCI Generative AI, you notice that the model is generating repetitive phrases. Which combination of parameters can help reduce repetition?

Question 33easymulti select
Read the full Fundamentals of Large Language Models explanation →

Which TWO statements about tokens in large language models are correct?

Question 34mediummulti select
Read the full Fundamentals of Large Language Models explanation →

A data scientist is evaluating different models for a summarization task. Which two metrics are commonly used to evaluate the quality of generated summaries?

Question 35hardmulti select
Read the full Fundamentals of Large Language Models explanation →

An organization is planning to use OCI Generative AI for sensitive customer data. Which three OCI services or features should they consider for data governance and security?

Question 36mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

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?

Network Topology
oci generative-ai model listcompartment-id ocid1.compartment.oc1..exampleprofile DEFAULT"data": [
Question 37hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

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

Network Topology
oci generative-ai inference generate-textmodel-id cohere.commandprompt "Summarize: ..."max-tokens 100temperature 0.7top-p 0.9frequency-penalty 0.0presence-penalty 0.0Error: (400, RateLimitExceeded, false)
Question 38easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

Refer to the exhibit. A user in group GenAIUsers reports that they cannot call the OCI Generative AI API. What is the most likely issue?

Exhibit

{
  "compartmentId": "ocid1.compartment.oc1..example",
  "definedTags": {},
  "description": "Allow group GenAIUsers to call generative-ai-family in compartment",
  "freeformTags": {},
  "id": "ocid1.policy.oc1..example",
  "name": "genai-policy",
  "state": "ACTIVE",
  "statements": [
    "Allow group GenAIUsers to use generative-ai-family in compartment ExampleCompartment"
  ]
}
Question 39easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A startup needs to deploy a large language model for a customer support chatbot that requires low latency and cost efficiency. They are evaluating OCI Generative AI models. Which model type is most appropriate?

Question 40mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

An AI engineer is testing a large language model on OCI Generative AI and receives this error: 'Token limit exceeded. Maximum context length is 4096 tokens.' The prompt is 4000 tokens long. What is the most effective way to resolve the issue without losing important context?

Question 41hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

A data scientist is designing a prompt to extract structured information (e.g., JSON) from text using an instruct model on OCI Generative AI. The model sometimes outputs additional text beyond the JSON, breaking parsing. Which prompt engineering technique is most effective to enforce structured output?

Question 42easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A company wants to build a retrieval-augmented generation (RAG) system using OCI Generative AI and a vector database. Which model type should they use to convert documents into vector embeddings?

Question 43mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A developer is reviewing the model card for an LLM on OCI Generative AI and notices it was trained on a dataset that is predominantly English. The application will serve users in multiple languages. What is the most likely limitation of using this model without additional steps?

Question 44hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

An architect is optimizing an LLM application that processes long documents. The model has a 4096 token limit, but the documents are often 8000 tokens. They are using a chunking strategy. However, model responses sometimes miss key information that spans across chunks. Which technique most directly addresses this issue?

Question 45easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A researcher wants to compare the performance of two LLMs on OCI Generative AI: a base model and an instruct model. They notice the instruct model often refuses to generate certain types of content. Which factor most likely explains this behavior?

Question 46mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A team is fine-tuning an LLM on OCI Generative AI for a domain-specific task. They have a dataset of 10,000 labeled examples. What is a best practice to avoid catastrophic forgetting during fine-tuning?

Question 47hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

A company uses an LLM to generate product descriptions. The outputs are consistently too verbose and include irrelevant details. The prompt includes a simple instruction: 'Describe the product.' Which adjustment to the prompt is most likely to yield concise, relevant descriptions?

Question 48mediummulti select
Read the full Fundamentals of Large Language Models explanation →

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

Question 49hardmulti select
Read the full Fundamentals of Large Language Models explanation →

Which THREE are known challenges when deploying large language models in production?

Question 50easymulti select
Read the full Fundamentals of Large Language Models explanation →

Which TWO are advantages of using retrieval-augmented generation (RAG) over fine-tuning for incorporating new knowledge?

