Data 360 Consultant Practice Test 36 — 15 Questions

Question 1

A consultant is ingesting order records from an external system via an S3 bucket data stream. Each order has a Customer_Email__c field in the source, but the Order DMO in Data Cloud must link to the Individual DMO using the standard Party Identification (Party Identification DMO) relationship rather than a direct email field. Which approach should the consultant take to establish the relationship between Order and Individual in the data model?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Map Customer_Email__c to a Party Identification DMO record and relate the Order DMO to the Individual DMO through Party Identification using the standard Party Identification relationship.. Data Cloud's Cloud Information Model uses Party Identification as the bridge between the Individual DMO and other DMOs such as Order. Mapping the source email to Party Identification and using the standard Party Identification relationship ensures the order data participates in identity resolution and can be used for segmentation and activation without custom workarounds.

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Map Customer_Email__c directly to the Individual DMO's Email attribute and configure a lookup relationship from Order to Individual on that field.

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Create a custom DMO that stores email addresses and manually relate Order to Individual by matching on the email string during ingestion.

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Use a formula field on the Order DMO that concatenates the email with a prefix, then link to Individual using a text-based foreign key.

Question 2

A consultant is explaining the Identity Resolution process to a client. The client wants to understand what happens after match rules identify potential matches. Which statement best describes the role of reconciliation rules in Salesforce Data Cloud?

Accepted Answer

Reconciliation rules define the logic for merging field values from matched records into a unified profile.. Reconciliation rules are used after match rules have identified that two or more source records refer to the same individual. Their purpose is to determine which value to keep for each field in the unified profile when the matched records have conflicting values. This ensures that the unified profile has a single, coherent set of attributes based on business rules such as most recent or highest priority.

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Reconciliation rules assign a confidence score to each match to determine if a unified profile should be created.

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Reconciliation rules determine which source records are considered for matching based on data quality.

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Reconciliation rules define the schedule for running the Identity Resolution batch job.

Question 3

A consultant is building a data stream from Salesforce CRM using the CRM connector. The source object Account has a field AnnualRevenue that is a currency field with values in different currencies depending on the record. The consultant needs to ensure that revenue values are consistently comparable in the Unified Individual DMO for segmentation. What should the consultant do during data stream configuration and mapping?

Accepted Answer

Ingest the currency code field alongside AnnualRevenue and use a Data Cloud transformation or formula to convert all values to a single currency before mapping to the Unified Individual DMO.. Data Cloud does not automatically convert currencies, so mixed-currency revenue values would remain inconsistent in the unified profile. Bringing in the currency code and applying an explicit conversion before mapping ensures all revenue values are normalized to one currency, enabling accurate segmentation and reporting on the Unified Individual DMO.

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Map AnnualRevenue directly to the Unified Individual DMO's revenue attribute and rely on Data Cloud to automatically convert currencies using the org's corporate currency.

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Map AnnualRevenue as a text field to preserve the original values and let segmentation handle currency comparison at query time.

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Create a separate DMO for each currency and map AnnualRevenue into the appropriate DMO based on the currency code, then use multiple relationships to Individual.

Question 4

A Data Cloud Consultant is designing a solution where a financial services firm must ensure that analysts in the wealth management division can only build segments from data ingested for that division, while analysts in the retail banking division work with separate data. Both divisions share the same Data Cloud instance. Which Data Cloud feature should the consultant use to isolate the data?

Accepted Answer

Data Spaces, which partition data and metadata so users only see and work with the objects relevant to their assigned space.. Data Spaces provide logical partitioning of data and metadata within a single Data Cloud instance. By assigning wealth management analysts to one space and retail banking analysts to another, the consultant ensures each group only accesses the data model objects and ingested data relevant to their division, satisfying isolation without creating separate orgs.

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Sharing rules on Data Model Objects, which replicate Salesforce record-level sharing to restrict analysts to records they own.

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Named Credentials that scope each analyst's authentication so that queries return only records tied to their division.

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Separate Data Cloud orgs for each division, since a single instance cannot logically separate data for different business units.

Question 5

A consultant is preparing a ruleset run and wants to understand what the run history provides after execution completes. Which information does the Identity Resolution ruleset run history primarily deliver?

Accepted Answer

Metrics about the ruleset run, including match counts and unified profile results for that execution. Identity resolution run history captures the results of each ruleset execution, including match counts and unified profile outcomes, so consultants can confirm behavior and compare runs. Transformation errors appear in ingestion monitoring, rule type catalogs appear in the ruleset editor, and reconciliation priority lives in field configuration, so none of those are delivered by run history.

