Oracle 1Z0-1035-26 Real Exam Questions [October 2026 Update]

Oracle Intelligent Advisor’s Policy Modeler uses plain English rule authoring – rules are written as complete sentences in a Word-like editor, and Policy Modeler translates them into a formal logical rule engine. This design choice makes the tool accessible to business analysts and policy experts who are not programmers, and it is also the source of the most common exam preparation mistake: candidates study the rule syntax without studying the debugging and validation skills that distinguish someone who can write rules from someone who can implement them correctly.

A rule that is syntactically valid – correctly parsed by Policy Modeler without errors – can still produce logically incorrect results when the interview presents certain data combinations. The exam tests debugging scenarios where a rule produces an unexpected outcome for a described case. Resolving these requires tracing which rules fired, in what order, with what intermediate attribute values – skills that require understanding of how Policy Modeler’s backward-chaining inference engine resolves attribute values rather than just how to write rules that look correct.

The third domain – Intelligent Advisor Hub deployment – adds a second distinct competency area that pure Policy Modeler practitioners often have not developed. Deploying a Policy Model to the Hub requires understanding the Hub’s project structure, the deployment process (which version is promoted to live), how the interview is surfaced through the Hub’s web service API or direct interview embedding, and how access is controlled through Hub user management. An Oracle Service Cloud or Oracle CX implementor who has never managed Hub deployments will find this domain the least familiar on the exam.

Exam Snapshot

What Oracle Intelligent Advisor Does

Oracle Intelligent Advisor (formerly Oracle Policy Automation / OPA) is Oracle’s guided decision-making and intelligent interview platform. It allows organisations to encode complex eligibility rules, assessment logic, and compliance determinations into a rule-based engine that can guide customers or staff through a structured interview – collecting the information needed to determine an outcome (eligible/not eligible, approved/declined, recommended action) and presenting that outcome with a full explanation of the reasoning.

Intelligent Advisor is commonly deployed in government benefits eligibility determinations, financial services product qualification, insurance claims assessment, customer service guided resolution, and compliance obligation checking – any context where a complex rule set must be applied consistently across many individual cases.

Domain 1: Understanding Rules and User Interface in Intelligent Advisor

Policy Modeler Rule Authoring

Three rule formats: Rules can be authored in three formats in Policy Modeler. Word rules are plain-English sentences in a Microsoft Word-style document – the most human-readable format, used for complex narrative rules. Excel rules are structured spreadsheets where conditions and outcomes are arranged in rows and columns – ideal for decision tables with many condition combinations. Flowchart rules represent logic as a visual decision tree – appropriate for step-by-step process logic.

The exam tests which rule format is appropriate for described rule structures – a rule with many condition/outcome combinations is best represented in Excel; a rule requiring step-by-step procedural logic is best in a Flowchart; a rule requiring narrative-quality explanation of complex regulatory text is best in Word.

Attributes are the data variables that rules reason about – they carry values that the interview collects from the user or derives from other attributes through inference. Each attribute has a data type (Boolean, Number, Text, Date, Entity). The exam tests attribute typing – a rule that compares two dates must use Date-typed attributes; a rule computing a percentage must use Number-typed attributes.

Entities allow Policy Modeler to reason about collections of instances – a household with multiple members, a claim with multiple line items. Entity attributes are defined per instance; global attributes apply once per interview session. The exam tests when an Entity is required: when the rule must evaluate a variable number of instances (each member of a household, each item on a form) rather than a fixed set of values.

Backward-chaining inference: Policy Modeler uses a backward-chaining rule engine. When the interview needs to determine the value of an outcome attribute (for example, “Is the applicant eligible?”), the engine works backwards through the rules to find what attribute values it needs to determine that outcome, then asks the interview user for any values that cannot be derived from rules alone. This means attributes are never collected unnecessarily – the interview only asks what the current case’s rule path requires. The exam tests backward-chaining as the explanation for why a correctly configured interview asks different questions for different case types.

