Adobe AD0-E602 Practice Test - Questions Answers, Page 4

According to the Adobe documentation2, streaming ingestion is the type of ingestion that allows data to appear in real time in customer profiles. Streaming ingestion enables data to be ingested continuously from sources such as mobile applications, websites, or IoT devices. Streaming ingestion also supports identity stitching and profile merge policies to create unified customer profiles.

Reference: 2 https://experienceleague.adobe.com/docs/experienceplatform/ingestion/home.html?lang=en#streaming-ingestion

According to the Adobe documentation3, batch ingestion is the type of ingestion that supports automatic authentication. Batch ingestion enables data to be ingested periodically from sources such as databases, cloud storage, or CRM systems. Batch ingestion also supports authentication methods such as OAuth 2.0, API key, or username and password.

Reference: 3 https://experienceleague.adobe.com/docs/experienceplatform/ingestion/home.html?lang=en#batch-ingestion

According to the Adobe documentation, subscribing to source flow run failure alerts is the method to receive alerts when an error occurs while ingesting data from a source connection. Source flow run failure alerts are notifications that are sent to the user's email address when a source connection run fails due to an error. The user can subscribe to these alerts by selecting the option in the source connection settings.

Reference: https://experienceleague.adobe.com/docs/experienceplatform/sources/ui/monitor.html?lang=en#subscribing-to-source-flow-run-failure-alerts

A. The segment logic should include an OR statement instead of an AND.

B. The recency needs to be expanded to include 90 days of activity.

C. Women are less interested in the brand's camping supplies than men.

D. The majority of browsers on the website are not authenticated.

Answer: D

Explanation:

The most likely explanation for the dramatic drop in segment size is that the majority of browsers on the website are not authenticated. This means that they do not have a loyalty account or a unique identifier that can be matched with the gender attribute from the loyalty system. Therefore, they are excluded from the segment that ANDs the gender attribute with the browsing behavior. To increase the segment size, the retailer could use other methods to identify or infer the gender of anonymous browsers, such as cookies, device IDs, or third-party data.

Two metrics that can be tracked to calculate the value of RTCDP for this customer are time-to-market and CPA. Time-to-market measures how quickly the customer can reach their target audience with relevant offers and messages using RTCDP's near real-time segmentation and activation capabilities. CPA (cost per acquisition) measures how much it costs the customer to acquire a new customer or retain an existing one using RTCDP's data-driven optimization and personalization features.

Two attributes that could be used to understand high-value prospects are household income and credit score. Household income indicates the financial capacity and spending power of a prospect, which could affect their interest in credit cards with crypto currency bonus offers. Credit score indicates the creditworthiness and risk level of a prospect, which could affect their eligibility and approval for credit cards with crypto currency bonus offers.

Two steps that the retailer should take to analyze their shoe segment in this way using RTCDP are to divide the size of each age group segment into the total sum of athletic shoe purchaser events to get percentage splits for analysis and to create multiple segments which AND each age group attribute with athletic shoe purchaser events. The first step allows the retailer to calculate the proportion of each age group among the shoe buyers, while the second step allows the retailer to create segments that include only the shoe buyers who belong to a specific age group.

The explanation for why the segment size which includes both forms is 12,000 is that 3,000 people filled out both form 1 and form 2. This can be derived from the following formula:

Segment size of form 1 OR form 2 = Segment size of form 1 + Segment size of form 2 - Segment size of form 1 AND form 2 Plugging in the given numbers, we get:

12,000 = 5,000 + 10,000 - Segment size of form 1 AND form 2

Solving for Segment size of form 1 AND form 2, we get:

Segment size of form 1 AND form 2 = 3,000

According to the Adobe documentation, Query Services is the best platform service to analyze the data to support different use cases. Query Services enables users to query data stored in the Data Lake using SQL or Spark SQL. Query Services also supports various data formats, such as Parquet, CSV, JSON, or Avro. Query Services can be used for various use cases, such as data exploration, data validation, data transformation, data visualization, or data export. Reference:

https://experienceleague.adobe.com/docs/experience-platform/query/home.html?lang=en#queryservice-overview