Microsoft Customer Data Platform Specialist (MB-260) Interview Questions

Candidates for the Microsoft Customer Data Platform Specialist (MB-260) test carry out arrangements that give bits of knowledge into client profiles and that track commitment movement to assist with further developing client encounters and increment client maintenance.

Candidates ought to have firsthand involvement in Dynamics 365 Customer Insights and at least one extra Dynamics 365 application, Power Query, Microsoft Dataverse, Common Data Model, and Microsoft Power Platform. They ought to likewise have direct involvement in rehearses connected with protection, consistence, assent, security, mindful AI, and information maintenance strategy.

Candidates need insight into processes connected with KPIs, information maintenance, approval, representation, planning, coordinating, discontinuity, division, and improvement. They ought to have an overall comprehension of Azure Machine Learning, Azure Synapse Analytics, and Azure Data Factory.

1.) What exactly is a Customer Data Platform in Microsoft?

A Customer Data Platform incorporates client information from all your various information storehouses, and it furnishes you with more profound experiences of that information. Moreover, it helps make that information accessible to different frameworks. As needs are, you can give customized excursions to your clients, from the very first moment, in view of what their identity is, the manner by which they draw in with you, and how they carry on with their lives. Eventually, these customized encounters matter since individuals is the justification for having a Customer Data Platform.

The initial segment of any Customer Data Platform arrangement is to give a merged perspective on your clients. You can accomplish this objective by gathering however much information as could be expected and bringing together it into a solitary perspective on the client. Consider this approach like making an advanced twin for your clients. This computerized twin comprises of understanding the advanced touchpoints that a client has with your association. You need to draw in with, catch, and comprehend however much about your clients’ advanced exercises as could be expected. The more computerized exercises that you can catch and comprehend, the more clear the image of them you will have. Each advanced movement, for example, an email that a client sends or gets, makes a commitment information point that assists you with building a more complete computerized twin of your client. As you acquire contact focuses, they will on the whole make the groundwork of client information and start to give a more clear picture of the client’s advanced twin.

2.) Mention some of the touchpoints.

Orders placed
Cases created
Products returned
Customer survey responses
Web searches
Social posts
TXT messages
Events attended
Webpages visited

3.) What are Dynamics 365 Customer Insights?

Microsoft Dynamics 365 Customer Insights, part of the Customer Data Platform from Microsoft, assists you with conveying customized client encounters. The stage’s capacities assist you with bringing together client information across different sources to get a solitary perspective on clients, and it gives experiences into who your clients are and the way that they draw in with your foundation. The Dynamics 365 Customer Insights Audience bits of knowledge assist you with changing your business into a client-driven association. Marketing, sales, and administration experts have the bits of knowledge that they need to customize encounters.

4.) What are audience insights in Microsoft Customer Data Platform?

Audience insights, a capacity of Dynamics 365 Customer Insights, assist you with building a more profound comprehension of your clients. By interfacing with information from different conditional, social, and observational sources, you can make a 360-degree perspective on your clients.

5.) What are the functions of audience insights?

With audience insights, your company can:

Take out information storehouses and bind together customer information – You can associate and bind together your information through prebuilt connectors to well-known information sources that get the conditional, observational, and conduct information that are generally vital to you.

By utilizing AI and AI proposals, you can determine client personalities across these sources. Thus, you will have a solitary, bound-together client profile that gives top to bottom bits of knowledge about the client.

Consists of audience intelligence – With worked in usefulness that utilizes Microsoft Graph, you can fabricate a more extravagant client profile that joins crowd knowledge.

Convey customized customer experiences – With out-of-the-container logical bits of knowledge and custom-made client profile cards, you can rapidly implant this information into business applications that you utilize consistently to all the more likely to engage in promoting, deals, and administration experts.

Redone client profiles, business measures, and client fragments to let you characterize what’s generally critical to your association and computerize more client-driven encounters.
Experiences are inserted into custom line-of-business applications that are based on the Microsoft Power Platform. This element guarantees that your representatives are furnished with the right information with regards to how they’re functioning with clients at that point.

6.) How will you identify your audience with the help of the Microsoft Customer Data Platform?

Each association will contrast the sorts of customers that they engage with. A few associations center basically around focusing on organizations, while others will target individual customers.

A single audience insights occurrence can include:

Business accounts (B2B) – The essential interest groups are accounts associations or organizations contacts.
Individual consumers (B2C) – The essential interest group is individuals.

