O R A C L E C E R T I F I C A T I O N
1Z0-1041 Oracle Analytics Cloud 2025 Professional Practice Exam
Exam Number: 4804 | Last updated April 19, 2026 | 875+ questions across 5 vendor-aligned objectives
The 1Z0-1041 Oracle Analytics Cloud 2025 Professional exam is written for BI developers, data engineers, and analytics consultants who design, build, and manage content on Oracle Analytics Cloud. Candidates validate the ability to model enterprise semantic layers, build interactive visualizations, configure machine-learning-assisted analytics, and administer an OAC instance including security, data connections, and content promotion between environments.
The heaviest content area is Data Modeling and Data Preparation (roughly 30%), covering the Semantic Modeler, subject areas, joins, calculated columns, data flows, data sets, and the self-service enrichment recommendations OAC surfaces. Visualization and Dashboard Design carries another 25% with canvas layout, narration, parameters, drill paths, and mobile rendering.
Administration and Content Lifecycle contributes around 20% for instance scaling, SSO, role mapping, and snapshot-based promotion between development and production. Advanced Analytics covers the remaining weight with Explain, Machine Learning scripts, Auto Insights, and Oracle Analytics Cloud Day by Day for mobile consumption.
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Question #1 - Data Modeling and Data Preparation
A BI consultant at a regional hospital network is designing an enterprise semantic model in Oracle Analytics Cloud. Finance, Clinical, and Operations teams all need their own subject-area views without duplicating logical tables. She needs a way to expose different columns and filters to each team from the same physical layer.
Which Semantic Modeler artifact supports this multi-audience exposure pattern?
A) Multiple presentation layers (subject areas) built on the same business model.
B) Three copies of the workbook with different filters saved on each.
C) Three parallel physical connections to the same database.
D) Separate data sets duplicated for each team.
Show solution
Correct answers: A – Explanation:
In Oracle Analytics Cloud’s governed semantic model, one business model (logical layer) can be published as multiple subject areas in the presentation layer, each with its own curated columns, hierarchies, and permissions. Option D duplicates data and breaks governance. Option C solves a connectivity problem that does not exist. Option B manages workbook filters, not modeling concerns. Source: Check Source
Question #2 - Visualization and Dashboard Design
A sales operations analyst at a B2B software firm is building a workbook showing pipeline health by region. She wants viewers to click a region on a bar chart and have a companion line chart automatically filter to that region’s monthly trend.
Which Oracle Analytics Cloud feature delivers this interactive filtering without custom code?
A) A narration added to the canvas.
B) Visualization-to-visualization interactions (auto-filter on selection).
C) A manually crafted SQL view in the dataset.
D) A scheduled snapshot emailed to viewers.
Show solution
Correct answers: B – Explanation:
Oracle Analytics Cloud automatically links visualizations on the same canvas: clicking a category in one chart filters other visualizations, with no code required. Option A adds a text layer, not interactivity. Option C would enforce a static filter. Option D is a distribution mechanism, not interactive. Source: Check Source
Question #3 - Advanced Analytics and ML
A retail analyst at a fashion chain has attached a workbook to a sales data set and wants Oracle Analytics Cloud to surface the key drivers of a target column (Units_Sold) using built-in AI, without having to code an ML model from scratch.
Which feature provides this one-click driver analysis?
A) Data Actions exported to the ERP.
B) Augmented KPI strips on the dashboard.
C) Explain on the target column.
D) A custom BI Publisher report.
Show solution
Correct answers: C – Explanation:
Explain in Oracle Analytics Cloud runs automated statistical analyses on a chosen column and returns key drivers, segments, anomalies, and predictive insights with no code. Option A pushes data to downstream systems. Option D produces pixel-perfect reports, not driver analysis. Option B surfaces KPIs but does not explain drivers. Source: Check Source
Question #4 - Data Modeling and Data Preparation
An analytics engineer at a utilities company is cleansing a customer dataset with 1.2 million rows in Oracle Analytics Cloud. She wants the preparation steps — standardize casing, parse a full-name column, and remove rows where account_status is ‘Closed’ — to be reproducible and re-runnable when the source refreshes.
Which OAC feature captures these steps as a reusable, schedulable pipeline?
A) A shared workbook.
B) A one-time export to CSV.
C) A visualization narration.
D) A Data Flow.
Show solution
Correct answers: D – Explanation:
Data Flows in Oracle Analytics Cloud chain transformations (filter, split, cleanse, merge, save) into a saved, schedulable pipeline that produces curated data sets. Option B is a manual one-off. Option C narrates a canvas. Option A lets users collaborate on a workbook but does not transform data. Source: Check Source
Question #5 - Administration and Content Lifecycle
A lead administrator is promoting a set of workbooks, data sets, and the semantic model from Dev to Prod on Oracle Analytics Cloud. She wants a single artifact to carry everything, including connections and security artifacts where possible.
Which OAC promotion mechanism is designed for this?
A) Take a snapshot of the instance and import it in the target.
B) Export each workbook as a .dva file individually.
C) Re-create every object by hand in Prod.
D) Copy the Essbase cube manually.
Show solution
Correct answers: A – Explanation:
A snapshot bundles catalog, semantic model, connections, users, and roles into one file for promotion between environments, and is the recommended path for content lifecycle. Option B works for single workbooks but does not carry the model or security roles. Option D is unrelated to content promotion. Option C defeats the purpose of automation. Source: Check Source
Question #6 - Visualization and Dashboard Design
A mobile-first product manager at a consumer-goods startup wants sales managers in the field to consume a daily KPI briefing on their phones, with voice-assisted narration and hands-free scrolling.
Which Oracle Analytics Cloud experience targets this use case?
