Explanation:

Here’s why:

They want a periodic (recurring) newsletter.

Audience is a specific Data Extension that is regularly updated (subs can be added/removed multiple times).

They care about speed of delivery.

A Send Activity in Automation Studio is:

- Designed for batch marketing sends.
- Very fast and efficient at sending to all records currently in a DE at send time.
- Easy to schedule (e.g., weekly, monthly) via a Scheduled Automation.
- Automatically respects the current state of the DE each time it runs (so new subs are included, removed ones are excluded).

That matches the requirements perfectly.

Why the others are less suitable

A. Journey that allows re-entry after exiting
Journeys are better for event-driven or multi-step flows, not simple recurring newsletters.
Re-entry is about how often a contact can re-enter a journey, not about speeding up batch newsletters.

B. Scheduled Automation utilizing Triggered Send Emails
Triggered Sends are best for real-time, one-to-one messages (e.g., order confirmations), not bulk newsletters.
Using them for batch sends is less efficient than a simple Send Activity.

D. Single Send Journey
Works for simple sends and can even be scheduled, but:
It still uses Journey Builder infrastructure.
It’s not as straightforward or efficient as a classic Send Activity in Automation Studio for recurring newsletters.

So the best option to speed up delivery and handle a frequently changing DE is:

✅ C. Scheduled Automation using a Send Activity

Explanation:

This is a classic scenario requiring a data join and comparison across two Data Extensions within a Journey. The path a contact takes must be determined by comparing an attribute from the entry source (Journey Data) with an attribute looked up from a related Data Extension (Contact Data).

Journey Data: This refers to the data from the contact's entry source—the Data Extension used to inject contacts into the birthday journey. It contains the Customer Number field for the entering contact.

Contact Data: In Journey Builder context, this refers to related data that can be looked up from other Data Extensions using a common key. Here, it would be the "transaction data extension" containing redeemed promo codes, also keyed by Customer Number.

The Decision Split Activity has a specific configuration mode designed for this exact cross-reference: "Contact Data and Journey Data." This mode allows you to:

- Select the related "transaction" Data Extension (Contact Data).
- Define the relationship by matching the Customer Number field from the Journey entry source to the Customer Number field in the transaction Data Extension.
- Create a split rule based on a field from the related Data Extension. For example: "Where Contact Data: TransactionDE.PromoCodeRedeemed is equal to 'TRUE'" to identify those who have redeemed, or conversely, "Where Contact Data: TransactionDE.PromoCodeRedeemed is null" to identify those who haven't.

This configuration performs the necessary "attribute to attribute comparison" to divert contacts appropriately.

Why the Other Options Are Incorrect:

A. Decision Split Activity using only Contact Data: This mode only allows you to evaluate rules against fields from a single, related Data Extension (Contact Data). It does not allow you to perform a direct comparison or join between an attribute from the Journey's entry source and an attribute from the related data. It's for splits based solely on a contact's status in an external system.

B. Decision Split Activity using only Journey Data: This mode only allows you to evaluate rules against fields from the contact's entry source Data Extension (the Journey Data). Since the transactional promo code data is housed in a separate Data Extension, it is not accessible in this mode. You cannot compare a Journey Data field to a field from another DE here.

C. Einstein Split Activity: This activity uses predictive intelligence (like likelihood to click or convert) to split the audience. It is not used for deterministic, rule-based splits that compare data fields between two known Data Extensions. It does not perform the required attribute-to-attribute comparison.

References:
Salesforce Marketing Cloud Help Documentation: "Decision Split Activity in Journey Builder" – This article details the different data comparison types. It explicitly describes the "Contact Data and Journey Data" option for creating splits that "compare data from the contact’s entry event with data from a related data extension."

Trailhead Module: Marketing Cloud Consultant > Orchestrate Customer Journeys – Covers the use of the Decision Split with multiple data sources to create complex branching logic based on customer attributes and behaviors stored in separate systems.

Explanation:

B. AMPscript: This is the fundamental, versatile scripting language in Marketing Cloud. Specific AMPscript functions (HTTPGet, ParseJSON, or custom utilities) can be used to request the RSS feed URL, parse the XML content, and dynamically loop through the entries to display the latest links at the time of send.

D. External Content: This is a feature within the Email Content block (often configured in Content Builder or an Email form). It allows the user to specify an External URL (the RSS feed) and define rules for how to retrieve and display the content inside the email when the send occurs. This often uses an underlying functionality similar to AMPscript/SSJS but provides a more user-friendly interface for configuration.