Question 51mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

Based on the exhibit, which model is best suited for a conversational chatbot that needs to handle multi-turn dialogues?

Exhibit

Refer to the exhibit.

# OCI CLI output from `oci generative-ai model list`
{
  "data": [
    {
      "id": "ocid1.generativeaimodel.oc1..aaaaaa...",
      "model-name": "cohere.command",
      "capabilities": ["chat", "text-generation"],
      "context-window": 4096
    },
    {
      "id": "ocid1.generativeaimodel.oc1..bbbbbb...",
      "model-name": "cohere.base",
      "capabilities": ["text-generation"],
      "context-window": 8192
    },
    {
      "id": "ocid1.generativeaimodel.oc1..cccccc...",
      "model-name": "cohere.embed",
      "capabilities": ["embeddings"],
      "context-window": null
    }
  ]
}
Question 52hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

A developer in the GenAIDevelopers group tries to call the OCI Generative AI inference API but receives an unauthorized error. Which statement best explains the issue?

Exhibit

Refer to the exhibit.

# OCI IAM policy for accessing generative AI models
Allow group GenAIDevelopers to use generative-ai-model-family in compartment ProdCompartment
Allow group GenAIDevelopers to read generative-ai-model in compartment ProdCompartment
Allow group GenAIDevelopers to create generative-ai-inference in compartment ProdCompartment
Question 53easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

Based on the exhibit, what is the primary action the developer must take to successfully make the inference request?

Exhibit

Refer to the exhibit.

# Error from OCI Generative AI inference API
{
  "code": "InferenceRequestTooLarge",
  "message": "The prompt plus max_new_tokens exceeds the model's context length of 8192 tokens.",
  "prompt_length": 6000,
  "max_new_tokens": 4000
}
Question 54mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A company uses OCI Generative AI service to power a chatbot. After deployment, the chatbot starts generating inappropriate responses. Which action should be taken first?

Question 55mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A data scientist wants to improve the accuracy of a summarization model on medical texts. Which OCI service feature is most suitable?

Question 56hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

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

Question 57easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A developer notices that an LLM's responses are too verbose. Which parameter adjustment would most effectively reduce verbosity?

Question 58easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A team wants to deploy an LLM for real-time inference with low latency. Which OCI deployment option is best?

Question 59mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

An AI specialist is troubleshooting why a fine-tuned model produces inconsistent results across different inference calls. What is the most likely cause?

Question 60hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

A company uses RAG (Retrieval-Augmented Generation) with OCI OpenSearch and OCI Generative AI. The system retrieves irrelevant documents. What is the first step to debug?

Question 61easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

An OCI administrator wants to limit which users can invoke a specific LLM endpoint. Which resource type should be used?

Question 62mediummultiple choice
Study the full Python automation breakdown →

A developer is using the OCI Generative AI Python SDK. They receive a 400 error 'InvalidParameter'. What is the most likely reason?

Question 63mediummulti select
Read the full Fundamentals of Large Language Models explanation →

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

Question 64hardmulti select
Read the full Fundamentals of Large Language Models explanation →

Which THREE factors should be considered when choosing between a fine-tuning and a prompt engineering approach?

Question 65easymulti select
Read the full Fundamentals of Large Language Models explanation →

Which TWO techniques can help reduce bias in LLM outputs?

Question 66easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

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

Network Topology
prompt "Explain quantum computing in one sentence."oci generative-ai inference text-generationmodel-id "cohere.command-r-08-2024"max-tokens 100Output:
Question 67mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

Refer to the exhibit. What is the solution?

Network Topology
base-model-id cohere.command-lightoci generative-ai fine-tuning createcompartment-id ocid1.compartment.oc1..exampletraining-dataset-id ocid1.dataset.oc1..exampletraining-params '{"num_epochs": 5Error:"status": 400,"code": "InvalidParameter",
Question 68hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

Refer to the exhibit. A user in GenAI-Users group tries to run a text generation inference but gets permission denied. What is the most likely issue?

