1Z0-1122-26 Practice Test: OCI AI Foundations Associate Questions & PDF

Exam overview 1Z0-1122-26

Most candidates lose marks on 1Z0-1122-26 in the two domains they assume are easy. OCI AI Services carries 20% and Generative AI with Large Language Models another 15%, yet the distractors there are built from near-miss service descriptions: Vision versus Document Understanding for text extraction, Language versus Generative AI for text output, Speech versus Language for translation. The exam code itself is 1Z0-1122-26, delivered by Oracle University as 40 multiple-choice questions in 60 minutes with a 65% passing score, free once you complete the matching learning path, and valid for 24 months.

The credential replaced 1Z0-1122-25 when Oracle retired that version on May 29, 2026, and the 2026 release dropped the calendar year from the certification title — so the badge reads Oracle Cloud Infrastructure AI Foundations Associate even though the exam code still ends in -26. Oracle states that no prior AI, ML, or coding experience is required, and the paper is genuinely conceptual: you are asked what a technique is for and which service to reach for, not to write a training loop.

Seven weighted sections make up the blueprint: AI Foundations 10%, Introduction to OCI AI Services 20%, Machine Learning Foundations 15%, Deep Learning Foundations 15%, Generative AI and Large Language Models 15%, OCI AI Portfolio 15%, and OCI Generative AI with Oracle 23ai 10%. That spread rewards breadth — a candidate who can define an epoch and a hallucination but cannot say what a dedicated AI cluster is will plateau around 60%, just short of the cut.

Our bank mirrors that spread. Questions run from perceptrons, activation functions, and the loss-optimizer loop, through supervised versus unsupervised task identification and precision-recall style reasoning about thresholds, into tokenization, in-context learning, foundation models, and RAG retrieval against a VECTOR column in Oracle 23ai. Each item explains why the three distractors fail rather than just naming the answer, and the downloadable PDF carries the same set for offline review or a timed 40-question run at 60 minutes.

Sit one timed pass to locate your weakest of the seven sections, then re-drill only that section before attempting the next full run.

Official Exam Domains & Weighting

To successfully pass the 1Z0-1122-26 exam, candidates must master the following core domains:

Domain 1: AI Foundations (10%)

AI terminology and basics, the AI ⊃ ML ⊃ DL relationship, common industry use cases, structured versus unstructured and labeled versus unlabeled data, and responsible AI principles.

Domain 2: Introduction to OCI AI Services (20%)

The pretrained service split — OCI Language for sentiment and entity extraction, Vision for classification, object detection and OCR, Speech for transcription, Document Understanding for text, tables and key-value pairs — plus the service APIs and OCI Select AI use cases.

Domain 3: Machine Learning Foundations (15%)

Supervised learning with regression versus binary and multi-class classification, labels and feature-to-output mapping, unsupervised clustering and dimensionality reduction, reinforcement learning basics, training versus inference, and model evaluation concepts.

Domain 4: Deep Learning Foundations (15%)

Perceptrons, weighted connections, biases and activation functions, forward and backward passes with loss and optimizer steps, epochs and dropout, CNN convolution and pooling for images, and RNN/LSTM sequence modeling with hidden state.

Domain 5: Generative AI and Large Language Models (15%)

Generative versus discriminative AI, foundation models, tokenization, hallucination behavior, prompt engineering versus fine-tuning, in-context and few-shot learning, instruction tuning, and RAG with embeddings.

Domain 6: OCI AI Portfolio (15%)

The AI services, ML services and AI infrastructure split, OCI Data Science workflows, GPU shapes and Superclusters, dedicated AI clusters, and responsible AI practice on OCI.

Domain 7: OCI Generative AI and Oracle 23ai (10%)

OCI Generative AI Service capabilities with hosted Cohere and Meta Llama models, embeddings endpoints, and Oracle 23ai AI Vector Search with the VECTOR data type, vector indexes and SQL similarity queries feeding RAG pipelines.

Domain coverage 7 domains

AI Foundations (10%)
Introduction to OCI AI Services (20%)
Machine Learning Foundations (15%)
Deep Learning Foundations (15%)
Generative AI and Large Language Models (15%)
OCI AI Portfolio (15%)
OCI Generative AI and Oracle 23ai (10%)

