2026 Actual Oracle 1Z0-1122-26 Dumps, Updated Exam Questions for Guaranteed Pass

1Z0-1122-26 free sample questions and answers

Oracle 1Z0-1122-26 practice quiz with correct answers for search indexing.

1. Question 1

   A healthcare organization is evaluating AI/ML approaches for different use cases. For one project, they have large volumes of patient data and want to discover unknown patterns or groups of patients with similar characteristics for research purposes, without predefined labels. For another project, they need to predict patient readmission risk within 30 days based on historical data. Which learning paradigm is best suited to each use case respectively?

   APattern discovery use case: Unsupervised learning (clustering); Readmission prediction use case: Supervised learning (regression or classification)
   

   BPattern discovery use case: Supervised learning with labels created retrospectively; Readmission prediction use case: Unsupervised learning clustering
   

   CBoth use cases: Reinforcement learning because healthcare decisions require sequential optimization
   

   DPattern discovery use case: Supervised learning (classification); Readmission prediction use case: Unsupervised learning (clustering)
   

   Correct answer: A

   The correct answer properly matches learning paradigms to use cases: unsupervised learning (specifically clustering) is ideal when you want to discover natural groupings without predefined labels, while supervised learning is appropriate when you have a clear target variable (readmission yes/no) to predict based on historical examples.

   Why other options are incorrect:

   • Option 2 Reverses the appropriate paradigms; creating labels retrospectively defeats the purpose of exploratory pattern discovery, and unsupervised learning is less direct than supervised for a concrete prediction task
   • Option 3 Reinforcement learning is for sequential decision-making with reward signals, not for static prediction or pattern discovery in healthcare data
   • Option 4 Incorrectly assigns supervised classification to pattern discovery (requires predefined labels) and unsupervised clustering to readmission prediction (requires a target outcome)

2. Question 2

   What is the purpose of the model catalog in OCI Data Science?

   ATo create and switch between different environments
   

   BTo provide a preinstalled open source library
   

   CTo store, track, share, and manage models
   

   DTo deploy models as HTTP endpoints
   

   Correct answer: C

   The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.

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3. Question 3

   In machine learning, what does the term "model training" mean?

   AWriting code for the entire program
   

   BPerforming data analysis on collected and labeled data
   

   CAnalyzing the accuracy of a trained model
   

   DEstablishing a relationship between input features and output
   

   Correct answer: D

   In machine learning, 'model training' refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.

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4. Question 4

   What are Convolutional Neural Networks (CNNs) primarily used for?

   AImage classification
   

   BText processing
   

   CImage generation
   

   DTime series prediction
   

   Correct answer: A

   Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.

   CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.

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5. Question 5

   An e-commerce company is implementing prompt engineering strategies for their chatbot powered by OCI Generative AI. They want to improve response quality and consistency. Which of the following best practices should they prioritize?

   AProviding clear, specific instructions with examples (few-shot prompting), maintaining consistent formatting, and iterating on prompts based on output quality to refine behavior
   

   BMaking prompts as short as possible to minimize computational cost, and avoiding any examples or context as they slow down processing
   

   CInstructing the LLM to always provide responses in a single paragraph regardless of topic complexity, as this standardizes output format
   

   DUsing abstract, philosophical language in prompts because LLMs perform better with creative rather than direct instruction
   

   Correct answer: A

   The correct answer reflects established prompt engineering best practices: clarity, specificity, in-context examples, and iterative refinement. These techniques significantly improve LLM output quality and consistency for business applications.

   Why other options are incorrect:

   • Option 2 While brevity matters, omitting context and examples actually reduces quality; modern LLMs handle more detailed prompts efficiently and benefit from examples
   • Option 3 Forcing single-paragraph format regardless of content complexity reduces usability and clarity
   • Option 4 LLMs respond better to direct, clear instructions than abstract language; clarity beats creativity for predictable business outcomes