Free Practice Questions for Adobe AD0-E207 Exam

For Single Page Applications (SPAs), it is important to capture the URL changes correctly. Thes.pageURLvariable should be mapped todataLayer.urlto ensure that Adobe Analytics captures the correct URL each time the state changes in the SPA. This allows accurate tracking of user navigation within the SPA framework.

To ensure that Adobe Launch and its variables are correctly defined and available when the page view call is fired, the data layer should be placed before the Adobe Launch script in thesection of the webpage. This ensures that all data layer variables are available to the Launch script during its execution.

 Business Requirement: The company wants to ensure that the original search term ("Mobile") receives credit for the purchase, despite subsequent searches.

 Understanding Merchandising eVars: Merchandising eVars are used to attribute success events (like purchases) to specific values captured earlier (like search terms).

 Allocation and Expiration Settings:

Original Value (First) Allocation: This setting ensures that the first value captured (in this case, "Mobile") remains attributed to the visitor, regardless of subsequent values.

Expiration Setting: Setting the expiration to the purchase event ensures that the value ("Mobile") remains active until the visitor makes a purchase.

 Explanation:

Configure an eVar as Merchandising Variable: This allows tracking specific values like search terms in relation to product views and purchases.

Original Value (First) Allocation: Ensures that the initial search term ("Mobile") gets credit.

Expiration to Purchase Event: Keeps the eVar value until the purchase is made, ensuring accurate attribution.

 Verification: According to Adobe Analytics documentation on Merchandising eVars, using Original Value allocation with appropriate expiration settings ensures correct attribution of original search terms to final purchases (Adobe Analytics Implementation Guide).

 Business Requirement: Report on the monetary value of a visitor's cart at the checkout page without using product IDs.

 Variable Configuration:

s.events: Captures the checkout event and the monetary value.

s.products: Lists the product names and their corresponding values.

 Explanation:

s.events = "scCheckout,event1": This sets the event to capture the checkout action and records the event value.

s.products = ";Novelty Mug;;;event1=8.00,;Coffee Beans;;;event1=10.00": This format lists the products by name and assigns their respective monetary values.

 Verification: According to Adobe Analytics product string documentation, the format used correctly attributes product names and values to the checkout event (Adobe Analytics Implementation Guide).

To capture and analyze the contribution of news sections to conversions, the following steps should be taken:

Assign a custom eVar to capture the news section value: This allows for tracking which news sections users engage with.

Enable linear attribution to the eVar: Linear attribution ensures that all news sections engaged with during a session are credited proportionally for any conversions, providing a more balanced view of their contribution.

Expire the eVar after the visit: This ensures that the attribution is relevant to the current session and does not carry over to future sessions, maintaining accuracy in conversion analysis.

This approach provides a clear and comprehensive understanding of how different news sections contribute to conversions.

 Business Goal: Increase website account registrations by tracking relevant metrics.

 Key Dimensions and Metrics: Visitor Login Status, Form Name, Form Starts, Forms Submitted With Errors, Forms Submitted Successfully.

 Calculated Metrics:

Registration Form Completion Rate: Measures the percentage of forms started that are successfully completed, providing insights into form usability and effectiveness.

Return Visitation Rate: Tracks how often visitors return but is not directly related to registration form completion.

Registered Visitor Transaction Rate: Useful for post-registration analysis but not directly for increasing registrations.

Registered Visitor Form Start Rate: Redundant as registered visitors are less relevant for new registrations.

Unregistered Visitor Registration Form Start Rate: Tracks the engagement of unregistered visitors with the registration form, crucial for understanding initial interest.

 Explanation:

Registration Form Completion Rate: Essential for evaluating the effectiveness of the registration form.

Unregistered Visitor Registration Form Start Rate: Provides insights into how many new visitors are interested in registering, a direct indicator of potential registration increase.

 Verification: According to Adobe's Measurement Planning documentation, these calculated metrics are vital for tracking and improving form conversion rates (Adobe Measurement Planning Guide).

 Business Requirement: The company needs to report on internal search keywords and their contribution to revenue by creating thedigitalData.search.keyworddata object in the data layer.

 Recommended Syntax: The syntax must be correctly structured to allow Adobe Analytics to map the data object to eVar1 efficiently.

 Explanation:

Introduction: The requirement is to use a value in both a prop (for pathing reports) and an eVar (for persistence) while minimizing the size of the server call.

Explanation of Methods:

A. A VISTA rule to copy the prop value to the eVar:

VISTA (Visitor Identification, Segmentation & Transformation Architecture) rules are server-side rules that can copy values between variables. However, they are complex, incur additional costs, and do not reduce server call size.

Overview of the Issue: The hourly unique visitor report being 3 hours behind indicates a delay in data processing within Adobe Analytics.

Potential Causes: The delay could be due to several factors such as increased data volume, server performance issues, or unexpected traffic spikes.

Explanation:

Increased unique variable values: This could slow down processing, but it typically affects data collection rather than causing such a significant delay.

Increased number of users running reports: This might slow down the user interface and report generation, but not data processing itself.

New variables enabled for report suite: This usually affects the data collection stage and can cause delays but would not typically result in a consistent 3-hour lag.

An unexpected traffic spike: A sudden increase in traffic can overload data processing servers, causing delays in reporting as the system tries to catch up with the increased data volume.

Verification: According to Adobe's documentation, data processing delays are often caused by unexpected traffic spikes that increase the volume of data beyond typical processing capacity (Adobe Analytics Documentation).