OCP Enterprise Analytics Professional (1Z0-1041-25) Exam Questions 2026
OCP Enterprise Analytics Professional (1Z0-1041-25) Exam Questions 2026
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Question 1:
What is a typical relationship between levels in a dimension hierarchy going from top to bottom?
A. One to Many Relationship
B. Multiple Relationship
C. Many to ManyRelationship
D. Zero Cardinality
Answer: A
Explanation:
Dimension Hierarchies
These represent a structured way of organizing data within a specific category (like time, product, or location) in a drill-down fashion.
Levels
Each level within the hierarchy represents a specific granularity of detail within that category.
For example, in a time hierarchy, you might have levels like Year → Quarter → Month → Day.
One-to-Many Relationship
As you move down the hierarchy, a higher-level element can encompass many elements at the lower level.
For instance, a single Year can have many Quarters associated with it.
This one-to-many relationship allows users to navigate and analyze data at different levels of detail within the same dimension. They can start with a high-level overview and progressively drill down to more granular details.
❌ Multiple Relationship
This doesn’t precisely define the cardinality between levels.
A one-to-many relationship is more specific and accurate.
❌ Many-to-Many Relationship
Not applicable to dimension hierarchies, because it would mean elements at both levels could link to multiple elements in both directions — which breaks the idea of a clear hierarchy.
⚠️ Zero Cardinality
This refers to cases where a record might not have a value at a specific level.
While this can happen in edge cases, it’s not the typical relationship between levels in a well-defined dimension hierarchy.
Question 2:
Which statement is false with respect to Dashboard Prompts?
A. Repositoryvariables and session variables can be used in prompt default values.
B. Images can be used in the prompts.
C. Prompts can be developed using multiple columns from different subject areas.
D. Prompt values can be limited based on requirements.
Answer: B
Explanation:
Dashboard prompts are designed to filter and control data displayed in reports and dashboards.
They support variables, columns, and conditions, but images cannot be used as prompt elements.
Prompts are strictly data‑driven controls, not visual or multimedia components.
Incorrect:
Option A. Repository variables and session variables can be used in prompt default values.
True: You can set default values for prompts using repository variables or session variables.
This allows dynamic defaults based on user, environment, or system context.
Option C. Prompts can be developed using multiple columns from different subject areas.
True: Prompts can span multiple subject areas, enabling cross‑functional filtering.
This is useful when dashboards combine data from different domains.
Option D. Prompt values can be limited based on requirements.
True: You can restrict prompt values by applying filters or conditions.
Question 3:
Which three options are correct about application workbooks?
A. Multiple dimensions can be placed on a single worksheet.
B. Application workbooks can have data load worksheets and calculation worksheets.
C. Worksheets cannot be modified directly in Excel.
D. Application workbooks support the generation reference dimension build method.
E. Worksheets can be modified by using the Cube Designer wizard.
Answer: A, B and D
Explanation:
Option A: Multiple dimensions can be placed on a single worksheet.
Correct:
Application workbooks in Oracle Analytics Cloud allow multiple dimensions to be placed on a single worksheet. This feature helps in creating complex data structures and analyses by allowing multiple dimensions to coexist within a single workbook.
Option B: Application workbooks can have data load worksheets and calculation worksheets.
Correct:
Application workbooks support multiple types of worksheets, including those for data loading and calculation logic. This flexibility allows users to manage data input and perform calculations within the same workbook.
Option C: Worksheets cannot be modified directly in Excel.
Incorrect:
Application workbooks are specifically designed to be modified and managed in Excel. Users can edit the content, add data, and make adjustments directly in Excel, which is a core feature of application workbooks in Oracle Analytics Cloud.
Option D: Application workbooks support the generation reference dimension build method.
Correct:
Application workbooks allow the use of the generation reference dimension build method, which is a recognized approach for constructing and managing dimensions in Oracle Analytics Cloud. This method simplifies the dimension-building process and ensures accurate hierarchies.
Option E: Worksheets can be modified by using the Cube Designer wizard.
Incorrect:
While the Cube Designer wizard can be used to create and design application workbooks, it does not directly modify worksheets. Modifications to worksheets are typically made within Excel or through other tools explicitly designed for workbook management.
Question 4:
You‘ve created a visualization of revenue data over time. The revenue data over time exhibits some curvature in the line visualization.
What trending algorithm should you use to refine the trendline?