Whenever you characterize a climate, you’ll have to first characterize the kind of crowd that you need to target. After you’ve established a climate that objectives one sort of crowd, the following climate that you make should focus on the other kind of crowd. For instance, assuming you’ve proactively added a B2C climate to your example, the following climate that you make in the occurrence should target B2B clients.

7.) What is the audience insights process?

Audience insights will ingest information from your various information sources and afterward bind together with them into a solitary client profile. After you have a bound together profile, you can utilize things like measures and information advancement. From inside the client profile, you can follow information across various socioeconomics and distinguish patterns in view of key information that you need to follow.

This cycle includes the following activities:

Ingesting the data – Defines the data sources that your information is coming from. Information can be ingested from a wide scope of information sources through working in connectors that are associated with various information suppliers.

Make customer profiles – Customer profiles are made by binding together the information that is ingested from your various information sources into a solitary profile.

The unification interaction comprises three stages:

Map – Identifies which substances and fields from your information will be utilized to recognize the client record, for example, a client number.
Match – Specifies how to join your datasets into a brought-together profile through a progression of precludes that call which fields will be utilized during the matching system.
Merging – Completes the cycle and accommodates clashes that may be available.

Defining Activities – Activities help to unite your client exercises across information sources and put them into a timetable view. These exercises could address cooperations or buys.

Defining relationships – Relationships interface your elements and create a diagram of your information.
Characterize measures – Measures address the examination that best mirrors the presentation and soundness of your business. These actions could address fulfillment levels, income targets, or execution levels.
Enhance data – Enrichments assist you with a better comprehension of who your clients are by utilizing supplemental information that is given by Microsoft and outer sources to give more detail, like brand proclivity and loyalties or monetary subtleties.
Assemble predictions – Prediction models let you utilize the force of AI to make forecasts about your information, for example, assuming that a client may be prepared to buy something or on the other hand on the off chance that they’re at risk for not reestablishing a membership. You can use out-of-the-container expectation models or utilize your own that you have made by utilizing devices like Microsoft Azure.
Make fragments – With sections, you can bunch your clients in view of segment, conditional, or social client credits.
Activating the data – After you’ve assembled your crowd bits of knowledge example, you can involve the data in crowd experiences (measures, exercises, and bits of knowledge) in applications, for example, other Microsoft Dynamics 365 applications, Microsoft Power Apps, LinkedIn Ads, Google Ads, and that’s only the tip of the iceberg.

8.) What are the sources for gaining a 360-degree view of your customers that starts by ingesting the specific data that you need to work with across all your organization’s different data silos?

These sources could include:

Traditional information like point-of-sale systems.
Observational information, for example, item testing application
Behavioral sources, for example, Customer Service, Sales, and so on, applications
Any information source where customer-related information is put away and stored.

9.) What do you understand by ETL in the Customer data platform of Microsoft?

Prior to applications that work with information from various sources, for example, Audience Insights can utilize it. The information should be gathered and refined into something that can be consumed by the application. This interaction is basic to guarantee that the information is utilized fittingly. This interaction is alluded to as ETL, which represents Extract, Transform, Load.

As the name states, there are three stages that make up the ETL interaction and empower information to be incorporated from source to objective.

Data extraction: Extracts crude information from an information source like heritage frameworks, cloud conditions, CRM/ERP applications, information stockrooms, and so forth.
Data transformation: Improves information quality and availability through cycles, for example, information purifying, normalizations, arranging, and so forth.
Data loading: Loads the information into its new area.

Inclining upon the information ingestion strategy that you will utilize, ETL may be done as a feature of the ingestion interaction, or before ingesting the information. As we inspect the different ingestion techniques accessibly, we’ll take a gander at when ETL ought to be finished.

10.) What are the three options that audience insights can choose from to decide which ingestion method to use?

Audience Insights give three choices to browse:

Import Data – Used when you need to associate with information, for example, Microsoft Dataverse, Azure Blobs, OData sources, and so on. For more data. see Power Query Connectors.
Associate with a Common Data Model Folder – Used when you need to interface with an Azure Data Lake Storage Gen 2 Account. For more data, see Common Data Folder.
Associate with a Dataverse – Used when you need to interface with informational collections in the Dataverse information lake. For more data, see Dataverse.

11.) What is Power Query?