Uncertainty and unknown values: Policy Modeler handles uncertainty explicitly. An attribute can be True, False, or Unknown (not yet determined). Rules can specify behaviour under uncertainty – whether an outcome is True, False, or Unknown when an input attribute’s value is not yet collected. The exam tests uncertainty handling as a design consideration for interviews where some information may not be available or applicable to every case.

Building Interviews (User Interface)

Interview screens present questions to the user. Screens contain controls – input fields, dropdowns, radio buttons, text areas – mapped to the attributes they collect.

Screen flow defines the sequence of screens and the conditions under which each screen is shown. Conditional screen flow is the key interview design concept the exam tests: showing a screen only when a specific attribute value or rule condition is met, and skipping screens whose information is not relevant to the current case’s rule path.

Goals define what the interview is trying to determine. A Goal attribute is the outcome the interview computes – “Is the applicant eligible?” A session must have at least one Goal attribute, and the backward-chaining engine uses Goals to determine which questions to ask.

Explanations present the reasoning behind the interview’s determination to the end user – explaining which rules applied, with what input values, to reach the stated outcome. The exam tests explanation configuration as a required element for implementations where the organisation must communicate rule reasoning to applicants or customers.

Custom styling and branding: The interview visual appearance is customised through CSS stylesheets and interview-level configuration – logo, colour scheme, fonts. The exam tests styling as an interview configuration task distinct from rule authoring.

Domain 2: Deploying Intelligent Advisor Hub

The Intelligent Advisor Hub is the server-side deployment and management platform for Policy Models. Policy Models are authored in Policy Modeler (a desktop application), then deployed to the Hub for production use.

Hub Projects organise deployed Policy Models. Each project contains one or more Policy Model versions. The Hub maintains version history – multiple versions of a model can coexist, with one version designated as the Live (active) version that interviews use when accessed by end users.

Deployment workflow: A Policy Model is deployed from Policy Modeler to a Hub project using the Deploy command. Deploying creates a new version in the project but does not immediately make it live – the Hub administrator must explicitly promote a version to Live status. This two-step process (Deploy → Promote to Live) ensures that new rule versions do not go live accidentally. The exam tests this workflow and the consequences of promoting the wrong version.

Interview access methods: An interview deployed to the Hub can be accessed in three ways. Direct interview URL (the Hub serves the interview directly through a browser URL – no integration required). Web service (the interview logic is exposed as a REST API, allowing an external system to submit case data programmatically and receive an outcome determination without a user interface). Embedded interview (the interview UI is embedded within another web application using an iframe or the Intelligent Advisor JavaScript API). The exam tests which access method is appropriate for described integration requirements.

Hub Administration: User accounts and access permissions are managed within the Hub. Hub users are assigned roles – Administrator (full access), Developer (can deploy and manage projects), Interviewer (can run interviews for test purposes). The exam tests Hub user role assignment for described deployment scenarios.

Site settings and connection settings: The Hub is configured with Site settings (the Hub’s URL, authentication settings, interview timeout behaviour) and Connection settings (how the Hub connects to integrated data sources – Oracle Service Cloud, Oracle CRM, or custom REST endpoints that interviews call to retrieve or save data).

Domain 3: Using AI Capabilities in Intelligent Advisor

Oracle Intelligent Advisor includes AI capabilities that extend the rule-based interview with machine learning-driven recommendations and natural language processing.

AI-assisted rule authoring: Policy Modeler includes AI-assisted features that suggest rule completions, identify potential logical gaps in a rule set, and flag attributes that are defined but never used in any rule – helping implementers identify incomplete policy logic before testing.

Natural language understanding for interviews: Intelligent Advisor can be configured to accept natural language input from interview users rather than requiring structured form field responses – the AI interprets free-text answers and maps them to interview attributes. The exam tests this capability as an interview design option for use cases where conversational interaction is preferred over form-based data collection.