Exhibit

IAM Policy:
Allow group GenAI-Users to use generative-ai-family in compartment ABC where request.operation = 'TextGeneration'
Question 69easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

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?

Question 70mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A machine learning engineer is fine-tuning a model on OCI Data Science and notices that the training loss decreases but then suddenly increases. What is the most likely cause?

Question 71hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

An engineer sets beam search width to 1 during inference on OCI Generative AI. What is the most likely effect on output?

Question 72easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

Which technique allows an LLM to be adapted to a new task with only a few examples?

Question 73mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A model generates code with security issues. Which approach is best to mitigate this?

Question 74hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

During multi-turn conversation with an OCI GenAI model, the model repeats user messages from earlier turns. What is the most likely cause?

Question 75easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

What is the role of the softmax function in the output layer of an LLM?

Question 76mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A developer uses OCI Generative AI's chat endpoint with a system message placed after user messages. The model ignores the system message. What is the most likely reason?

Question 77hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

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

Question 78mediummulti select
Read the full NAT/PAT explanation →

Which TWO factors are most likely to cause hallucinations in LLMs?

Question 79hardmulti select
Read the full Fundamentals of Large Language Models explanation →

Which THREE techniques are commonly used to improve the quality of text generation?

Question 80easymulti select
Read the full Fundamentals of Large Language Models explanation →

Which TWO are advantages of using LoRA for fine-tuning?

Question 81mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

Refer to the exhibit. A user in group GenAIGroup cannot see models in the Production compartment using OCI Generative AI. What is the most likely issue?

Exhibit

{
  "statement": "Allow group GenAIGroup to read generative-ai-models in compartment Production",
  "resource": "oci-generativeai:model",
  "compartment": "Production"
}
Question 82hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

Refer to the exhibit. The output is very short and cuts off mid-sentence. Which parameter is most likely the cause?

Exhibit

oci generative-ai inference chat \
--endpoint https://inference.generativeai.us-chicago-1.oci.oraclecloud.com \
--model-id "cohere.command" \
--compartment-id "ocid1.compartment.oc1..example" \
--messages '[{"role":"user","content":"Explain quantum computing."}]' \
--temperature 0.7 \
--top-p 0.9 \
--max-tokens 500
Question 83easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

Refer to the exhibit. A user deployed a custom model via OCI Data Science and registered it in the Model Catalog. They use the correct OCID but get this error. What is the most likely issue?

Exhibit

Error:
{
  "code": "InvalidParameter",
  "message": "The specified model 'my-custom-model' does not exist or you are not authorized to access it."
}
Question 84easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A data scientist is using OCI Data Science to fine-tune a Cohere command model on domain-specific documents. They observe that the fine-tuned model generates repetitive text. What is the most likely cause?

Question 85mediummultiple choice
Read the full NAT/PAT explanation →

A company uses OCI Generative AI to power a chatbot for customer support. They notice that the model's responses sometimes contain factual inaccuracies. Which strategy would best reduce hallucination?

Question 86hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

An architect is designing a multi-tenant application using OCI Generative AI. Each tenant has custom instructions and data. To minimize cost while maintaining isolation, which deployment approach is recommended?

Question 87easymulti select
Read the full Fundamentals of Large Language Models explanation →

Which two factors are essential for calculating the cost of using OCI Generative AI for text generation? (Choose two.)

Question 88mediummulti select
Read the full Fundamentals of Large Language Models explanation →

Which three techniques are commonly used to reduce the risk of prompt injection in LLM applications? (Choose three.)

Question 89hardmulti select
Read the full Fundamentals of Large Language Models explanation →

Which three statements about transformer architecture are correct? (Choose three.)

Question 90easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

An OCI AI Language text classification request returns the output shown. Which conclusion is most accurate?

