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AD0-E602 Adobe Practice Test Questions and Exam Dumps

Question 1

A Real-Time CDP Business Practitioner has a client who wants to target audiences across Facebook. To send segments to this social destination, what are the first two steps they should take? (Choose two.)

A. Check Facebook account prerequisites
B. Confirm use cases for journey optimization
C. Connect to the destination
D. Export segment to destination

Correct answer: A, C

Explanation:
When working with Adobe Real-Time Customer Data Platform (Real-Time CDP), integrating with social destinations like Facebook requires a structured approach. This ensures that all technical and business requirements are met before audience segments are activated and used in marketing campaigns. The process of targeting Facebook begins well before any segments are exported—it starts with preparation and establishing a connection to the destination.

Let’s look closely at each option:

Check Facebook account prerequisites (Option A):
This is a necessary first step. Before you can activate segments in Facebook through Real-Time CDP, you must confirm that the Facebook account has the appropriate setup. This includes administrative permissions on the Facebook Business Manager account, access to the Facebook Ad account, and ownership or permission for the Facebook Page. Additionally, you may need to ensure the account is linked to a valid data-sharing agreement that complies with privacy regulations. Without confirming these prerequisites, the integration cannot proceed, so this step is foundational.

Connect to the destination (Option C):
After verifying that the account is properly set up, the next step is to create the actual connection between Real-Time CDP and Facebook. This is done through the Destinations workspace, where you authenticate the Facebook account and define how data will flow from CDP to Facebook. This connection includes choosing the appropriate destination type (e.g., Facebook Custom Audiences), setting up the schema mapping, and ensuring that data privacy settings are correctly applied. Without this connection, no segments can be exported.

Confirm use cases for journey optimization (Option B):
While understanding your use cases is always valuable, it is more of a planning or strategy phase rather than a technical prerequisite for activating a Facebook destination. Use cases are important when designing the overall marketing strategy, but they are not part of the required steps to connect and begin exporting data to Facebook.

Export segment to destination (Option D):
This step comes much later in the process. After prerequisites are confirmed and the connection is successfully established, segments can then be exported. Exporting segments is a part of audience activation and cannot be done until the destination has been configured and validated.

To summarize, the correct first two steps to enable Real-Time CDP to work with Facebook are to check the Facebook account prerequisites to ensure eligibility and permissions are in place, and then connect to the Facebook destination using the Destinations workspace in CDP. Only after these steps are complete can segments be mapped and exported for use in Facebook campaigns.

Question 2

An Adobe Real-Time CDP Business Practitioner wants to define a segment of users who made a purchase within the last 90 days and then made another purchase 5 days afterward. 

What type of segmentation should be applied?

A. Frequency Segmentation
B. Recency Segmentation
C. Sequential Segmentation

Answer: C

Explanation:

In Adobe Real-Time Customer Data Platform (CDP), creating meaningful audience segments based on behavior and timing is essential for delivering personalized marketing campaigns. The scenario describes a need to identify users based on the order and timing of two purchase events: the first purchase within the last 90 days, and a second purchase occurring specifically five days later.

This specific sequence of actions — where one event must occur after another within a defined time gap — is a textbook case for using sequential segmentation, which is option C.

Sequential segmentation allows marketers to build audience definitions based on the chronological order of events. It enables the practitioner to define “event A followed by event B” patterns, often with time constraints between the two events. In this case, the segment must capture users who purchased once, and then purchased again exactly five days later — a sequential pattern that directly reflects user behavior over time.

Option A, Frequency Segmentation, is used when the focus is on how often an event occurs. For example, if the requirement was to segment users who purchased twice or more in the last 90 days, regardless of when those purchases occurred, frequency segmentation would be suitable. However, frequency segmentation does not take into account the order or timing between events, which is critical in this case.

Option B, Recency Segmentation, focuses on how recently an event occurred — for instance, users who made a purchase in the last 7 or 30 days. While part of the requirement involves a 90-day window, recency alone doesn’t capture the relationship between the two purchase events or their sequence. It only looks at when the most recent event occurred, without understanding event sequences.

