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While auditing the Adobe Analytics implementation, an Architect finds that the hourly unique visitor report is 3 hours behind.
What is causing this issue?
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).
A company develops a new website using a Single Page Application framework. A data layer variable called dataLayer.url is updated with the correct URL when each state change happens.
Which variable should the Architect map to dataLayer.url?
For Single Page Applications (SPAs), it is important to capture the URL changes correctly. The s.pageURL variable should be mapped to dataLayer.url to 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.
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?
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.
A coworker creates the calculated metric named "Orders / Visits" in Adobe Analytics to determine the percentage of visits that purchased something on the company's website.
The coworker includes the calculated metric in the Products report. The coworker notices that the sum of the orders for the products is greater than the total orders shown in the report.
What should the Architect do?
The issue arises because the calculated metric 'Orders / Visits' is not aggregating correctly at the product level in the Products report. To resolve this, the Architect should create a calculated metric that uses the Total versions of the Orders and Visits metrics. This ensures that the calculation is performed on the overall totals rather than individual line items, providing an accurate percentage of visits that resulted in purchases.
Which two are standard metrics? (Choose two.)
Standard metrics in Adobe Analytics are those that come pre-configured within the platform and are commonly used across various types of implementations. These include:
Purchase: Tracks the completion of a purchase transaction.
Orders: Records the number of orders placed.
These metrics are essential for eCommerce tracking and are part of the standard set of metrics available in Adobe Analytics.