Exhibit

Refer to the exhibit.

```
$ oci ai language text-classification --text "I love this product!" --endpoint https://language.oci.oraclecloud.com
{
  "data": {
    "text": "I love this product!",
    "document-classification": [
      {
        "text": "I love this product!",
        "labels": [
          {
            "name": "Positive",
            "score": 0.98
          },
          {
            "name": "Negative",
            "score": 0.01
          },
          {
            "name": "Neutral",
            "score": 0.01
          }
        ],
        "version": "1.0"
      }
    ]
  }
}
```
Question 91mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A developer sends this request but receives an error: "modelId not found". Which is the most likely cause?

Exhibit

Refer to the exhibit.

```json
{
  "compartmentId": "ocid1.compartment.oc1..example",
  "modelId": "cohere.command",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of France?"}
  ],
  "maxTokens": 100,
  "temperature": 0.7,
  "topP": 0.9,
  "frequencyPenalty": 0.0,
  "presencePenalty": 0.0
}
```
Question 92hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

The job fails with "InvalidParameter: trainingDatasetUri". What should the administrator check first?

Exhibit

Refer to the exhibit.

```
$ oci ai language fine-tuning-job create \
  --compartment-id ocid1.compartment.oc1..example \
  --model-id cohere.command \
  --training-dataset-uri "oci://bucket@namespace/train.jsonl" \
  --validation-dataset-uri "oci://bucket@namespace/val.jsonl" \
  --parameters '{"numEpochs": 5, "learningRate": 0.0001, "batchSize": 16}' \
  --max-duration-in-minutes 600
```
Question 93easymultiple choice
Read the full NAT/PAT explanation →

An OCI GenAI practitioner wants to deploy a model that can generate code from natural language descriptions. Which type of model is most suitable?

Question 94mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

During fine-tuning of a Cohere model on OCI Data Science, the loss curve shows a sharp spike after epoch 3. What is the most appropriate action?

Question 95hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

A team uses OCI Generative AI's summarization feature to condense legal documents. The summaries sometimes omit critical clauses. Which parameter adjustment is most likely to improve completeness?

Question 96easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

Which OCI service provides pre-trained models for custom text classification without requiring fine-tuning?

Question 97mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

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?

Question 98hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

A data engineer wants to migrate a large corpus of PDFs to OCI for use with GenAI. Which storage and preprocessing approach is most efficient for RAG?

Question 99easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A developer is using OCI GenAI to generate structured data. They often get responses that include additional commentary or markdown. Which prompt engineering technique should they use to ensure only JSON output?

Question 100easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A user has a prompt that exceeds the model's token limit. What is the best practice to handle this?

Question 101easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A startup needs to deploy an LLM for a simple FAQ chatbot on OCI with low latency. Which model choice is most appropriate?

Question 102mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A company wants to create a chatbot that answers questions based on a large internal document set that is updated weekly. They have limited ML expertise. Which approach is recommended?

Question 103mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

An application using OCI GenAI experiences high response times. Which change will most directly reduce latency?

Question 104mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A multi-turn chatbot needs to maintain context across user queries. The context window is limited. What design should be used?

Question 105hardmultiple choice
Read the full NAT/PAT explanation →

A financial institution uses an LLM for generating investment advice. They are concerned about hallucinations. Which method is most effective?

Question 106hardmultiple choice
Read the full NAT/PAT explanation →

A development team wants to generate code snippets from natural language. Which model strategy should they adopt?

Question 107hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

An AI assistant needs to solve complex math word problems step by step. Which prompting technique is most suitable?

Question 108easymulti select
Read the full Fundamentals of Large Language Models explanation →

Which two are essential components of the Transformer architecture? (Select TWO)

Question 109mediummulti select
Read the full Fundamentals of Large Language Models explanation →

Which three factors most significantly affect the quality of an LLM's output? (Select THREE)

Question 110hardmulti select
Read the full NAT/PAT explanation →

Which three characteristics of LLMs can lead to hallucinations? (Select THREE)

Question 111easymultiple choice
Read the full NAT/PAT explanation →

A developer integrates OCI GenAI into a mobile app to provide product descriptions. The responses sometimes include explanations or questions instead of the requested format. The developer is using a simple prompt: 'Describe product X.' The app expects a single paragraph. Which corrective action should the developer take?