Integration with Oracle AI Services: Intelligent Advisor can call Oracle AI Services (document understanding, language processing) within interview sessions – for example, extracting data from an uploaded document to pre-populate interview attributes rather than requiring the user to manually enter that data. The exam tests this integration at the conceptual configuration level – what capability it provides and when it is appropriate to deploy it.

Domain 4: Administration

Managing users and projects: Hub administrators manage the user accounts that can access the Hub, assign project-level permissions, and control which user accounts can deploy, promote, or run specific projects.

Policy Model versioning and rollback: If a promoted Live version is found to contain a rule error after going live, the Hub administrator can promote an earlier version back to Live status – effectively rolling back the interview to the previous rule set. The exam tests version management as a production support capability, including how to determine which version is currently Live and how to compare deployed versions.

Checkpoints and debugging in Policy Modeler: Policy Modeler includes a Debugger that allows implementers to trace a specific case through the rule engine – entering attribute values and observing which rules fire, in what sequence, and with what outcomes. The exam tests the Debugger as the primary tool for diagnosing unexpected interview behaviour and incorrect rule outcomes.

Interview testing: Before deploying to the Hub, Policy Models should be tested using Policy Modeler’s built-in interview preview and through the Regression Testing feature – which compares a new rule version’s outcomes against a set of pre-recorded test cases to confirm that existing correct determinations are not broken by rule changes.

5 Study Tips for Oracle 1Z0-1035-26

• Study rule format selection (Word, Excel, Flowchart) by the structural characteristics of the described policy – decision tables with many condition combinations belong in Excel; step-by-step procedural logic belongs in Flowchart; complex narrative regulatory text belongs in Word.
• Study backward-chaining inference as the conceptual model behind why the interview adapts its questions to each case – know that the engine works from Goal attributes backwards through rules to determine what questions to ask.
• Study Hub deployment workflow (Deploy → version in project → Promote to Live) as a two-step process – know the consequence of deploying versus promoting, and how to roll back to a prior version.
• Study the three interview access methods (direct URL, web service API, embedded) and the integration requirement each satisfies.
• Practice with Cert Empire’s 1Z0-1035-26 exam questions covering all four domain areas – rule format selection, interview design, Hub deployment, and AI capability scenarios at implementation professional depth.

Best Study Resources

• Cert Empire 1Z0-1035-26 exam questions PDF and practice simulator (October 2026 edition).
• Oracle official 1Z0-1035-26 exam page (education.oracle.com – search “1Z0-1035-26”).
• Oracle Intelligent Advisor documentation (docs.oracle.com/en/cloud/saas/intelligent-advisor/).
• Oracle University: Implementing Oracle Intelligent Advisor course.
• Oracle Policy Modeler User Guide (included in the Intelligent Advisor documentation set).

Why Candidates Choose Cert Empire for Oracle 1Z0-1035-26 Preparation

Free Practice Tests

Sample our 1Z0-1035-26 questions before purchasing. Test your rule format selection reasoning, backward-chaining understanding, and Hub deployment workflow knowledge before committing.

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Oracle updates Intelligent Advisor with each quarterly release, including AI capability additions. Our 1Z0-1035-26 question bank is continuously reviewed against current Oracle documentation and exam objectives to ensure all content reflects current platform behaviour.

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Every purchase includes our free 1Z0-1035-26 exam guide covering all four domain areas, the rule format selection reference, the Hub deployment workflow, interview access method comparison, and a structured study plan.

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Available at any hour for questions about your materials, your order, or specific Intelligent Advisor concepts – including backward-chaining inference, rule format selection, and Hub deployment workflow questions.

3 Months of Unlimited Access

Three full months of unlimited access to the 1Z0-1035-26 question bank and simulator. Revisit rule authoring scenarios, interview design questions, and Hub administration topics as many times as needed.