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A per-source-stream list of raw records that failed data transformation during ingestion

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The reconciliation priority order applied to each unified profile field

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A catalog of available match rule types that can be added to the ruleset

Question 6

A consultant is configuring a data stream from an Amazon S3 bucket that contains 40,000 JSON files, each under 200 KB, delivered continuously throughout the day. The business requires that newly landed files be visible for segmentation within minutes rather than hours. Which ingestion configuration should the consultant choose for this data stream?

Accepted Answer

Configure the data stream with the Streaming ingestion type and enable file-based event notifications on the S3 bucket.. The scenario describes many small files arriving continuously, which is the classic case for event-driven streaming rather than scheduled bulk polling. Configuring the data stream as Streaming and using S3 event notifications means each new object triggers ingestion promptly, delivering the minutes-level freshness the business requires without repeatedly scanning the entire bucket.

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Configure the data stream as a Direct Data API ingestion with a scheduled pull from the S3 bucket.

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Configure the data stream with the Batch ingestion type and schedule it to run every 15 minutes.

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Configure the data stream using the Data Cloud S3 connector with a transformation that merges all files into one record.

Question 7

A consultant is creating an activation to a cloud file storage target, such as Amazon S3, to export a segment of customers for a data warehouse. The segment includes 10 million records. The consultant notices that the activation is taking longer than expected and sometimes fails with a timeout error. Which action should the consultant take to improve the activation's performance?

Accepted Answer

Enable compression and partition the output files by a key attribute to reduce file size and improve parallelism.. For large activations to cloud file storage, enabling compression and partitioning the output files are key optimizations. Compression reduces the amount of data transferred, and partitioning allows multiple files to be written concurrently, improving throughput and reducing the chance of timeouts. These settings are available in the activation configuration and are recommended for high-volume exports.

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Reduce the segment size by applying additional filters to exclude less active customers.

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Increase the batch size in the activation configuration to reduce the number of API calls.

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Schedule the activation to run during off-peak hours to avoid contention with other processes.

Question 8

A consultant has ingested a large volume of web event data into Data Cloud using the Web SDK connector. The events include page views, clicks, and custom events. The consultant needs to model this data so that it can be used for segmentation and activation, and also joined with the Individual DMO. Which DMO should the consultant map the web event data to, and how should the relationship to Individual be established?

Accepted Answer

Map to the Engagement DMO and relate to Individual through the Individual ID that is resolved during identity resolution, using the standard Engagement to Individual relationship.. Web event data is best modeled as Engagement DMO records, which capture interactions and relate to the Individual through the resolved Individual ID from identity resolution. This standard relationship ensures events are linked to the correct unified profile, enabling behavioral segmentation and activation even when events start with anonymous identifiers.

Answer

Map to the Individual DMO directly, storing each web event as an attribute on the Individual record.

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Map to the Engagement DMO and relate to Individual through Party Identification using the standard Engagement to Individual relationship.

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Map to a custom DMO for web events and relate to Individual by mapping the web event's anonymous ID to the Individual DMO's primary key.

Question 9

A consultant ingests order line items from an external system into a custom Data Model Object. Each source row contains an order number and a product SKU, and a single order can contain many line items. The consultant maps both fields as standard text attributes and links the DMO to the Individual DMO. During segmentation, counts of line items per customer are drastically inflated. What is the most likely cause?

Accepted Answer

The DMO lacks a primary key that uniquely identifies each line item, so the platform cannot deduplicate records arriving from the source.. Inflated counts after repeated ingestion are the signature of missing record identity. If the DMO has no primary key that uniquely identifies each order line item, every ingestion run inserts new rows instead of updating existing ones, so segmentation sees many copies of the same line item. Defining a proper primary key on the line item grain resolves the duplication.

Answer

The source order number field was mapped to a text attribute instead of a number attribute, so values are concatenated rather than counted.

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The Individual DMO relationship was configured as one-to-one, which forces the platform to replicate each line item per customer.

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The product SKU field was mapped as a number attribute, causing leading zeros to be stripped and multiple SKUs to collapse into one.

Question 10

A consultant is onboarding a new source system and needs to create the data stream, map its fields to the data model, and then verify that the mappings produced the expected objects. Which sequence of Data 360 capabilities should the consultant follow?

Accepted Answer

Create the data stream, then use Data Explorer to browse the resulting data model object records and confirm the mapped fields are populated.. After creating a data stream and mapping source fields to the data model, the consultant needs a direct view of the resulting records. Data Explorer provides exactly that by letting users browse data model objects and inspect field values, confirming that mappings populated the intended attributes before moving on to segmentation or activation work.

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Create the data stream, then configure an activation to an external platform and confirm delivery to validate the mappings.