Question 112mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A customer support company uses Cohere Command on OCI to answer user queries. They have enabled grounding with a knowledge base of product manuals. However, for about 20% of queries, the model provides incorrect product recommendations that are not in the manuals. The team has verified the knowledge base is up to date. What is the most likely cause and solution?

Question 113hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

A machine learning team is fine-tuning a 7B parameter Llama 2 model on a custom dataset of 10,000 documents using OCI Data Science and GPU instances. They encounter out-of-memory (OOM) errors during the fine-tuning process. They are using a batch size of 8 and a sequence length of 2048. They cannot increase the GPU memory. Which change should they prioritize to resolve the OOM?

Question 114easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A company is building a chatbot using OCI Generative AI service. They want to ensure that the model responses are grounded in their internal knowledge base. Which approach should they use?

Question 115mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A data scientist is using OCI Data Science with the Generative AI service to fine-tune a Cohere Command model on a custom dataset of customer support tickets. After training, the model produces poor, irrelevant responses. What is the most likely cause?

Question 116hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

An enterprise wants to deploy a large language model for processing sensitive internal documents. They must ensure that data does not leave their OCI tenancy. Which OCI GenAI deployment option meets this requirement?

Question 117mediummulti select
Read the full Fundamentals of Large Language Models explanation →

An organization is implementing a RAG system using OCI GenAI. Which two are best practices for optimizing retrieval and generation? (Choose two.)

Question 118hardmulti select
Read the full Fundamentals of Large Language Models explanation →

A developer is evaluating OCI GenAI model families. Which three are correct characteristics of the available models? (Choose three.)

Question 119easymultiple choice
Read the full NAT/PAT explanation →

A healthcare company is using OCI GenAI to generate patient summaries from clinical notes. The model output sometimes includes hallucinated medical facts, such as incorrect dosages or diagnoses, which could be dangerous. The team needs to improve factual accuracy while maintaining data privacy. They have a large collection of internal medical knowledge bases (clinical guidelines, drug databases) that are stored in OCI Object Storage. The current implementation uses a zero-shot prompt with the base Cohere Command model. The data science team has limited GPU resources and wants to avoid building a complex pipeline. Which course of action best addresses the hallucination problem?

Question 120mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

An e-commerce company fine-tuned a Cohere Command model on their product catalog to generate product descriptions. During inference, they notice the model outputs are too repetitive: it often repeats similar phrases across different products, and the descriptions lack diversity. The team wants to increase the variety of the generated text without sacrificing relevance. They are currently using temperature=0.8, top_p=0.9, frequency_penalty=0, and presence_penalty=0. Which parameter adjustment should they make to most effectively increase diversity?

Question 121mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

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?

Question 122hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

A research team is using OCI Data Science and OCI GenAI to build a multilingual chatbot for customer service. They have training data in English, Spanish, and French. The model currently struggles with code-switching—users often mix languages in a single query (e.g., 'Quiero cancel my order'), and the model responds inconsistently, sometimes in English, sometimes mixing incorrectly. The team wants to improve performance on code-switching while maintaining fluency in each language. They have limited compute resources and cannot deploy separate models per language. Which approach should they take?

Question 123hardmultiple choice
Read the full Fundamentals of Large Language Models explanation →

A company is using OCI GenAI with a Dedicated AI Cluster to serve a large language model for real-time chat applications. They notice high inference latency (average 2 seconds per response) and want to reduce it to under 500 milliseconds without significantly degrading the quality of responses. The cluster is configured with NVIDIA A100 GPUs. The model is the base Cohere Command model (52B parameters). They have explored increasing batch size, but that increases latency for interactive use cases. Which action should they take?

Question 124easymultiple choice
Read the full Fundamentals of Large Language Models explanation →

A developer is testing the OCI Generative AI API by sending a request to generate text using the Cohere Command R model. The request returns the following error: 'The model 'cohere.command-r-08-2024' is not available in this region. Please check the model availability in your region.' The developer is using the us-ashburn-1 region. What is the most likely cause of this error?