The key reason sequential segmentation is correct lies in its ability to model behavior over time. It is particularly useful for journey-based insights, like abandoned cart sequences, re-engagement flows, or — as in this case — follow-up purchases. Adobe Real-Time CDP allows business practitioners to define these patterns using sequential logic, which can even include time constraints like “within 5 days,” making it ideal for the situation described.

Therefore, since the segmentation must reflect the chronological order and time gap between two specific user actions, the most appropriate and accurate method is sequential segmentation, making C the correct answer.

Question 3

A Real-time CDP Business Practitioner needs to enable segments to be evaluated on a schedule for their organization.

What is the correct way to activate this scheduled evaluation?

A. Add all segments to a destination
B. Create a scheduled segment
C. Add all segments to schedule

Answer: C

Explanation:
In Adobe Real-time Customer Data Platform (Real-time CDP), segment evaluation is an important function that allows practitioners to determine which individuals meet certain audience conditions based on real-time data. While segments can be evaluated in real time, there are also cases where scheduled evaluations are more appropriate—such as when working with large volumes of data, managing batch destinations, or ensuring performance optimization.

The correct approach to enable this is to add segments to a schedule, which allows them to be evaluated at specific times rather than continuously in real time. This process is typically managed through the CDP user interface where you can choose to assign certain segments to a scheduled evaluation job. These jobs may run daily, weekly, or at other intervals as defined by the organization’s needs and configurations.

Option A, "Add all segments to a destination," is related to activating or exporting segment data to third-party platforms or internal systems (such as marketing tools, CRMs, or advertising networks). While this is part of the data activation pipeline, it is not the mechanism for triggering scheduled evaluations. It simply defines where the evaluated segment results go, not when or how they are computed.

Option B, "Create a scheduled segment," might sound plausible but is not a recognized function in Adobe Real-time CDP. Segments are generally created using rules or conditions applied to the profile data, and then they are either evaluated in real time or through scheduled processes. The distinction is not about the type of segment (scheduled or not) but about the evaluation method applied to it.

Only Option C, "Add all segments to schedule," directly addresses the task of enabling a scheduled evaluation. This ensures the segments are processed based on the timeline configured by the organization and are updated consistently without relying on real-time triggers.

For Real-time CDP practitioners, using scheduled evaluations can be particularly beneficial in use cases where real-time responsiveness is not necessary or where system efficiency and scalability are more critical. It also helps ensure that large data workloads are processed during off-peak hours or in line with organizational data refresh cycles.

Therefore, the most accurate and effective method for enabling scheduled segment evaluation is to add all segments to the schedule, making C the correct answer.

Question 4

An Adobe Real-Time CDP Business Practitioner wants to forward raw website events to an analytics destination in real-time rather than sending a segment using a cookie identifier. Which type of destination should the practitioner use?

A. Streaming Profile Export Destination
B. Connection
C. Extension

Correct answer: A

Explanation:
Adobe Real-Time Customer Data Platform (CDP) offers several types of destination configurations depending on the kind of data you want to share and how you intend to use it. In this question, the practitioner’s goal is to forward raw website events in real time to an analytics destination, rather than relying on user segmentation or cookie-based identifiers. This specific use case requires a destination type capable of sending data immediately as it is collected and processed.

Let’s break down the options:

Streaming Profile Export Destination (Option A):
This is the correct option. Streaming destinations are designed for real-time data export. Specifically, a Streaming Profile Export Destination allows Adobe Real-Time CDP to send profile fragments, including event-level data, as soon as they are ingested and enriched in the system. These destinations are particularly well-suited for real-time use cases such as behavioral analytics, fraud detection, or machine learning pipelines that rely on the most up-to-date customer behavior. The fact that the practitioner wants to forward raw website events (not aggregated segments) aligns perfectly with the capabilities of a streaming destination. These do not depend on segment qualification, cookies, or scheduled batch processing—making them ideal for instant data forwarding.

