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Adobe AD0-E207 Practice Test - Questions Answers
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Question 1
A new Adobe Analytics implementation recently went live on an ecommerce site.
An Analyst notices some problems during the analysis of the product details pages for Electronics products:
* The expected 'PDP' value does not appear in the Site Section dimension.
* No product views are associated with these pages.
* The expected product category of 'Electronics' is not populating eVarlO.
Which parameters should the Architect see in browser network requests when the solution operates correctly?
Explanation:
Business Requirement: Ensure correct tracking of product detail pages for Electronics products.
Expected Parameters:
events=prodView: Tracks the product view event.
ch=PDP: Sets the site section to 'PDP' (Product Detail Page).
eVar10=Electronics: Captures the product category.
events=prodView: Ensures product views are recorded.
ch=PDP: Ensures the site section is correctly identified as 'PDP'.
eVar10=Electronics: Ensures the product category is captured correctly.
Verification: According to Adobe Analytics documentation on event tracking and eVars, using these parameters ensures accurate and detailed tracking of product detail pages (Adobe Analytics Implementation Guide).
Question 2
A company is using Segment IQ to compare mobile and desktop traffic.
The metrics of interest to them are as follows:
* Page Views/Visitors
* Searches/Visitors
* Carts/Visitors
* Cart Additions/Visitors
* Revenue/Visitors
The differential score when comparing each metric with each segment is high at 0.9 or greater. What does this differential score indicate?
Explanation:
Segment IQ in Adobe Analytics is used to compare different segments of traffic to determine the statistical significance of differences between metrics. A high differential score of 0.9 or greater indicates a high statistical significance difference between the metrics of the two segments. This means that the differences observed in metrics like Page Views/Visitors, Searches/Visitors, Carts/Visitors, Cart Additions/Visitors, and Revenue/Visitors are not due to random chance but are significant and meaningful.
Question 3
One of the records in the data sources files does not have the same number of columns as the header record. What will be the outcome of this file upload?
Explanation:
When uploading data source files to Adobe Analytics, it is crucial that each record has the same number of columns as the header record. If one of the records does not match the number of columns, the entire file will not be processed due to the column mismatch. This ensures data integrity and consistency in the uploaded data.
Question 4
What are three uses of Processing Rules? (Choose three.)
Explanation:
Processing Rules in Adobe Analytics are versatile and can be used for various purposes:
Clean up misspelled site sections: Corrects misspelled values in data collection to ensure accurate reporting.
Populate campaign with a query string parameter: Extracts query string parameters and uses them to populate campaign variables.
Copy an eVar into a prop to see pathing: Allows copying values from an eVar to a prop to enable pathing analysis on that variable.
These rules help maintain data quality and flexibility in reporting.
Question 5
A product was viewed on two different pages and was added to the cart from one of the pages. Below are the product syntax used for each page.
Page 1:
s.products = ';prod123;1;100;;evar2=merch_category1';
Page 2:
s.products = ';prod123;1;100;;evar2=merch_category2';
If the product was checked out and purchased for $100, how might revenue be attributed to eVar2 if merchandising is enabled? (Choose two.)
Explanation:
When merchandising is enabled in Adobe Analytics, revenue attribution can vary based on the allocation method:
Linear Allocation: Distributes revenue equally across all instances of the product view and add-to-cart events. Thus, $100 would be attributed to both merch_category1 and merch_category2.
Most Recent Allocation: Attributes the revenue to the most recent instance of the variable. Thus, $100 would be attributed to merch_category2.
This approach ensures that revenue attribution accurately reflects user interactions with products.
Question 6
An Architect advises a site developer to embed the Adobe Launch script in the <head> and to place the data layer before the closing </body> tag of a web page.
During testing, an Adobe Analytics page view call fires successfully. Several Adobe variables are not defined in the call. The embedded Launch script and the data layer are implemented correctly per the Architect's specifications.
What should the Architect do to resolve the issue?
Explanation:
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 the <head> section of the webpage. This ensures that all data layer variables are available to the Launch script during its execution.
Question 7
While auditing the Adobe Analytics implementation, an Architect finds that the hourly unique visitor report is 3 hours behind.
What is causing this issue?
Explanation:
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.
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).
Question 8
While auditing an Adobe Analytics implementation, an Architect discovers that reports built using the Marketing Channel dimension show a large proportion of 'None'' visits.
Which two steps should be taken to diagnose the problem? (Choose two.)
Explanation:
Overview of the Issue: The 'None' value in Marketing Channel reports indicates visits that are not attributed to any of the defined marketing channels.
Potential Diagnostic Steps:
Break down 'None' Marketing Channel by Tracking Code: This can provide insights but does not directly address the underlying configuration issues.
Review Marketing Channel Processing Rules: Ensures that the rules are correctly defined and applied. Incorrect or missing rules can result in visits being categorized as 'None'.
Review Marketing Channel Data Feeds: Useful for data validation but not for configuration troubleshooting.
Check that Internal URL Filters are configured correctly: Ensures that internal traffic is filtered out and does not interfere with marketing channel attribution.
Review Marketing Channel Data Connector settings: Relevant for integrations but not for basic processing rule configurations.
Explanation:
Review Marketing Channel Processing Rules: Essential to verify that all necessary rules are correctly set up to attribute visits to the correct channels.
Check that Internal URL Filters are configured correctly: Ensures that visits from internal sources do not affect marketing channel data, preventing incorrect attribution to 'None'.
Verification: According to Adobe Analytics Implementation documentation, reviewing processing rules and internal URL filters is crucial for accurate channel attribution (Adobe Analytics Implementation Guide).
Question 9
A company has a secure website that requires visitors to log in prior to accessing any content beyond the homepage. A visitor can log out manually, or their website will automatically log them out at the end of their browser session. The visitor must log back in to continue browsing.
The company has a business requirement to track the login status of their visitors.
Which three login statuses should the Architect recommend capturing? (Choose three.)
Explanation:
Business Requirement: Track the login status of visitors on a secure website.
Potential Login Statuses:
'Unregistered / Logged Out': Tracks users who have not registered or have logged out.
'Anonymous': Generally used for visitors who browse without logging in, but does not apply in a secure login-required context.
'Registered / Logged Out': Important for tracking users who have accounts but are currently logged out.
'Registered / Logged In': Essential for tracking active, logged-in users.
'Incomplete Registration': Useful for identifying users who started but did not complete the registration process.
'Unregistered / Logged In': Typically not applicable as unregistered users cannot be logged in.
Explanation:
'Unregistered / Logged Out': Helps in understanding the behavior of users who visit but have not completed registration or logged out.
'Registered / Logged In': Critical for identifying and analyzing the behavior of active users.
'Incomplete Registration': Helps in identifying and addressing barriers in the registration process.
Verification: According to Adobe Analytics best practices, capturing various login statuses helps in comprehensive user behavior analysis (Adobe Analytics User Guide).
Question 10
A company wants to market to more visitors via email, SMS, and print mail. They obtain contact details from visitors through their website account registration form. Their immediate business goal is to increase website account registrations.
The Architect adds the following dimensions and metrics to the Measurement Plan:
eVar for Visitor Login Status
eVar for Form Name
Event for Form Starts
Event for Forms Submitted With Errors
Event for Forms Submitted Successfully
Which two calculated metrics should the Architect add to the Measurement Plan? (Choose two.)
Explanation:
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).
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