Power Query is an information change and information arrangement motor. Power Query accompanies a graphical connection point for getting information from sources and a Power Query Editor for applying changes. Since the motor is accessible in numerous items and administrations, the objective where the information will be put away relies upon where Power Query was utilized. Utilizing Power Query, you can play out the concentrate, change, and burden (ETL) handling of information.

12.) How does one import data sources using Power Query?

Before you start ingesting information into Audience Insights, you really want to choose the best technique to use for gathering the information. This will fluctuate contingent upon elements, for example, the volume of information being ingested and how much information changes is required. In situations that require a lot of information change or while you will associate non-Common Data models or Dataverse sources, bringing in information is many times the most ideal choice.

The import choice gives the most adaptability as it utilizes Power Query. Power Query is Microsoft’s Data Connectivity and Data Preparation innovation used to get to and reshape information from many information sources. It incorporates around 40 distinct connectors to interface with information sources, for example, Excel, Oracle, OData, and Azure, and that’s just the beginning. It’s vital to take note that when information is ingested utilizing the import choice, it’s replicated into your Audience Insights information lake. Contingent upon the volume of information, this could require additional capacity to help. You’ll have to ensure you can get the information you need to import as various connectors might require various arrangements and confirmation. Despite the fact that Audience Insights utilizes Power Query, not all connectors Power Query upholds permit you to bring information into Audience Insights. You ought to be comfortable with various choices accessible in light of the connector you select.

13.) What is the most important advantage of Power Query?

One of the principal benefits of bringing in information utilizing Power Query is the capacity to perform information change. Information change is utilized to change information to a suitable structure so it very well may be utilized for a measurable test or technique. From an Audience Insights stance, information normally should be changed to guarantee it very well may be utilized suitably by application highlights like exercises and measures. For instance, exercises depend vigorously on dates, so assuming you’re proposing to involve information for exercises, you’ll have to guarantee that the informational collection has no less than one information field in it. A numeric field would be required for fields that may be utilized in estimations or as measures like computing the complete expense of something.

During the import interaction, you can alter arrangement settings, for example, the delimiter utilized (in light of the information source chosen). The see region permits you to change a portion of these settings before you start the change cycle.

14.) What are the data transformations highly recommended by Microsoft?

Use headers as the primary row – If you’re ingesting information from a CSV document, you can utilize the principal line of the CSV record as the header data. Select Transform Table, and you’ll see the choice to switch between involving the principal column as headers or involving it as information.

Map your information to a standard Data Format – Audience Insights permits you to plan your information to the Common Data Model. The Common Data Model (CDM) is a norm and extensible assortment of diagrams (elements, ascribes, connections) that addresses business ideas and exercises with distinct semantics, to work with information interoperability. Instances of substances incorporate Account, Contact, Lead, Opportunity, Product, and so forth. To do this, go to Map to Standard, and afterward map fields from your source information to Common Data Model fields.

Alter Field Data Types – Each field with an informational collection will have an information type related to it.

15.) What is Clean, transform, and load data?

Whenever you use Power Query to ingest information into Customer Insights, you can utilize the information change abilities accessible to shape your information depending on the situation for the best involvement with the application. After you select the Power Query connector, have recognized the information source to utilize, and associated with it, you can begin forming your information. Select the Transform information choice at the lower part of the screen to open the Power Query architect.

16.) How will you identify column headers and names?

The initial phase in molding your underlying information is to distinguish the section headers and names inside the information, and afterward assess where they’re situated to guarantee they are perfectly located. At the point when the information is acquired, Power Query Editor expects that all information has a place in table columns. In any case, an information source could have a first line that contains section names. To address this error, you want to advance the initial table line into section headers.

You can advance headers in two ways:

Select the Use First Row as the Headers choice on the home tab.
Select the drop-down button close to Column1, then select Use First Row as Headers.

17.) How does one rename columns in Customer Data Platform Microsoft?

As your information comes in, you could observe that at least one columns have some unacceptable name, contains a spelling mistake, or simply isn’t easy to use. In those occurrences, you can rename columns on a case-by-case basis.

Columns can be renamed in two ways:

Right-click the column header and select Rename.
Double-tap the column header and overwrite the name with the right name.

18.) How will you remove columns?

A key stage in the information forming process is to eliminate superfluous segments. Eliminating sections at the beginning phase in the process instead of later is ideal, particularly when you have laid out connections between your tables.

