Explanation:
The issue of jumbled or corrupted accented characters (like those found in French, Spanish, or German names—e.g., é, ü, ñ) in an extracted file is a classic problem caused by a mismatch between the character set used by the source system (Marketing Cloud, which uses Unicode) and the target file format. To correctly display these extended characters, the administrator must ensure the extraction process explicitly uses a character encoding standard that supports the full range of global characters.

Correct Options:

B. Change Character Encoding to UTF-8:
UTF-8 (8-bit Unicode Transformation Format) is the most widely supported character encoding standard and is the default used by Marketing Cloud for storing global characters. Explicitly setting the extraction's character encoding to UTF-8 ensures that the accented characters are written to the file using the correct, universally supported format, preventing the jumbling that occurs when using older standards like ASCII or Latin-1.

C. Select Unicode Output:
This is a specific setting within the Marketing Cloud Extract activities that forces the output file to use a Unicode-compliant format, which is essentially a confirmation of the UTF-8 encoding (or similar robust Unicode standard). Selecting this option is often required to correctly handle multi-byte and accented characters and is the official Marketing Cloud mechanism for solving this exact problem in extracts.

Incorrect Options:

A. Change Column Delimiter to:
The Column Delimiter (e.g., comma, pipe, tab) dictates how columns are separated within a row. It has absolutely no bearing on how the characters within the fields (like accented names) are encoded or displayed.

D. Change Format to xml:
While an XML file format can handle Unicode characters, the Format setting determines the file structure (CSV, TXT, XML) of the output, not the fundamental character encoding. The problem (jumbled characters) is a character encoding issue, not a file structure issue. The administrator should fix the encoding rather than forcing a completely different file type.

Reference:
Salesforce Marketing Cloud Documentation on Data Extract Activity and Character Encoding (Unicode/UTF-8)

Explanation:
The requirement is to ingest, combine, and analyze disparate advertising data sources (DoubleClick Bid Manager, Facebook Ads, Google Analytics) with Marketing Cloud data to measure campaign effectiveness. This calls for a marketing analytics and attribution platform capable of unifying multi-channel data into a single view for performance insights. Among the options, one is a Salesforce product designed specifically for this purpose.

Correct Option:

C. Datorama:
This is the correct recommendation. Datorama (part of Salesforce Marketing Cloud) is a marketing intelligence and analytics platform. It is built to connect to a vast array of data sources via APIs—including all those listed (Google Marketing Platform, Facebook, Google Analytics) and Marketing Cloud itself—to unify, visualize, and analyze performance data in a single dashboard, enabling accurate cross-channel attribution and optimization.

Incorrect Options:

A. Einstein AI:
This refers to artificial intelligence capabilities infused across Salesforce products (e.g., Einstein Recommendations, Journey Insights). While it can provide predictive insights within Marketing Cloud, it is not a dedicated platform for integrating and harmonizing raw data from multiple external advertising APIs for holistic reporting.

B. Interaction Studio:
This is a real-time customer data platform (CDP) and personalization engine. It focuses on creating a unified customer profile and delivering next-best-action experiences in real-time. It is not designed for the backward-looking, aggregate campaign performance analysis and multi-source data unification required here.

D. Google Marketing Platform:
This is the suite of Google's advertising and analytics tools (which includes Display & Video 360, formerly DoubleClick Bid Manager, and Google Analytics). It is the source of some of the data, not the solution for combining it with Marketing Cloud data. Using it alone would keep the data siloed within Google's ecosystem.

Reference:
Datorama's product documentation positions it as the solution for marketing performance management, specifically to "unify data from all your marketing channels" including "social, search, email, and web analytics" to "measure ROI and optimize campaigns."

Explanation
Selecting “Send as Multipart MIME” means Marketing Cloud sends both an HTML version and a text-only version of the email in a single message. This allows email clients that cannot render HTML—or users who prefer text—to receive the text version. It also preserves full tracking functionality. If a custom text version is not provided, Marketing Cloud automatically generates one. This improves deliverability and accessibility.

Correct Option Explanations

A. Open and click activity are tracked in either version.
Multipart MIME ensures that tracking functions work whether the subscriber views the HTML or text version. HTML opens use tracking pixels, while text version engagement is still tracked through click-throughs on tracked links, providing consistent reporting regardless of which version the subscriber interacts with.