Connection (Option B):
The term “Connection” in Adobe Experience Platform (AEP) generally refers to the initial configuration of a data source or destination connection—essentially the setup phase where authentication and access are established. A connection itself is not a destination type and does not define how data is processed or streamed. While necessary in the destination setup process, a connection alone does not fulfill the practitioner’s requirement of sending raw events in real time.

Extension (Option C):
Extensions in Adobe systems, particularly within Adobe Experience Platform Tags (formerly Launch), are modular components that allow developers and marketers to integrate SDKs and tools for data collection or transformation at the edge. While extensions can help configure how data is collected or tagged on websites, they are not designed to send raw events directly to analytics systems from Real-Time CDP. In this context, an extension is not a destination type and would not serve the purpose of forwarding event-level data to an external analytics destination in real time.

In summary, the practitioner needs a way to transmit data immediately as it is generated. Only the Streaming Profile Export Destination supports this requirement by sending raw events as they flow through the platform. It is especially useful in scenarios where downstream systems need to act on customer behavior in real time, such as personalized product recommendations, live dashboards, or behavioral scoring models. Therefore, this destination type is the appropriate choice for the described use case.

Question 5

A Real-Time Business Practitioner needs to export a customer's email address and related attributes to an SFTP connection. What is the recommended destination type for this use case?

A. Streaming profile export
B. File based destinations
C. Streaming segment export

Answer: B

Explanation:

In Adobe Real-Time Customer Data Platform (CDP), exporting data to external systems is a common task, and the type of destination selected depends heavily on the nature of the export and the requirements of the receiving system. In this scenario, the practitioner needs to export email addresses and associated attributes to an SFTP server, which clearly indicates a need for file-based output.

The correct and recommended destination type for exporting data to an SFTP location is file based destinations, which is option B.

File-based destinations allow for the batch export of user profile data or segment membership to file formats like CSV or JSON, which can then be delivered to a location such as an SFTP server. This method is ideal when the receiving system is not equipped to handle real-time streaming data and instead expects to process files on a scheduled or recurring basis. In most enterprise data workflows, SFTP is used to securely exchange files between systems, often at regular intervals (e.g., nightly exports of customer lists).

Option A, streaming profile export, is used when real-time updates of individual profiles are required — for example, if a system needs to receive instantaneous updates every time a user’s profile changes. However, streaming profile exports do not deliver data in file format and are not suitable for delivery to SFTP, as SFTP is designed for batch file transfers, not real-time data ingestion. This option is more appropriate for APIs, webhooks, or cloud applications that can handle event-based streaming.

Option C, streaming segment export, is similar to streaming profile export but operates at the segment level. It continuously sends updates when users enter or exit a segment in real time. Like option A, this method is not appropriate for SFTP delivery, as it does not support file-based output and is meant for integration with systems that can consume real-time data flows, such as advertising platforms or customer engagement tools.

Given the need to send customer email addresses and attributes as a file to an SFTP destination, only file based destinations fulfill both the data format and delivery mechanism requirements. Adobe Real-Time CDP provides options for mapping attributes, setting file formats, and scheduling exports when using file-based destinations, making this the optimal choice for batch file transfers like the one described in the question.

Therefore, the correct answer is B.

Question 6

An Adobe Real-time CDP customer has low match rates when sharing cookie-based retargeting audiences to Google DV360, mainly due to users accessing their site via Safari. However, 70% of these users log in using their email addresses. 

What should a business practitioner recommend to improve the audience reach on Google?

A. Only send users who have visited the website using another browser (e.g., Chrome)
B. Share the segment using the Google Customer Match connection using hashed email addresses as the target identity
C. Share the segment using the Google Customer Match connection using ECID as the target identity

Answer: B

Explanation:
The main issue in this scenario is that the customer is experiencing a low match rate when attempting to perform cookie-based retargeting via Google DV360. This issue is largely due to the increasing restrictions on third-party cookies, particularly by browsers like Safari, which aggressively block cross-site tracking technologies.