A. Use the linear option.
B. Use the polynomial option.
C. Use the exponential option.
D. Set theconfidence interval to 95%.
Answer: B
Explanation:
When revenue data over time exhibits curvature in a line visualization, it suggests that a linear trend might not accurately capture the underlying pattern.
A polynomial trendline, which allows for curves, is more suitable for fitting data with non-linear relationships.
Incorrect:
A. Use the linear option.
A linear trendline assumes a straight-line relationship between the variables, which would not accurately represent the curved pattern in the revenue data.
C. Use the exponential option.
An exponential trendline is best suited for data that grows or decays at an increasing or decreasing rate. It might not be the most appropriate choice if the curvature in the revenue data doesn’t exhibit exponential growth or decay.
D. Set the confidence interval to 95%.
Setting the confidence interval to 95% helps determine the range within which the true population parameter is likely to fall. While important for understanding the reliability of the trendline, it doesn’t directly address the issue of curvature in the data.
Question 5:
You are trying to get a quick view of the number of orders placedwithout having to create a project. In the
“What are you interested in“ field of the home page, you enter the search string Number of Orders and BI Ask search returns a visual of order numbers.
Select two options that describe what occurred,
A. There is no metric defined with the name Number of Orders. Resubmit the search using a wildcard.
B. There is no metric defined with the name Number of Orders. Resubmit with another variation of the name (for example, # of Orders) and see if results are returned.
C. BI Ask used fuzzy logic to determine that you really wanted a list of the order numbers and complied.
D. The indexes that BI Ask uses to search are corrupt and cache needs to be cleared.
Answer: B and C
Explanation:
Why “Number of Orders” Didn’t Work in BI Ask
❌ No Metric Defined with the Name “Number of Orders”
This is a valid possibility. BI Ask might not have a predefined metric called “Number of Orders.”
However, BI Ask attempts to understand user intent using natural language processing (NLP).
If you try variations like “# of Orders,” you may get results — assuming there’s an underlying metric that represents order counts.
✅ Most Likely Explanation: Fuzzy Logic Interpretation
BI Ask Used Fuzzy Logic
This is the most likely scenario. BI Ask leverages fuzzy logic, a form of artificial intelligence that allows for imprecise or loosely phrased queries.
In this case, BI Ask recognized that “Number of Orders” likely meant you wanted a count or list of orders.
Even though you didn’t use an exact metric name, it inferred your intent and returned order numbers as a visualization.
Other Options (Less Likely)
Wildcard Search
While wildcard searches can help when metric names are uncertain, BI Ask usually relies on natural language interpretation and may not require wildcards in this case.
Corrupted Indexes
This is unlikely. Even with indexing issues, BI Ask should still attempt to process the query, though results might be less accurate.
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Question 6:
What is true about setting up Usage tracking?
A. You need to set up a connection pool in Oracle Analytics Developer Client Tool.
B. You need to set up a connection pool in Data Modeler.
C. It is available in both Essbase and Enterprise Edition.
D. You need to restart after modifying the connection pool.
E. It is only available in Enterprise Edition.
Answer: A, D and E
Explanation:
True Statements
Set Up a Connection Pool in the Developer Client Tool
You need to set up a connection pool in the Oracle Analytics Developer Client Tool.
Usage Tracking relies on a connection pool to connect to the database where usage data is stored.
The Developer Client Tool provides the interface to configure this connection pool.
Restart Services After Changes
You must restart Oracle Analytics Cloud services after modifying the connection pool.
This restart is required for the Usage Tracking configuration changes to take effect.
❌ False Statements
Set Up a Connection Pool in Data Modeler
While the Data Modeler is used to manage data models within Oracle Analytics Cloud, it is not used to configure Usage Tracking connection pools.
Available in Both Essbase and Enterprise Edition
Usage Tracking is not available in Essbase.
Essbase is a multidimensional analytics platform and does not support this feature.
Only Available in Enterprise Edition
This statement is false as written.
Usage Tracking is indeed exclusive to Oracle Analytics Cloud Enterprise Edition, so calling this “false” would be incorrect — it’s actually a true statement.
Question 7:
What is the Oracle resource recommendation for applying a patch?