You can eliminate sections in two ways:

Select your desired columns to eliminate. On the Home tab, select Remove Columns.
Select your desired columns to keep. On the Home tab, select Remove Columns and afterward decide to Remove Other Columns.

19.) How does one change the column data type?

The sorts of information that are put away in your columns can affect various parts of how you can manage the information in Customer Insights. For instance, assuming you’re making a fragment that will distinguish clients who are inside a particular age range, it’s significant the date of the birth section is organized to date. In the event that it isn’t, the framework can’t utilize information articulations to really take a look at the age of the client. This is likewise significant while you’re intending to perform estimations, for example, recognizing the amount somebody could have spent.

It is entirely expected for the information to at first have some unacceptable information type related to it. For instance, when you interface with a CSV/Text document, every one of the stacked columns will probably all be appointed a text information type. In the event that we don’t guarantee that every column has the right information type, we could run into issues not too far off. Information kinds of the can are altered in two ways.

The primary way is to choose the column that has the issue related to it. On the Home lace, select the Data Type drop-down in the Transform tab, and afterward, select the right information type from the rundown.

The second method for changing information types is to choose the information type symbol close to the section header, and afterward, select the right information type from the rundown.

20.) What do you know about renaming a query?

At first, when you make your question, it will probably have a nonexclusive name. It’s a great practice to change remarkable or pointless question names to names that are more self-evident, or that the client is more acquainted with. For example, assuming you’re bringing in buys from a Point-of-offer framework, you should name the inquiry something like POS Purchases. Whenever you’ve molded the information you need, you can choose the Next button to save the inquiry and start the ingesting system.

21.) What are the things to consider before deciding to use a Common Data Model?

In contrast to bringing in information, connecting with a Common Data Model organizer doesn’t duplicate the information into Audience Insights. Moreover, Common Data Model information ingestion upholds Azure Data Lake Gen2 stockpiling accounts only. You can’t utilize Azure Data Lake Gen1 stockpiling records to ingest information. The information in your Azure Data Lake needs to keep the CDM guideline. Different arrangements weren’t upheld at the hour of this module distribution (November 2020).

The Azure Data Lake you going to interface with and ingest information from should be in a similar Azure area as the Dynamics 365 Customer Insights climate. You can’t associate with a Common Data Model envelope from an Azure Data Lake in an alternate Azure locale. You can track down the Azure district for your Customer Insights climate by getting to About from the System settings.

22.) How do you connect to a Common Data Model folder?

The methodology to ingest information from a Common Data Model beginnings equivalent to some other technique for ingesting information. To interface with a Common Data Model, you’ll have to give the accompanying:

Access key
Container
Account name

You’ll have to choose the model.json that you need to import information from. You’ll simply have the option to choose model.json records from your Common Data Model organizer. Any model.json record related to another information source in the occurrence won’t show in the rundown. Once chosen, you will be furnished with a rundown of accessible elements in the model.json document. You can choose which substances you need to ingest from the information source.

23.) What is Brand and interest enrichment?

Knowing your customer’s image affinities and interests can assist your association with creating sustaining and showcasing techniques in view of who your clients are. Crowd Insight’s image and interest advancement abilities assist you with improving your information by showing various brands and interests for your clients in view of their socioeconomics.

Audience insights brand and interest improvement utilize online pursuit information from Microsoft Graph to advance your client information with brand and interest affinities. These affinities are resolved in view of information from individuals with comparative socioeconomics in light of their age, orientation, and area. Online quest volume for a brand or interest decides the amount of fondness a segment section possesses to a brand or interest contrasted with different portions. You can dive deeper into how the scores are determined in the Affinity score and certainty documentation.

24.) What is View enrichment data on the customer card?

Ordinarily, addresses in your association’s information can be unstructured, fragmented, or inaccurate. Utilize Microsoft’s models to standardize and enhance your addresses into the Common Data Model organization for better precision and bits of knowledge. The model goes through a two-venture interaction to improve a location. To begin with, it parses the location to distinguish its parts and places them into an organized organization. Then, at that point, we use AI to right, complete, and normalize the qualities in the location.

25.) What is the use of enrichment services?

Brand and interest advancement is given by utilizing Microsoft Graph, and it tends to be added to any Audience Insights case. Your association could find that to more readily comprehend and target clients, more profound improvement is required. For instance, you should recognize higher-pay families or clients who are in unambiguous areas or locales.