Safari uses Intelligent Tracking Prevention (ITP), which limits the lifespan and accessibility of cookies, particularly third-party ones. This means that even if users are tagged through traditional cookie-based tracking on the website, that data often can’t be shared reliably with advertising platforms like Google DV360, leading to poor match rates and a reduction in the size and accuracy of retargeting audiences.

However, the business has a key advantage here: 70% of users log in with an email address. This opens up the opportunity to use deterministic identifiers rather than relying on probabilistic identifiers like cookies. Adobe Real-time CDP allows you to activate audiences through various identity namespaces, including hashed email addresses, which are highly effective for integration with platforms that support Customer Match — a feature in Google Ads and DV360 that allows advertisers to reach users based on their email addresses.

Option B is the most effective and strategic solution. By sharing segments using Google Customer Match with hashed email addresses, the business leverages a stable and persistent identity that is unaffected by browser-level cookie restrictions. This approach also aligns with industry best practices and privacy standards because Adobe hashes the email addresses before sharing them with Google, maintaining user anonymity.

Option A, sending users only from browsers like Chrome, would drastically limit audience reach and create bias. It also fails to solve the root problem of low match rates and doesn’t leverage the available email login data. This workaround would also exclude a significant portion of users who continue to use Safari and other privacy-focused browsers.

Option C, using ECID (Experience Cloud ID), is a valid identity within Adobe’s ecosystem but is not recognized by Google’s advertising platforms for identity resolution. ECID is helpful for internal Adobe integrations but not for activating audiences in external advertising platforms like Google DV360.

Therefore, to improve reach and match rates with Google, especially for users who log in with an email address, the recommended approach is to share the segment using hashed email addresses via Google Customer Match, making B the correct answer.

Question 7

An entertainment company and Adobe Real-Time CDP customer wants to message subscribers on the anniversary of their subscription date. The goal is to send an upsell offer to increase their subscription benefits by creating a segment and activating to their email service provider. 

Which segmentation event recency type should be used to evaluate new users each day into this segment once users reach 365 days since the first subscription?

A. This year
B. Rolling Range
C. Custom date
D. Within (+/-)

Correct answer: D

Explanation:
In Adobe Real-Time Customer Data Platform (Real-Time CDP), segmentation allows marketers to build dynamic audience segments based on customer behavior, profile attributes, and key events. The scenario in this question focuses on sending an upsell offer to subscribers exactly on the one-year anniversary of their original subscription. This means the segment must evaluate daily which users have reached exactly 365 days since the date they subscribed.

To do this accurately, the event recency type used in the segmentation logic must allow for precision around date matching—specifically targeting the subscription date anniversary relative to the current day.

Let’s examine the choices:

This year (Option A):
This type is generally used to evaluate events that occurred at any point in the current calendar year. It’s too broad for this use case because it would include all users who subscribed at any time from January 1 to the current date. It does not allow for precision on specific days like a subscription anniversary. Therefore, this would not satisfy the need to identify users who subscribed exactly 365 days ago.

Rolling Range (Option B):
Rolling ranges are used to evaluate whether an event occurred within a dynamic window—for example, within the last 30 days. While more dynamic than “This year,” it is still a range and would pull in users who subscribed in a larger span of time (e.g., 360–370 days ago), which would not provide the needed granularity for an exact 1-year anniversary. Thus, it may include users who subscribed before or after the exact 365-day mark.

Custom date (Option C):
Custom date allows a segment to be built around a fixed date, like December 1st, 2023. This works for static campaigns, but not for ongoing anniversary messages that need to be evaluated dynamically every day based on each user’s own subscription date. Because this use case needs to re-evaluate daily and is tied to each user’s data (not a single fixed date), this option is not appropriate.

