AD0-E207 Exam Dumps - Adobe Analytics Architect Master

To correctly capture the specified data in Adobe Analytics, thes.productssyntax needs to be properly formatted to include the product SKU, quantity, price, and custom variables and events. The correct format is:

SKU:atp29

Quantity:1

Price:700.00

Event1:70.00(discount)

eVar1:SUMMER10(coupon code)

eVar2:Amaze Touch Pro(product name)

The correct syntax is:

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s.products =";atp29;1 ;700.00;eventl =70.00;eVar1 =SUMMER10|eVar2=Amaze Touch Pro";

This format ensures that all the specified information is correctly captured and recorded in Adobe Analytics.

ï‚· Business Requirement: The company wants to track shares of blog posts to social networks and report on the total number of shares and the specific networks used.

ï‚· Variables and Metrics Needed:

eVar for "Blog Post Title": Captures the title of the blog post being shared.

eVar for "Blog Share Social Network": Captures the social network to which the post is shared.

Event for "Blog Shares": Captures the number of times a post is shared.

ï‚· Explanation:

eVar for "Blog Post Title": This variable allows reporting on the specific blog posts being shared.

eVar for "Blog Share Social Network": This variable tracks which social networks the posts are shared to, enabling detailed reporting.

Event for "Blog Shares": This metric captures the total number of shares, providing a quantifiable measure of sharing activity.

ï‚· Verification: According to Adobe Analytics best practices for tracking social sharing, using specific eVars for post titles and social networks along with an event for total shares ensures comprehensive reporting (Adobe Analytics Implementation Guide).

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.

To create a segment that captures visitors who engaged with a specific link type and purchased a specific product within the same visit, the following structure should be used:

Visitor container: Ensures the segment applies to the entire visitor’s history.

Visit container (product = "xyz"): Ensures that the product "xyz" was purchased during the visit.

**Hit (prop17 = "Checkout"

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When different parts of a website use different methods to implement Adobe Analytics (hard-coded vs. tag management system), it can lead to inconsistencies in data reporting. This is because the two methods might not be perfectly synchronized in terms of how they handle data collection, processing rules, or variables. Despite using the same AppMeasurement version and operating on the same domain, differences in the deployment method can cause issues such as discrepancies in variable persistence, tracking cookies, or firing sequence of the analytics calls. These inconsistencies can make it difficult to maintain and accurately report on the collected data.

ï‚· Business Requirement: Enable a two-way integration between Adobe Audience Manager (AAM) and Adobe Analytics.

ï‚· Configuration Needs:

List Variable: Used to bring in multiple AAM segments into Adobe Analytics.

Server-Side Forwarding: Ensures that data flows between Adobe Analytics and AAM seamlessly.

ï‚· Explanation:

List Variable: Suitable for handling multiple segments and their associations with other metrics within Adobe Analytics.

Server-Side Forwarding: This method ensures real-time data exchange between the platforms, enabling both to use each other's data for enhanced segmentation and reporting.

ï‚· Verification: According to Adobe's documentation on integrating Audience Manager and Analytics, using list variables with server-side forwarding is the recommended approach for robust two-way integration (Adobe Audience Manager and Analytics Integration Guide).

ï‚· 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.

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

ï‚· Explanation:

Business Requirement: Analyze all internal search terms, including those falling into the "(low traffic)" category.

Extraction Methods:

Data Warehouse: Provides comprehensive data extraction capabilities, allowing for the retrieval of detailed and granular data that might not be fully visible in standard reports.

Data Feeds: Offers raw data extraction capabilities, enabling the capture of all search terms without the aggregation and sampling that can occur in standard reports.

Explanation:

Data Warehouse: Can export detailed data, bypassing the "(low traffic)" limitation by accessing the raw, unsampled data.

Data Feeds: Provides a way to extract raw data directly from Adobe Analytics, ensuring that all search terms, including those with low traffic, are included.

Verification: According to Adobe Analytics documentation, using Data Warehouse and Data Feeds are recommended methods for extracting comprehensive datasets, including detailed search terms (Adobe Analytics Data Warehouse Guide, Data Feeds Documentation).

ï‚· Business Requirement: Report on Facebook and Twitter activity separately in Marketing Channel reports.

ï‚· Configuration Steps:

Identify Social Media Traffic: Create a rule to identify all social media traffic.

Split Facebook and Twitter Traffic: Create rules to distinguish traffic from Facebook and Twitter based on tracking codes.

ï‚· Explanation:

First Rule for Social Media Traffic: This rule captures all social media traffic under a single category.

Second Rule to Split Traffic: Additional rules to distinguish between Facebook and Twitter based on specific tracking codes ("socialjb" for Facebook and "sociaLtw" for Twitter).

ï‚· Verification: According to Adobe's documentation on Marketing Channel Processing, using multiple rules to identify and then split traffic ensures detailed and accurate reporting (Adobe Analytics Marketing Channel Processing Rules Guide).

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.

ï‚· Business Requirement: The company wants to track successful and unsuccessful form submissions and identify the types of forms being submitted.

ï‚· Variables and Metrics Needed:

Event for "Forms Submitted with Errors": Captures instances where forms are submitted but contain errors.

eVar for "Form Type": Identifies the type of form being submitted, which is crucial for analyzing different form performance.

Event for "Forms Submitted Successfully": Captures instances where forms are successfully submitted.

ï‚· Explanation:

Event for "Forms Submitted with Errors": Essential for understanding and improving form completion processes by identifying where users encounter issues.

eVar for "Form Type": Provides detailed breakdowns of form performance by type, aiding in targeted improvements.

Event for "Forms Submitted Successfully": Measures the completion rate of forms, which is critical for business metrics.

ï‚· Verification: According to Adobe Analytics best practices, using specific events for tracking successful and error submissions alongside eVars for categorization provides comprehensive insights (Adobe Analytics Implementation Guide).

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.

Explanation:

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).