Audience Insights gives you the capacity to utilize mixes with various accomplice administrations to give extra advancement choices that depend on the segment, area, and company subtleties.

Location – Provided by HERE Technologies
Company data – Provided by Leadspace
Demographics – Provided by Experian

26.) What is the prediction feature in Audience insights in the Customer Data Platform?

The predictions highlighted in Audience Insights permit you to make anticipated values that can improve how you might interpret a client. You can make basic on/off or valid/misleading style predictions in the application by utilizing AI Builder. Man-made intelligence Builder is a Microsoft Power Platform turnkey arrangement that brings the force of AI through a point-and-snap insight. It permits you to construct custom models that are customized to your necessities or utilize a prebuilt model that is prepared to use for some normal business situations. You can look further into AI Builder in What is AI Builder?.

Audience Insights utilizes the AI Builder Predict model. The model makes yes/no or valid/misleading style expectations in light of information that is put away in Microsoft Dataverse. To utilize the forecast include, your association should have both a Dataverse occurrence and an Audience Insights climate that are in a similar inhabitant. Moreover, Audience Insights should be your Dataverse case by utilizing the Dataverse connector. You can’t utilize foresee choices assuming that you imported your Dataverse information source by utilizing Microsoft Power Query. After a foresee model has been made, you can alter the model in AI Builder to adjust the outcomes, if important.

27.) What are the two usual ways to create predictions in the Customer Data Platform?

Audience Insights permits you to make forecasts in two distinct ways:

From Quick Create segments
From the Customer entity

No matter what technique you use to make the expectation, they’re generally founded on a field in which you need to make the forecast. For instance, a client’s orientation could have just been remembered for one of your information sources. Along these lines, you could have numerous clients who are missing orientation data in their profiles. You could make a forecast to decide if a client is male or female.

You can make a forecast on the client profile by going to Data and choosing Entities. Open the Customer element under Profiles. Under the Summary section, find the quality name that you need to anticipate values for, and afterward select the Overview symbol.

28.) Do you know how to create a prediction while creating a segment?

One more way that you can make predictions for missing qualities on a particular property is the point at which you’re making a speedy segment that depends on whether you’re bringing together the Customer element or the Customer_Measure substance. You can finish this interaction by going to Segments, making another section, and afterward selecting Create from Profile.

You’ll make the segment as you would some other fast section. You’ll have to characterize a field to make a portion on and afterward select your desired administrator to utilize. Assuming the section that you made has inadequate information in the source field, the framework will inquire as to whether you need to anticipate the missing qualities.

29.) How will you best manage predictions in the Customer Data Platform?

As your predictions run, you might experience situations where the Predictions come up short. There are a few kinds of mistakes that can happen, and they portray what condition caused the blunder. For instance, a blunder that there’s insufficient information to precisely foresee is ordinarily settled by stacking more information into Customer Insights. In these situations, you can investigate your forecasts from the My expectations tab and select log.

One way that you can acquire insights connected with blunders and alerts produced by your out-of-box forecasts is through the Input information convenience report. The report is accessible after a model has finished its preparation cycle. It’s made for each model independently, notwithstanding in the event that it is finished effectively or not. It likewise gives proposals on the best way to work on the model’s exhibition.

After an out-of-box model has finished its preparation step, view the report:

Select the ovals close to the model name and pick the Input information ease of use report.
Select the situation with a model and select See Input information convenience report in the side sheet.
Select one of the models in the rundown and select the Input information convenience report in the order bar.
Open the model outcomes page and select the Input information convenience report in the order bar.

30.) What is the process to configure the customer churn model in the Customer Data Platform?

The cycle to design the customer churn model is as following:

Name model – Specifies the name of your expectation model that will be shown in Audience Insights and the name of the result substance that will be made to store information that is connected with your forecast.
Preferences – Defines what comprises agitate for your association, for example, how long to stand by after a membership end date before it’s viewed as beaten, and the time-frame for foreseeing weakening before a membership end date.
Add required data – Defines the important fields that the model will use to foresee which clients are at a higher gamble of beat, including characterizing membership subtleties and exercises that are utilized to help the membership.
Update plan – Defines how frequently to hold the model for your expectations.
Review and run – Allows you to audit the forecast subtleties prior to running the expectation interestingly.

Microsoft Customer Data Platform Specialist (MB-260) Practice Exam