Oracle Cloud Infrastructure 2026 AI Foundations Associate (1Z0-1122-26)
AI and Data Foundations
Review the AI, machine learning, data, and generative AI concepts that appear across the exam.
Official Scope and Verification
This lesson is mapped to the verified Oracle Cloud Infrastructure 2026 AI Foundations Associate (1Z0-1122-26) outline. Official sources and public status were rechecked on 2026-07-13. Provider pages remain authoritative for late-breaking blueprint, availability, scheduling, price, language, delivery, and retake changes.
Current Oracle AI Foundations Associate exam track with official Oracle University exam-page objectives and percentages.
Official Objectives Emphasized Here
| Domain or objective area | Published weight | Key objective groups | Official source |
|---|---|---|---|
| Intro to AI Foundations | 10% | Discuss AI Basics; Discuss AI Applications and Types of Data; Explain AI vs ML vs DL | Oracle University official 1Z0-1122-26 exam page |
| Intro to ML Foundations | 15% | Explain Machine Learning Basics; Discuss Supervised Learning Fundamentals including Regression and Classification; Discuss Unsupervised Learning Fundamentals; Discuss Reinforcement Learning Fundamentals | Oracle University official 1Z0-1122-26 exam page |
| Intro to DL Foundations | 15% | Discuss Deep Learning Fundamentals; Explain Convolutional Models (CNN); Explain Sequence Models (RNN and LSTM) | Oracle University official 1Z0-1122-26 exam page |
| Intro to Generative AI and LLMs | 15% | Discuss Generative AI Overview; Discuss Large Language Models Fundamentals; Explain Transformers Fundamentals; Explain Prompt Engineering and Instruction Tuning; Explain LLM Fine Tuning | Oracle University official 1Z0-1122-26 exam page |
| OCI Generative AI and Oracle 23ai | 10% | Describe OCI Generative AI Services; Discuss Autonomous Database Select AI; Discuss Oracle Vector Search | Oracle University official 1Z0-1122-26 exam page |
| Intro to OCI AI Services | 20% | Explore OCI AI Services and related APIs, including Language, Vision, Document Understanding, and Speech | Oracle University official 1Z0-1122-26 exam page |
Authoritative Sources for This Scope
- Oracle University official 1Z0-1122-26 exam page - Official source; accessed 2026-07-13.
This module gives you the baseline AI and data language needed for Oracle Cloud Infrastructure 2026 AI Foundations Associate (1Z0-1122-26). The goal is not to become a research scientist. The goal is to read an official learning or assessment scenario and know which concept is being tested.
Core Concepts To Know
- AI versus ML versus GenAI. AI is the broad goal of useful machine behavior. ML learns patterns from data. GenAI creates or transforms content such as text, code, images, audio, or structured summaries.
- Training versus inference. Training builds or adapts behavior from data. Inference uses a trained model to produce an output for a new input.
- Prediction versus generation. Prediction chooses a label, score, class, or forecast. Generation creates new content and must be checked for grounding, safety, and quality.
- Foundation model. A large pretrained model that can be adapted through prompting, retrieval, fine-tuning, tools, or workflow design.
- Embedding. A numeric representation of meaning that helps search, clustering, recommendations, semantic similarity, and RAG.
- Evaluation. The discipline of measuring whether outputs are correct, useful, safe, fair, and stable enough for the use case.
Data Foundations
Most AI failures start with data assumptions. For Oracle scenarios, ask where the data comes from, who is allowed to use it, whether it is current, whether labels are reliable, and whether sensitive information is protected.
| Data issue | Why it is tested | Self-learner check |
|---|---|---|
| Missing or stale data | The model may answer confidently from incomplete evidence. | Ask whether retrieval, refresh, or data validation is needed. |
| Biased or unrepresentative data | The output can treat groups or edge cases unfairly. | Look for fairness testing, representative samples, and human review. |
| Sensitive data | Prompts, files, logs, and model outputs can expose private or regulated information. | Apply classification, access control, encryption, masking, and retention limits. |
| Poor labels or definitions | A model cannot learn or evaluate a target that the organization has not defined clearly. | Define success metrics before choosing the model or tool. |
Model And Workflow Vocabulary
- Prompting: giving the model a task, context, constraints, examples, and desired output format.
- Grounding: connecting the model to trusted source material so outputs are tied to current facts.
- RAG: retrieving relevant content and passing it to the model at response time, often better than fine-tuning when source material changes frequently.
- Fine-tuning: adapting a model with training examples, useful for repeatable style or task behavior but not a replacement for current source retrieval.
- Agents: systems that plan or call tools to complete tasks; they need boundaries, permissions, logs, and fallback behavior.
- Human oversight: review by a person when the output affects safety, money, legal rights, employment, healthcare, education, or other high-impact decisions.
Provider-Specific Lens
For Oracle Cloud Infrastructure 2026 AI Foundations Associate (1Z0-1122-26), tie every AI concept back to Oracle Cloud Infrastructure AI, data, and enterprise app integration. A generic definition is useful only if you can apply it to a scenario from Oracle.
- OCI Generative AI
- OCI AI Services
- OCI Data Science
- Autonomous Database
- IAM policies
- OCI Audit
Track-Specific Vocabulary Priorities
- Read the exact credential title first. Many AI credentials are role-based, so the same AI concept can be tested differently for an engineer, architect, auditor, business leader, teacher, or administrator.
- Translate every objective into a real scenario with a user, data source, risk constraint, and expected output.
- Separate durable AI principles from provider product names so you can still reason when a product name changes.
- Know the difference between AI, ML, deep learning, GenAI, foundation models, embeddings, prompts, inference, and evaluation.
- Practice selecting the simplest managed or configured capability before assuming custom model training is required.
- Expect broad scenario questions about responsible use, data handling, service selection, and limitations rather than deep implementation math.
- Map AI workload needs to compute, accelerators, storage, network fabric, orchestration, observability, and capacity planning.
- Understand why AI workloads stress east-west traffic, memory, storage throughput, scheduling, and inference latency differently from ordinary web apps.
- Practice troubleshooting from symptom to layer: user, application, model, endpoint, container, node, network, storage, or control plane.
Example: RAG Or Fine-Tuning
Scenario: a support team needs answers from policy documents that change every month. The best first pattern is usually retrieval-grounded generation because the answer should come from current documents. Fine-tuning may help style or task behavior, but it does not automatically keep the model synchronized with the latest policy.
Common trap: choosing the more advanced-sounding option instead of the pattern that matches the data-change requirement.
Practice Routine
- Make flashcards for the vocabulary above, but put the definition on one side and a workplace example on the other.
- For every provider tool you study, write the AI concept it maps to: search, classification, generation, orchestration, monitoring, governance, or security.
- When you miss a question, classify the miss as vocabulary, data, model choice, security, or operations. Review the category, not just that one answer.
Useful Links
- Oracle AI Certification Path - Official Oracle University AI certification path.
- Oracle Cloud Infrastructure Documentation - OCI documentation for cloud services and controls.
- NIST AI Risk Management Framework - General reference for trustworthy AI risk management.