Official Vendor Resources – Learn from the Source

Every solution in our 1Z0-1035-26 question bank links directly to the relevant official Oracle documentation – the Policy Modeler User Guide, the Intelligent Advisor Hub Administration Guide, or the specific Oracle documentation section behind the answer.

Deep Knowledge – Every Answer, Every Option, Fully Explained

Every 1Z0-1035-26 question includes a complete explanation covering not just why the correct rule format, interview design choice, or Hub deployment step is right, but precisely why each incorrect option fails – whether it selects the wrong rule format for a described policy structure, misidentifies how backward-chaining determines question sequence, or confuses Deploy with Promote in the Hub workflow.

Interactive Practice Simulator – Think Like the Exam

Our simulator presents Oracle Intelligent Advisor implementation scenarios exactly as the real 1Z0-1035-26 exam does – describing a policy requirement or deployment configuration need and testing which rule format, interview design, or Hub configuration correctly addresses it. Per-question feedback explains the full reasoning behind every correct and incorrect option.

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FAQ’s

What is the Oracle 1Z0-1035-26 certification?

1Z0-1035-26 is the Oracle Intelligent Advisor 2026 Implementation Professional exam. It validates the ability to create and deploy Policy Modeler rule models and Intelligent Advisor interviews – covering rule authoring in Word, Excel, and Flowchart formats, interview design and screen flow, Hub deployment and version management, and AI capability configuration.

What is Oracle Intelligent Advisor used for?

Oracle Intelligent Advisor encodes complex eligibility rules, assessment logic, and policy determinations into a guided interview engine. It is used across government benefits determination, financial services product qualification, insurance claims assessment, and customer service guided resolution – anywhere a complex rule set must be applied consistently to individual cases.

Are there any prerequisites for the 1Z0-1035-26 exam?

No formal prerequisites exist. Oracle recommends familiarity with Oracle Intelligent Advisor through professional implementation experience, but no prior certification is required to sit the exam.

What is backward-chaining inference in Policy Modeler?

Backward-chaining means the rule engine starts from the Goal attribute (the outcome it needs to determine) and works backwards through rules to identify which input attribute values it needs, then asks only those questions. The result is an interview that adapts its question set to each individual case rather than presenting every possible question to every user regardless of relevance.

What is the difference between Deploy and Promote to Live in the Intelligent Advisor Hub?

Deploying a Policy Model from Policy Modeler to the Hub creates a new version in the Hub project – it is staged but not yet active. Promoting a version to Live designates it as the active version that interview end users encounter. This two-step process prevents new rule versions from going live unintentionally and enables administrators to review a deployed version before making it live.

What are the three interview access methods?

Direct URL (the Hub serves the interview through a browser – no integration code required), web service API (the interview logic is exposed as a REST API for programmatic case submission without a user interface), and embedded interview (the interview UI is embedded within another web application). The exam tests which method is appropriate for each described integration requirement.

What rule formats does Policy Modeler support?

Word rules (plain-English sentences – best for complex narrative policy text), Excel rules (decision table format – best for rules with many condition/outcome combinations), and Flowchart rules (visual decision tree – best for step-by-step procedural logic). The exam tests format selection by described policy structure.

What is the passing score for the 1Z0-1035-26 exam?

The passing score is 68%. The exam is multiple-choice format, delivered through Pearson VUE either online-proctored or at a test centre. The exam cost is USD $245.

Related Certifications Worth Exploring

Oracle Intelligent Advisor is most commonly deployed in customer service and CX implementations. The Oracle 1Z0-1038-26 (Service Center 2026 Implementation Professional) exam questions page covers the Oracle Service Center credential – the customer service platform where guided decision-making can support service workflows, case management, knowledge access, and agent interactions. For those extending their Oracle CX expertise into customer engagement and automated communications, the Oracle 1Z0-1032-26 (Responsys Marketing Platform 2026 Implementation Professional) exam questions page covers the adjacent Oracle CX credential focused on customer data, audience segmentation, personalized interactions, campaign automation, and cross-channel engagement workflows.