Question 125mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

A company uses OCI GenAI to build a content moderation system that filters toxic language in user-generated comments. They have a small labeled dataset of 1,000 comments (500 toxic, 500 non-toxic) and need an efficient solution that balances accuracy, cost, and latency. They are considering different model options: fine-tuning a large LLM (e.g., Cohere Command), using a pre-trained LLM with prompting, fine-tuning a smaller BERT-based classifier, or building a rule-based system. The team has moderate ML experience and wants to deploy using OCI Data Science. Which approach is most efficient for this binary classification task?

Question 126easymulti select
Read the full Fundamentals of Large Language Models explanation →

Which TWO statements about large language model (LLM) capabilities are correct?

Question 127mediummultiple choice
Read the full Fundamentals of Large Language Models explanation →

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?

Exhibit

{
  "compartmentId": "ocid1.compartment.oc1..aaaaaa...",
  "servingMode": {
    "modelId": "ocid1.generativeaimodel.oc1..aaaaaa...",
    "servingType": "ON_DEMAND"
  },
  "inferenceRequest": {
    "prompt": "Explain the transformer architecture.",
    "maxTokens": 500,
    "temperature": 0.7,
    "topP": 0.9,
    "frequencyPenalty": 0.0,
    "presencePenalty": 0.0
  }
}
Question 128hardmultiple choice
Read the full NAT/PAT explanation →

A multinational corporation is deploying a generative AI chatbot for customer support using Oracle Cloud Infrastructure's Generative AI service. The chatbot is powered by a large language model (LLM) accessed via the on-demand serving mode. During initial testing, the chatbot provides accurate answers for well-known products but frequently hallucinates or gives incorrect specifications for niche products. The company maintains a comprehensive internal database of product specifications, updated daily. The support team prefers not to fine-tune the LLM due to cost and maintenance overhead. Additionally, the chatbot must respond within 2 seconds to maintain a good customer experience. The team considers several approaches: A. Increasing the 'temperature' parameter to make the model more creative, hoping it will generate more accurate responses when unsure. B. Using few-shot prompting with three manually curated examples of correct product specifications included in every prompt. C. Implementing a Retrieval Augmented Generation (RAG) pipeline that retrieves relevant product documents from the internal database and prepends them to the prompt before inference. D. Reducing the 'topP' parameter to 0.1 to force the model to sample only from the highest probability tokens, thereby reducing randomness. Which approach best meets the requirements of improving factual accuracy while maintaining low latency?

Practice tests

Scored 10-question sessions with instant feedback and explanations.

1Z0-1127 Practice Test 1 — 10 Questions→1Z0-1127 Practice Test 2 — 10 Questions→1Z0-1127 Practice Test 3 — 10 Questions→1Z0-1127 Practice Test 4 — 10 Questions→1Z0-1127 Practice Test 5 — 10 Questions→1Z0-1127 Practice Exam 1 — 20 Questions→1Z0-1127 Practice Exam 2 — 20 Questions→1Z0-1127 Practice Exam 3 — 20 Questions→1Z0-1127 Practice Exam 4 — 20 Questions→Free 1Z0-1127 Practice Test 1 — 30 Questions→Free 1Z0-1127 Practice Test 2 — 30 Questions→Free 1Z0-1127 Practice Test 3 — 30 Questions→1Z0-1127 Practice Questions 1 — 50 Questions→1Z0-1127 Practice Questions 2 — 50 Questions→1Z0-1127 Exam Simulation 1 — 100 Questions→

Practice by domain

Each domain maps to a weighted exam section. Focus on the domain where you are weakest.

Fundamentals of Large Language ModelsUsing OCI Generative AI ServiceBuilding LLM Applications with RAG and Vector SearchDeploying and Managing Generative AI on OCI

Practice by scenario

Filter questions by type — troubleshooting, exhibit, drag-and-drop, PBQ, ACLs, OSPF, and more.

Browse scenarios→

Continue studying

All Fundamentals of Large Language Models setsAll Fundamentals of Large Language Models questions1Z0-1127 Practice Hub