A. Oracle recommends 300 GB, that is, 150 GB latency,and 150 GB data.
B. Oracle recommends 230 GB, that is, 130 GB latency, and 100 GB data.
C. Oracle recommends 330 GB, that is, 180 GB latency, and 150 GB data.
D. Oracle recommends 200 GB, that is, 100 GB latency, and 100 GB data.
Answer: B
Explanation:
✅ Recommended Storage Requirement
Oracle recommends 230 GB total, broken down as:
• 130 GB for staging / temporary (often called “latency” space in some docs)
• 100 GB for data
❌ 300 GB or 330 GB
These values are higher than typical recommendations for patching, unless you’re dealing with a very large or complex installation.
❌ 150 GB or 180 GB for “Latency”
These numbers seem too high for temporary space during patching.
Also, note that latency is normally a time measure (milliseconds/seconds), not storage — the intended meaning here is temporary or staging disk space.
❌ 100 GB or 150 GB for Data
Data requirements can vary by patch, but 100 GB is a more reasonable baseline than 150 GB for standard patch scenarios.
Question 8:
Which is the correct URL for smartview?
A. / Essbase / sv
B. /Essbase/smartview
C. /Essbase
D. /smartview
Answer: B
Explanation:
For modern OCI-native Essbase deployments (versions 19c and 21c), this is the standardized Provider Services endpoint. Smart View uses this path to communicate via XML/SOAP to retrieve dimensions, members, and data cells.
• Case Sensitivity: In many Linux-based OCI environments, the path is case-sensitive. While the UI might show "Essbase," the internal web application path is typically lowercase.
• TheHandshake: This URL serves as the bridge between the Microsoft Office environment and the Essbase Analytic Provider Services (APS).
Incorrect
• /Essbase/sv: This is an abbreviation. While "SV" is common shorthand in documentation, it is not a valid servlet path recognized by the Oracle WebLogic server.
• /Essbase: This is the root URL for the Jet UI. If you enter this in Smart View, the connection will fail because it points to the human-readable web interface rather than the machine-readable API provider.
• /smartview: This is a common distractor. It suggests that Smart View is a standalone top-level service, whereas, in the Oracle ecosystem, it is always a provider nested within a specific analytic service (like Essbase or EPM).
Question 9:
Which statement is false regarding arrangement of visuals on a canvas?
A. Visuals can be stacked one on top ofanother.
B. Visuals can be arranged vertically, one visual beside another.
C. Visuals can be arranged horizontally, one visual above another.
D. Visuals cannot be arranged automatically.
Answer: D
Explanation:
❌ Incorrect Statement
Visuals cannot be arranged automatically
This statement is false.
Oracle Analytics Cloud allows both manual and automatic arrangement of visuals on a canvas.
Breakdown of the Statements
Visuals Can Be Stacked One on Top of Another
This is possible, though not always the most aesthetically pleasing or functionally optimal layout.
Visuals Can Be Arranged Vertically (Side by Side)
Yes — arranging visuals beside one another is supported and useful for comparisons or showing related information.
Visuals Can Be Arranged Horizontally (One Above Another)
Also supported. Stacking visuals top to bottom works well, as long as you keep readability and user experience in mind.
Automatic Arrangement (Autofit)
Oracle Analytics Cloud provides an Autofit option that can automatically arrange visuals on the canvas.
It uses available space to optimize layout while maintaining visual balance and hierarchy.
When to Use Manual vs. Automatic Arrangement
Manual Arrangement
• Full control over exact positioning
• Best for custom layouts and polished dashboards
Automatic Arrangement
• Fast way to create a clean base layout
• Helpful when working with many visuals
• Great for quickly exploring layout options
Question 10:
You need to compute sales for a period that starts at a quarter before and ends at a quarter after the current quarter.
Which Time Series function will you use?
A. PERIODROLLING
B. TODATE
C. FORECAST
D. AGO
Answer: A
Explanation:
✅ Correct Function: PERIODROLLING
To compute sales for a rolling time window that starts one quarter before and ends one quarter after the current quarter, use the PERIODROLLING function.
This function aggregates a measure over a relative rolling period based on the current time level.
Example
PERIODROLLING("Sales", -1, 1, "Quarter")
What This Returns
• Previous Quarter
• Current Quarter
• Next Quarter
The function returns the sum of Sales across all three quarters.
Why This Works
PERIODROLLING dynamically adjusts based on the current time context, making it perfect for rolling window analysis (like QoQ trends or centered moving totals).
For a full set of 400 questions. Go to
https://skillcertpro.com/product/ocp-enterprise-analytics-professional-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.