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Create the data stream, then create a calculated insight to compute field statistics and treat that as confirmation of mapping success.

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Create the data stream, then build a segmentation to count records and infer whether mappings succeeded from the audience size.

Question 11

A consultant ingests a nightly batch of customer records into the Individual data model object. The source system sends a full snapshot each night, including customers who have not changed. The consultant wants to avoid creating duplicate individuals while ensuring changed records are reflected. Which approach should the consultant take?

Accepted Answer

Map a stable source identifier such as the customer number to the Individual data model object primary key so repeated snapshots upsert instead of insert.. Full nightly snapshots are common, and the way to prevent duplicates is to give each customer a stable identity in the data model. Mapping the source customer number to the Individual data model object primary key makes each snapshot an upsert, so unchanged customers are overwritten with identical values and changed customers are refreshed without creating new records.

Answer

Configure the data stream to append all rows and rely on segmentation filters to remove duplicates at query time.

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Create a new data stream for each nightly file and delete the previous data stream after each load to reset the object.

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Enable the batch data stream option that automatically detects changed rows and skips unchanged ones during each run.

Question 12

A consultant is mapping a Data Source Object (DSO) from an external e-commerce system to Data Model Objects (DMOs) in Data Cloud. The source has fields for order ID, customer ID, order date, and order total. The consultant needs to ensure the mapping supports identity resolution and segmentation. Which TWO actions should the consultant take when mapping the DSO to DMOs? (Choose two.)

Accepted Answer

Map the order ID, order date, and order total to the Order DMO and relate it to the Individual DMO through Party Identification.. To support identity resolution and segmentation, the customer ID should be mapped to Party Identification so it can be used as an identifier, and the order details should be mapped to the Order DMO with a relationship to Individual through Party Identification. This follows the Cloud Information Model and ensures orders link to the correct unified profile.

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Map the order total directly to the Individual DMO's lifetime value attribute to simplify segmentation.

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Map the customer ID to the Individual DMO's primary key to create a direct relationship.

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Create a custom DMO for each order and relate it to Individual using the order ID as the foreign key.

Question 13

A consultant has ingested data into a Data Lake Object and now needs to map it to a Data Model Object so that the data can be used in segmentation and activation. The consultant opens the Data Lake Object and begins the mapping process. Which statement correctly describes what happens when a Data Lake Object is mapped to a Data Model Object?

Accepted Answer

The Data Model Object becomes a semantic view over the Data Lake Object, and the DLO continues to store the ingested data.. Data Lake Objects store ingested data, and Data Model Objects provide the semantic layer used for segmentation, reporting, and activation. Mapping a DLO to a DMO creates that semantic view without removing the underlying storage, so the DLO continues to hold the data while the DMO exposes it in the modeled form required for downstream use.

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The Data Lake Object is deleted and its data is moved into the Data Model Object storage.

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Mapping is only possible after the Data Lake Object is converted into a Data Source Object.

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The Data Model Object replaces the Data Lake Object as the primary storage and the DLO is archived.

Question 14

A consultant is explaining the primary capabilities that Data Cloud provides as a platform to a customer's architecture review board. Which two statements accurately describe core Data Cloud platform capabilities? (Choose two.)

Accepted Answer

It ingests and harmonizes data from multiple sources into a unified customer profile.. Data Cloud's defining capabilities are ingesting and harmonizing multi-source data into a unified customer profile and activating that unified data to internal and external destinations. These two together describe the platform's core purpose. It does not replace CRM systems of record, it is not a permanent bulk-copy ETL engine, and identity resolution is scheduled rather than guaranteed sub-second, so those statements misrepresent how the platform actually works.

Answer

It guarantees sub-second real-time identity resolution across every ingested source automatically.

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It replaces the need for a CRM by storing all transactional sales records natively.

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It provides a full extract, transform, load engine that permanently copies all source data into Salesforce storage.

Question 15

A consultant is configuring an Identity Resolution ruleset for a telecom company. The business wants to ensure that when multiple source profiles are matched, the unified profile's 'Loyalty Tier' field is populated with the value from the most recently updated source record. Which type of rule should the consultant configure to achieve this?

Accepted Answer

Reconciliation Rule. Reconciliation rules determine how attribute values are selected when multiple source profiles are unified. The 'Most Recent' strategy picks the value from the most recently updated source record. Match rules identify duplicates, but they do not resolve attribute conflicts. Data spaces and fuzzy matching serve different purposes. Thus, a reconciliation rule is the correct component to ensure the Loyalty Tier reflects the latest update.

Answer

Match Rule with Fuzzy Matching

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Data Space Rule

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Match Rule