Within (+/-) (Option D):
This is the correct choice. The “Within (+/-)” recency type allows you to evaluate events that occurred exactly X number of days before or after the current date. In this use case, the business wants to check if a user’s first subscription event happened 365 days ago, so the segmentation logic should include something like “first subscription occurred within 0 days of 365 days ago.” This means the segment will dynamically include new users every day who are celebrating their subscription anniversary, ensuring timely and personalized targeting.

This approach gives marketers the precision needed to deliver anniversary-based campaigns, ensuring messages are relevant and sent on the correct day.

In summary, the Within (+/-) recency type provides the flexibility and accuracy required to identify users on their subscription anniversary. It enables daily segment evaluations and ensures users are activated exactly on the day they reach one year since subscribing, making it the optimal choice for this marketing objective.

Question 8

A Real-Time CDP Business Practitioner wants to target audiences on LinkedIn. Which three identity types are required to enable this kind of targeting?

A. Hashed emails, phone number, GAID
B. Hashed emails, IDFA, GAID
C. IDFA, GAID, MID

Answer: B

Explanation:

When targeting audiences on LinkedIn using Adobe Real-Time Customer Data Platform (CDP), it's important to align the identity data used in your segments with the types of identifiers LinkedIn accepts for audience matching. The goal is to maximize match rates and ensure that LinkedIn can recognize and map your audience data to their user profiles accurately.

The three key identifiers that LinkedIn accepts for custom audience targeting are:

1. Hashed email addresses

2. IDFA (Identifier for Advertisers)

3. GAID (Google Advertising ID)

This combination is exactly what is presented in option B, making it the correct answer.

Let’s break down each component:

• Hashed emails: This is one of the most common and widely supported identity formats for audience matching across advertising platforms, including LinkedIn. Adobe Real-Time CDP hashes email addresses using SHA-256 before sending them, which complies with LinkedIn’s data security requirements. It is an effective way to identify users who registered for LinkedIn with the same email address.

• IDFA (Identifier for Advertisers): This is a unique identifier for Apple devices used primarily for advertising. It helps LinkedIn connect mobile app activity back to user profiles, allowing for retargeting or lookalike audience building on iOS devices.

• GAID (Google Advertising ID): This is the Android equivalent of IDFA. It works the same way by enabling app-based targeting on Android devices and is essential for maximizing mobile reach across the user base.

Now, let’s explore why options A and C are incorrect.

Option A includes hashed emails, which is good, but it also includes phone numbers, which LinkedIn does not support as a standalone identity for audience matching. Furthermore, GAID is valid, but without IDFA, the reach is limited to Android users, and you miss out on the iOS audience.

Option C includes IDFA and GAID, which are both valid mobile identifiers, but it replaces hashed email with MID (Marketing ID), which is a platform-specific internal identifier that LinkedIn does not recognize. Without hashed emails, this combination would significantly reduce match rates and coverage on LinkedIn.

In summary, LinkedIn supports hashed emails and mobile advertising identifiers (IDFA and GAID) for building matched audiences. These identifiers help advertisers reach both desktop and mobile users with a broad and accurate reach. Adobe Real-Time CDP allows practitioners to map and hash these identities appropriately before activating them in LinkedIn.

Therefore, the best and only correct answer for targeting LinkedIn profiles is B.

Question 9

A business practitioner using Adobe Real-Time CDP receives an urgent request from marketing to create segments for a campaign launching the next day. These segments rely mostly on batch datasets, which are not scheduled to be evaluated for another 24 hours. 

What is the best way to speed up segment evaluation in this case?

A. Evaluate the segment on-demand using the Segment Service API
B. Evaluate the segment on-demand using the Segment Query Service
C. Evaluate the segment on-demand using the Experience Cloud Edge

Answer: A

Explanation:
When a segment in Adobe Real-Time Customer Data Platform (CDP) is based on batch data, it is normally evaluated according to a scheduled cadence — typically once every 24 hours. However, in time-sensitive situations like this one, waiting for the next scheduled evaluation isn't an option. Fortunately, Adobe provides a way to manually trigger segment evaluations using programmatic methods.

