Oracle Cloud Infrastructure 2023 AI Foundations Associate (1Z0-1122-23) — Lesson 1: AI, Machine Learning, Deep Learning, Data, and Model Foundations

Learning Objectives

• Explain the purpose and operating context of artificial intelligence, machine learning, deep learning, and analytics distinctions.
• Apply supervised, unsupervised, reinforcement learning, and common use cases and features, labels, training, validation, inference, and performance metrics in a controlled environment.
• Validate outcomes and troubleshoot risks involving data quality, bias, overfitting, generalization, and human oversight.

Detailed Topics

• artificial intelligence, machine learning, deep learning, and analytics distinctions
• supervised, unsupervised, reinforcement learning, and common use cases
• features, labels, training, validation, inference, and performance metrics
• data quality, bias, overfitting, generalization, and human oversight

Instructor Demonstration

Demonstrate artificial intelligence, machine learning, deep learning, and analytics distinctions for a business team selecting suitable OCI AI capabilities while recognizing limitations, risks, and governance needs; capture the exact configuration, code, command, transaction, query, dashboard, model, schedule, or report evidence that proves the expected result.

Hands-On Exercise

Complete a controlled Oracle Cloud Infrastructure 2023 AI Foundations Associate (1Z0-1122-23) exercise covering supervised, unsupervised, reinforcement learning, and common use cases and features, labels, training, validation, inference, and performance metrics; introduce an exception involving data quality, bias, overfitting, generalization, and human oversight, diagnose it from evidence, apply the smallest reversible correction, and rerun acceptance checks.

1. Confirm scope, prerequisites, identities, dependencies, data, risks, and expected outcome.
2. Perform the course-specific configuration, development, administration, analysis, or business-process workflow.
3. Capture commands, settings, records, logs, screenshots, reports, metrics, or test results.
4. Compare the observed result with acceptance criteria and document corrective or preventive action.

Scenario Analysis

• Identify the technical or business decision and the stakeholders affected by it.
• Compare security, performance, reliability, compliance, cost, usability, and support trade-offs.
• Define rollback, recovery, communication, evidence, ownership, and escalation requirements.

Troubleshooting Guidance

Diagnose issues involving artificial intelligence, machine learning, deep learning, and analytics distinctions, supervised, unsupervised, reinforcement learning, and common use cases, features, labels, training, validation, inference, and performance metrics, data quality, bias, overfitting, generalization, and human oversight without making an unverified production change. Separate symptoms from causes, gather time-correlated evidence, test a reversible hypothesis, validate the correction, and record recurrence controls.

Validation Checklist

• The requirement, environment, inputs, dependencies, and expected result are documented.
• The result is reproducible and supported by product-specific evidence.
• Normal, boundary, failure, security, performance, and operational conditions are reviewed.
• Exceptions, rollback, ownership, support actions, and the next improvement priority are recorded.

Lesson Outcome

Learners can apply AI, Machine Learning, Deep Learning, Data, and Model Foundations in a realistic Oracle Cloud Infrastructure 2023 AI Foundations Associate (1Z0-1122-23) scenario and defend the result with documented evidence.