C. An auto-generated text version will be sent with your HTML email.
If a custom text version isn't created, Marketing Cloud automatically generates a text-only version. This supports accessibility and ensures that subscribers using text-only clients (or those blocking HTML) still receive readable content.

Incorrect Option Explanations

B. The email will avoid detection by various SPAM filters.
Multipart MIME does not guarantee avoiding spam filters. Spam filtering depends on sender reputation, authentication, content quality, and sending behaviors—not whether the email is multipart.

D. A custom text version will be sent with your HTML email.
This only occurs if you manually create a custom text version. Multipart MIME does not automatically generate a custom version; it generates only an auto-formatted plain text version unless overridden.

Reference
Salesforce Documentation: Email Studio → Multipart MIME

Marketing Cloud Best Practices: HTML and Text Email Versions

Trailhead: Create and Send Emails with Marketing Cloud

Explanation:
The requirement is to leverage an intelligence product within Marketing Cloud to observe customer behavior, build performance profiles (i.e., preference profiles), and use that data to deliver the next best content—specifically, product or content suggestions. This functionality, which drives personalized product and content discovery based on real-time and historical behavioral data, is the core purpose of Einstein Email Recommendations.

Correct Option:

D. Einstein Email Recommendations
Einstein Email Recommendations (EER) observes customer behavior (clicks, views, purchases) on a website using the Collect Code.

It uses this data to build individualized preference profiles for each customer.

Based on these profiles and algorithms (e.g., similar products, popular products), EER delivers the next best product or content recommendations dynamically into emails at the time of open, achieving highly personalized content delivery.

Incorrect Options:

A. Content Builder:
This is the application used to create, manage, and store email content, templates, and content blocks. It is a necessary tool for displaying the recommendations but does not contain the intelligence or algorithms required to generate them based on customer behavior.

B. Einstein Send Time Optimization (STO):
STO is a feature that predicts the single best time of day to send an email to maximize the chance of an open. Its purpose is optimizing delivery timing, not observing behavioral preferences and recommending content/products.

C. Audience Builder:
This tool, part of the Audience Studio suite, is used to consolidate and segment data from various sources (DMPs, CRMs) to create audiences. While the behavioral data used by EER is stored in the cloud, Audience Builder is focused on macro-level audience creation and activation, not the real-time, personalized content delivery engine for product recommendations.

Reference:
Salesforce Help Documentation on Einstein Email Recommendations Functionality

Explanation:
To create event-driven, personalized journeys aimed at increasing purchase frequency, NTO needs data points that reflect a customer's purchase behavior and recency. These attributes allow for segmentation and triggers based on actual transactional history, enabling targeted re-engagement campaigns (e.g., post-purchase follow-ups, replenishment reminders, or win-back campaigns for lapsed customers).

Correct Options:

B. Number of items per order:
This indicates purchase volume and potential for cross-selling/up-selling. A customer who typically buys multiple items might be receptive to bundles or "complete the look" recommendations. It helps personalize the journey's messaging and offers based on their buying habits.

D. Last purchase date:
This is critical for measuring purchase recency and identifying customer lifecycle stage. It allows NTO to trigger journeys based on elapsed time (e.g., a "We miss you" email after 60 days) and is a foundational metric for RFM (Recency, Frequency, Monetary) segmentation, directly tied to increasing purchase frequency.

Incorrect Options:

A. Products purchased from a competitor:
While competitively insightful, this data is extremely difficult to obtain reliably and at scale for direct marketing use. It's not standard transactional data NTO would have in their own system. Journey Builder personalization relies on data owned and stored by NTO.

C. Channel preference of customers:
This is valuable for optimizing the delivery channel (e.g., email vs. SMS) but is secondary to the core purchase-behavior data needed to drive the content and timing of a purchase-frequency strategy. Knowing when and what someone bought (Options B & D) is more directly actionable for increasing purchase frequency than knowing their preferred channel.

Reference:
Lifecycle marketing and journey design best practices emphasize using transactional data like purchase history, average order value, and recency to power personalized, triggered campaigns aimed at customer retention and increasing lifetime value.