❌ Analysis of Incorrect Answers

A. Dynamic Content: Used to display different blocks of content (text, images) based on a subscriber's attributes, not to pull in and parse an external data feed like RSS.

C. Personalization Strings: Simple placeholders (e.g., %%firstname%%) used to insert subscriber attributes or system values. They cannot fetch and process dynamic external XML data.

📚 References
Salesforce Help: Creating External Content Blocks
Salesforce Developers: AMPscript Function Library (Look up functions like HTTPGet)

Explanation:

Why B is Correct: Error Notification
When automating file imports into Marketing Cloud, one of the most important considerations is error handling. If an import fails due to issues such as incorrect formatting, missing columns, or connectivity problems, someone must be notified immediately. This ensures accountability and allows the team to quickly resolve the issue before it impacts downstream sends. Without error notifications, failed imports could go unnoticed, leading to missed campaigns or incomplete data.

Why C is Correct: Consistent Timing
Automation relies on predictable schedules. If the data warehouse places the file on the SFTP at the same time daily, the automation can be scheduled to run reliably. If the timing is inconsistent, the automation may run before the file is available, causing failures or incomplete imports. Confirming the timing of file placement is therefore critical to designing a stable and dependable automation process.

Why E is Correct: Delta vs. Historical File
Understanding whether the file is a delta file (containing only new or changed records) or a historical file (containing the full dataset) is essential. This determines whether the automation should append new records to the data extension or overwrite it entirely. Using the wrong approach could lead to duplicate records or loss of historical data. This question directly impacts how the import activity is configured and how the data extension is maintained.

Why the Other Options Are Incorrect

A. Does the data extension have a data relationship?
While data relationships are important for segmentation and personalization, they are not directly relevant to the automation of importing files. The automation process only cares about the structure and timing of the file, not the relational design of the data extension.

D. Will the file have more than 5,000 rows?
Marketing Cloud can handle files with far more than 5,000 rows. Row count is not a limiting factor for automation design, so this question is not relevant to determining the solution.

References
Salesforce Help: Automation Studio – Import Activity
Salesforce Help: Automation Studio – File Drop Automation
Trailhead: Marketing Cloud Data Management Basics

Explanation:

Here’s how this fits the requirement step by step:

Requirement recap
Using Marketing Cloud Connect, but NOT sending tracking back to Sales Cloud.
Need to detect subscribers who have not opened five emails in a row (using MC tracking data views).
Then create a Task on the related Contact/Lead record in Sales Cloud for follow-up.

Because tracking isn’t going back to Sales Cloud, all the “did/didn’t open” logic has to live in Marketing Cloud data views (_Sent, _Open, etc.), and we only touch Salesforce when we’re ready to create the Task.

Why B works

1. Scheduled Automation
Runs on a schedule (e.g., nightly) to:

2. SQL Query Activity
Queries data views like _Sent and _Open to find subscribers who:
- Were sent 5 emails in a row, and
- Have no opens for those sends.

Outputs a Data Extension that contains:
- SubscriberKey (which should match ContactId/LeadId in a good MC Connect setup)
- Any fields needed to create the Task (Subject, Status, etc.)

3. Data Extension Entry (Journey Builder Entry Source)
Journey Builder uses the populated Data Extension as its entry source.
Contacts/leads in that DE are injected into the Journey.

4. Task Activity (in Journey Builder)
Journey step that creates a Salesforce Task on the corresponding Contact or Lead record via Marketing Cloud Connect.
This is where Sales users see the follow-up Task.

That chain matches option B exactly.

Why the others don’t fit

A. Salesforce Data Entry, SQL Query Activity, Task Activity
Salesforce Data Entry expects Salesforce data as entry source.
But our logic relies purely on Marketing Cloud tracking data, since tracking isn’t going back to Salesforce. We need to enter from a DE, not straight from Salesforce objects.

C. API Event Entry, SQL Query Activity, Task Activity
API Event Entry would require an external system to call into the journey; not what’s described here.
The requirement is fully solvable with internal automation and data views.

D. Scheduled Automation, Filter Activity, Data Extension Entry, Contact Activity
Filter Activity is limited compared to SQL and not ideal for “5 consecutive non-opens” logic.
Contact Activity updates Contacts, not create Tasks. The requirement explicitly wants Tasks on Contact/Lead.

So the correct set of activities to meet the requirement is:

✅ B. Scheduled Automation, SQL Query Activity, Data Extension Entry, Task Activity