Sample questions 6 of 55 shown

Q1
At a foundational level, what capability is artificial intelligence designed to simulate in computer systems?
1. Human-like reasoning and learning
2. Database query optimization
3. Motherboard voltage control
4. Network address translation
AI is the discipline of building systems that perform tasks that normally require human cognition — learning from examples, reasoning toward a conclusion, and solving problems. Options B, C, and D are all deterministic engineering tasks with fixed rules: a query optimizer follows cost models, voltage control follows firmware thresholds, and NAT follows a translation table. None of them involves inference from data, which is the line that separates AI from ordinary software.
Q2
Which three disciplines, from the choices below, are widely recognized as core subfields of artificial intelligence? (Choose three.)
1. Machine learning
2. Natural language processing
3. Deep learning
4. SQL performance tuning
Machine learning, natural language processing, and deep learning are all branches of AI — ML learns mappings from data, NLP handles human language, and DL implements learning with multi-layer neural networks. SQL performance tuning is database engineering: it depends on execution plans and indexing, not on learned behavior, so it belongs nowhere in the AI taxonomy.
Q3
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
1. AI is a subset of DL, which is a subset of ML
2. DL is a subset of AI, and ML is a subset of DL
3. AI, ML, and DL are entirely separate fields with no overlap
4. ML is a subset of AI, and DL is a subset of ML
The nesting runs AI ⊃ ML ⊃ DL: AI is the broad goal of machine intelligence, ML is the subset that learns from data instead of being explicitly programmed, and DL is the subset of ML that uses deep neural networks. Option A reverses the nesting entirely. Option B inverts the ML/DL relationship — DL is the narrower field, not the wider one. Option C is wrong because the three share methods and vocabulary.
Q4
How does AI enhance human efforts?
1. By processing data at a speed and effectiveness far beyond human capability
2. By completely replacing human workers in all tasks
3. By deleting data humans need to handle
4. By increasing the physical strength of humans
The value proposition is scale and consistency: models scan volumes of data and surface patterns faster than any manual review. Option B overstates the technology — AI automates narrow tasks and still needs human judgment for context, exceptions, and accountability. Option C describes data loss, not augmentation. Option D confuses AI with mechanical or robotic augmentation.
Q5
What key objective does machine learning strive to achieve?
1. Explicitly programming computers with hand-written rules
2. Creating algorithms to solve complex problems
3. Improving computer hardware performance
4. Enabling computers to learn and improve from experience
Machine learning replaces hand-coded rules with models that improve as they see more data — that is the defining objective. Option A is the traditional programming paradigm ML is meant to displace. Option B is too generic to be the objective, since classical algorithms also solve complex problems without learning. Option C is a hardware concern, not a learning one.
Q6
Which three use cases below are common applications of AI in everyday business environments? (Choose three.)
1. Recognizing objects in images
2. Identifying sentiment in customer reviews
3. Providing conversational support through chatbots
4. Compiling source code into an executable program
Image recognition, sentiment analysis, and conversational assistants are all learned behaviors — the system generalizes from examples rather than following fixed instructions. Compiling source code is deterministic translation governed by a grammar; the same input always produces the same output, so it is a classic non-AI task despite being technically sophisticated.

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Reader reviews 66 ratings

4.5 ★★★★★★ Based on 66 ratings

The 1Z0-1122-26 explanations are where this really stands out. Not just which answer is right but WHY the others are wrong.

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Frequently asked questions

Is 1Z0-1122-26 the current AI Foundations exam?

Yes. 1Z0-1122-26 is the live version, and Oracle retired 1Z0-1122-25 on May 29, 2026. Any practice set that still labels itself 1Z0-1122-25 is testing a retired blueprint, so check the code before you buy or study anything.

How many questions and how long, and what score passes?

40 multiple-choice questions, 60 minutes, 65% to pass — 26 correct answers. The pace is 90 seconds per question, and because the paper mixes definitional items with short scenarios, candidates who over-read the long scenario stems tend to run out of time at the end.

Is the exam free, and do I need the learning path first?

Oracle lists the AI Foundations exam as free, with the free attempt tied to completing the matching Oracle MyLearn learning path; without that path the attempt is priced like a standard Oracle exam. There is no prerequisite certification, and no coding, mathematics, or prior AI experience is assumed.

How much AI and machine learning theory does the exam actually require?

Enough to reason, not enough to implement. You should be able to identify whether a scenario needs regression, binary classification, multi-class classification, or clustering; explain what an epoch, a loss value, an activation function, and a hallucination are; and describe why RAG reduces retraining when source content changes. No derivation or coding is required.

What is the difference between the AI Foundations exam and the Data Platform Foundations exam?

They are separate 2026 Foundations credentials. 1Z0-1122-26 covers AI, ML, deep learning, generative AI and the OCI AI portfolio; 1Z0-1195-26 covers Oracle's data platform, including Autonomous Database, Exadata, MySQL HeatWave, NoSQL, data lakes and in-database machine learning. They share the same 40-question, 60-minute, 65% format and the same free pricing model.

How long is the certification valid?

Oracle Cloud Infrastructure certifications are valid for 24 months from the date earned. Recertification means passing the then-current exam release, so a credential earned against -26 is renewed against whatever version is live at renewal time.

Is there a 1Z0-1122-26 mock exam in the real 40-question format?

Yes. The question bank here is numbered for flat exam-style drilling, and the downloadable PDF carries the same items with answers and explanations for offline review. Because seven sections carry different weights, run at least one full timed set before test day so you feel the 20% OCI AI Services block early rather than in the last ten minutes.