A) An Essbase outline export.
B) Oracle Analytics Day by Day mobile app.
C) A BI Publisher burst.
D) A scheduled CSV download.
Show solution
Correct answers: B – Explanation:
Oracle Analytics Day by Day is the mobile experience that delivers personalized daily insight cards with voice and chat-style interactions. Option A is an OLAP artifact, not a consumer app. Option C is a static document burst. Option D is raw data, not a narrative mobile experience. Source: Check Source
Question #7 - Data Modeling and Data Preparation
An implementation partner building a new OAC model has two logical dimension tables (Customer and Region) that join to a fact table (Sales). Finance wants the Sales fact to support both current and historical region assignments, and each customer historically could move between regions.
Which Semantic Modeler construct is best to capture the changing customer-to-region relationship over time?
A) A flat extract refreshed nightly without history.
B) A calculated measure on the fact table.
C) A slowly changing dimension modeled as a Type 2 snapshot with effective dates.
D) A single row per customer with the latest region.
Show solution
Correct answers: C – Explanation:
A Type 2 slowly changing dimension stores one row per historical version with effective-from/to dates, letting facts join to the region that was valid at the transaction date. Option D loses history. Option A also discards history. Option B puts responsibility on a measure that cannot replicate proper historical keying. Source: Check Source
Question #8 - Advanced Analytics and ML
A data scientist at a bank has trained an OML (Oracle Machine Learning) classification model in Autonomous Database. She wants to surface the predictions inside an OAC workbook without exporting scored results to a flat file.
Which pattern keeps the model and data in Autonomous Database while surfacing predictions live in OAC?
A) Schedule an email of the model output daily.
B) Rebuild the model inside OAC using Data Flows.
C) Export the model as a CSV of scored rows and upload it.
D) Register the OML model and call it from a data flow or workbook calculation.
Show solution
Correct answers: D – Explanation:
OAC can register OML models and invoke them in data flows and workbook calculations, so predictions stay live against Autonomous Database. Option C decouples predictions from the source data. Option B duplicates work. Option A is a delivery mechanism, not an integration mechanism. Source: Check Source
Question #9 - Administration and Content Lifecycle
An OAC administrator at a pharmaceutical company needs row-level security so that each sales rep sees only their own territory’s data, even when running the same workbook. The territory assignment lives in an external HR system and flows into OAC via identity attributes.
Which OAC feature enforces row-level security based on the signed-in user?
A) Application roles combined with session variables in the semantic model.
B) Hiding columns in each workbook visualization.
C) A filter bar set per workbook.
D) Encrypting the underlying database files.
Show solution
Correct answers: A – Explanation:
Application roles map identity attributes into session variables, which are then used in data filters on logical tables in the semantic model — each user’s query is silently restricted to their territory. Option B hides columns, not rows. Option C can be removed by the user. Option D protects data at rest, not per-user visibility. Source: Check Source
Question #10 - Advanced Analytics and ML
A product marketing lead at an IoT vendor has a time-series of device error counts per hour for the last 90 days. She wants OAC to automatically flag hours that look anomalous compared to the historical pattern.
Which Oracle Analytics Cloud capability addresses this need without custom ML code?
A) A BI Publisher alert email template.
B) Converting the time series to a bar chart and eyeballing outliers.
C) Auto Insights and the built-in anomaly detection in Explain.
D) A geospatial map layer.
Show solution
Correct answers: C – Explanation:
Auto Insights and Explain include automated anomaly detection on time-series measures, surfacing statistically unusual points without manual coding. Option B relies on the human eye and is not reproducible. Option A is a publishing engine, not analytics. Option D is unrelated to time-series anomalies. Source: Check Source
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What the 1Z0 1041 Analytics Cloud Pro exam measures
- Data modeling and preparation (30%) — design semantic models with the Semantic Modeler, build data flows and data sets, apply enrichment recommendations, and curate subject areas, calculated columns, and logical joins for governed self-service.
- Visualization and dashboard design (25%) — craft canvases, storyboards, parameters, drill paths, and narration that render cleanly on desktop and Oracle Analytics Day by Day.
- Advanced analytics and ML (25%) — apply Explain, Auto Insights, and Machine Learning scripts, register OML models, and surface predictive outputs inside workbooks.
- Administration and content lifecycle (20%) — manage instance scaling, identity and role mapping, snapshot-based promotion, and connection security across dev, test, and production environments.
How to prepare for this exam
- Review the official 1Z0-1041 exam page for the current objectives and topic weights.
- Complete the Oracle University Oracle Analytics Cloud 2025 Professional learning path on MyLearn.
- Provision a trial OCI tenancy, launch an OAC instance, load the Sample Order Lines data set, and rebuild every out-of-the-box workbook from scratch.
- Apply the skills on real data — rebuild a stale report at work, donate analytics to a nonprofit, or contribute a dashboard to a public data community.
- Master one objective at a time, beginning with data modeling and preparation since it carries the most weight.
- Run PowerKram learn mode to see feedback after every question with sourced links back to Oracle documentation.
- Finish with PowerKram exam mode across all objectives until you pass three back-to-back attempts.
Career paths and salary outlook
Oracle Analytics Cloud skills are in demand on modern BI and analytics engineering teams:
- Oracle Analytics Consultant — $110,000–$155,000 (Glassdoor).
- BI Developer — $85,000–$125,000 (PayScale).
- Analytics Engineer — $120,000–$170,000 (Levels.fyi).
Official resources
Begin with the Oracle Analytics Cloud 2025 Certified Professional Learning Path on Oracle MyLearn. Supplement with the Oracle Analytics Cloud documentation library and the Oracle Analytics blog for release-specific updates.