The Segment Service API is specifically designed for scenarios like this. It allows practitioners and developers to trigger on-demand evaluation of audience segments without having to wait for the next scheduled job. By using this API, the practitioner can send a request to evaluate a specific segment (or set of segments) immediately. This capability is extremely useful when campaign deadlines are tight or when urgent business needs arise.

Option A is correct because the Segment Service API enables direct and immediate interaction with Adobe's segmentation engine, allowing batch segments to be evaluated ahead of schedule. It offers the flexibility to bypass the default 24-hour batch processing window and ensure that the segment data is up-to-date for activation or downstream usage — such as in a campaign going live the next day.

Option B, the Segment Query Service, refers to the underlying infrastructure used for running queries to evaluate segments. While it's involved in the segment evaluation process, it is not something practitioners directly interact with to trigger on-demand evaluations. It doesn’t provide a method to immediately evaluate a segment; instead, it supports the execution of queries behind the scenes as scheduled or triggered by other services.

Option C, the Experience Cloud Edge, is primarily used for real-time data ingestion and segmentation — for example, for online personalization or for triggering experiences based on streaming data. It is ideal for use cases involving immediate behavior on the web (like clicks or views) but not suitable for batch data-based segments. Since the segments in this scenario are based on batch datasets, Experience Cloud Edge would not apply here.

In summary, the practitioner should use the Segment Service API to immediately evaluate the new segments and make them available for the marketing campaign in a timely manner. This direct method is supported and recommended for urgent operational needs involving batch data. For this reason, the correct answer is A.

Question 10

An Adobe Real-Time CDP Business Practitioner wants to create a batch segment of users for a financial services client who opened an account in the last rolling 30 days. The analytics team needs an updated segment count prior to a campaign launch. 

What would the practitioner need to do to achieve this task?

A. Re-create the segment
B. Streaming segment
C. Segment job

Correct answer: C

Explanation:
In Adobe Real-Time Customer Data Platform (CDP), segmentation is a key capability that allows users to define dynamic audiences based on customer behavior, profile attributes, and event data. There are two primary types of segments: batch segments and streaming segments. Batch segments are evaluated on a schedule or manually, while streaming segments are updated in near real-time as data is ingested.

In this scenario, the practitioner is working with a batch segment targeting users who opened an account within the last rolling 30 days. This segment needs to be refreshed or updated before a campaign launch so that the analytics team gets the most current count of users who match this condition.

Let’s review the options:

Re-create the segment (Option A):
This approach is unnecessary and inefficient. Batch segments in Adobe Real-Time CDP are designed to be reusable and dynamic when configured with rolling date ranges like “last 30 days.” There is no benefit in deleting and rebuilding the segment each time an updated count is needed. Recreating a segment would be time-consuming and does not serve the purpose of updating existing data.

Streaming segment (Option B):
Streaming segments are continuously updated as data flows into the platform, making them ideal for use cases requiring immediate responsiveness to user behavior, such as abandonment triggers or real-time personalization. However, the practitioner is specifically using a batch segment, which means this is not the correct method. Also, streaming segmentation doesn't apply to calculated rolling windows in batch evaluation contexts like "last 30 days." Therefore, this option doesn't match the requirements.

Segment job (Option C):
This is the correct answer. A segment job is a manual or scheduled process that re-evaluates a batch segment against the most recent profile data. When you run a segment job, the platform calculates the latest list of users who qualify under the segment’s logic—in this case, those who have opened an account within the last 30 days. By running the job right before a campaign launch, the practitioner ensures the analytics team receives the most accurate and updated count of qualified users. This method is both efficient and aligns with the standard capabilities of batch segmentation workflows.

Running a segment job also updates the segment's audience in downstream destinations, if configured for activation, and provides clear insights into how many profiles currently meet the criteria.

In summary, to fulfill the goal of generating an updated count for a batch segment based on a rolling 30-day condition, the practitioner should use a segment job. It enables timely updates to the segment definition without rebuilding the segment or switching to a streaming model, making it the most appropriate and practical solution for this use case.