DP-600 Implementing Analytics Solutions Using Microsoft Fabric Questions and Answers

Comprehensive Detailed Explanation

Step 1: Requirement

We need to compare the average miles per trip for:

Statutory holidays (when IsHoliday = 1)

Non-statutory holidays (when IsHoliday = 0)

Step 2: Formula for average miles per trip

Average miles per trip = total miles ÷ number of trips

Total miles → SUM(t.tripDistance)

Number of trips → COUNT(t.tripID)

So the calculation is:

(SUM(t.tripDistance) / COUNT(t.tripID)) AS MilesPerTrip

Step 3: Grouping

We need a comparison by holiday status.

So we must group the results by:

GROUP BY d.IsHoliday

This ensures we get two rows: one for IsHoliday = 1 and one for IsHoliday = 0.

Step 4: Final Query

SELECT

d.IsHoliday,

(SUM(t.tripDistance) / COUNT(t.tripID)) AS MilesPerTrip

FROM DimDate d

INNER JOIN Trips t ON d.DateID = t.DateID

GROUP BY d.IsHoliday;

Why This is Correct

The formula ensures average miles per trip.

Grouping ensures comparison between holidays vs non-holidays.

Efficient aggregation, minimal computation.

References

Aggregate functions in T-SQL

GROUP BY clause

Comprehensive Detailed Explanation

We have a KQL table (TaxiData) with columns:

VendorID

tpep_pickup_datetime (timestamp)

payment_type

total_amount

The requirement:

Create a new column FirstPickupDateTime

It should contain the first pickup timestamp per hour

Partitioning should be done by payment_type

Step 1: Which windowing function?

row_cumsum → running cumulative sum (not needed here).

row_rank_dense → assigns ranks without gaps, but does not guarantee minimum value only.

row_rank_min → gives the first/minimum value in each window partition. ✅ Correct.

row_window_session → sessionization of events, not required.

So, the correct function is row_rank_min.

Step 2: Which comparison operator?

We need to select the row where the rank = 1 (the first per partition).

So the correct operator is == (equals).

Step 3: Partitioning

The KQL query should partition by:

bin(tpep_pickup_datetime, 1h) → buckets data into 1-hour windows

payment_type → partitions further by payment type

Completed KQL Query

TaxiData

| sort by tpep_pickup_datetime asc, payment_type asc

| extend FirstPickupDateTime = row_rank_min(tpep_pickup_datetime, 1h, 0m, payment_type)

| where FirstPickupDateTime == 1

This assigns a rank within each 1-hour, per-payment-type window, then keeps the first pickup timestamp.

Why This Works

row_rank_min → ensures we capture the first occurrence in each hour.

== → filters only the first row per partition.

bin(..., 1h) ensures grouping is by hour.

References

Kusto row_rank_min() function

KQL window functions

The relationship between OrderItem and Product must be based on: Both the CompanyID and the ProductID columns

The Company entity must be: Denormalized into the Customer and Product entities

In a dimensional model, the relationships are typically based on foreign key constraints between the fact table (OrderItem) and dimension tables (Product, Customer, Date). Since CompanyID is present in both the OrderItem and Product tables, it acts as a foreign key in the relationship. Similarly, ProductID is a foreign key that relates these two tables. To enable analysis by date, product, and customer, the Company entity would need to be denormalized into the Customer and Product entities to ensure that the relevant company information is available within those dimensions for querying and reporting purposes.

References =

Dimensional modeling

Star schema design

The KEEPFILTERS function modifies the way filters are applied in calculations done through the CALCULATE function. It can be particularly beneficial to replace the FILTER function with KEEPFILTERS when the filter context is being overridden by nested CALCULATE functions, which may remove filters that are being applied on a column. This can potentially reduce execution time because KEEPFILTERS maintains the existing filter context and allows the nested CALCULATE functions to be evaluated more efficiently.

To ensure that users assigned the SalesRegionManager role cannot see the Address column in the SalesAddress table, follow these steps in sequence:

Open the model in Tabular Editor.

Select the Address column in SalesAddress.

Set Object Level Security to None for SalesRegionManager.

The correct query to use in order to return data for the year 2023 that displays ProductID, ProductName, and has a summarized Amount greater than 10,000 is Option B. The reason is that it uses the GROUP BY clause to organize the data by ProductID and ProductName and then filters the result using the HAVING clause to only include groups where the sum of Amount is greater than 10,000. Additionally, the DATEPART(YEAR, SaleDate) = '2023' part of the HAVING clause ensures that only records from the year 2023 are included. References = For more information, please visit the official documentation on T-SQL queries and the GROUP BY clause at T-SQL GROUP BY.

Step 1 – Understanding star schema design

Fact tables → contain transactional, numeric, and foreign key columns (e.g., TransactionID, Date, SalesAmount, ProductID).

Dimension tables → contain descriptive attributes and business keys (e.g., ProductID, ProductName, ProductColor).

Step 2 – Column classification

TransactionID → Fact (surrogate key for the fact table).

Date → Goes into a Date Dimension (not into DimProduct).

ProductID → Business key → belongs in DimProduct.

ProductColor → Descriptive attribute → belongs in DimProduct.

ProductName → Descriptive attribute → belongs in DimProduct.

SalesAmount → Fact (measure, belongs in FactSales).

Step 3 – Select DimProduct columns

So DimProduct should include:

ProductID

ProductName

ProductColor

That matches option A.

To identify the slowest DAX query and analyze the time it spends in the formula engine compared to the storage engine, you should perform the following actions in sequence:

From Performance analyzer, capture a recording.

View the Server Timings tab.

Enable Query Timings and Server Timings. Run the query.

View the Query Timings tab.

Sort the Duration (ms) column in descending order by DAX query time.

In Microsoft Fabric, the OneLake endpoint allows users with appropriate permissions to read data from a lakehouse, leveraging the unified data storage system. Since DBUser has been granted access to Lakehouse1, they can utilize the OneLake endpoint for reading data. For querying, the OneLake file explorer provides a user interface to interact with and query data within the lakehouse, aligning with DBUser's access rights without requiring broader tenant permissions.

Based on the duration breakdown provided, the major contributor to the rendering duration is categorized as "Other," which is significantly higher than DAX Query and Visual display times. This suggests that the issue is less likely with the DAX calculation or visual rendering times and more likely related to model performance or the complexity of the visual. However, of the options provided, optimizing the DAX query can be a crucial step, even if "Other" factors are dominant. Using DAX Studio, you can analyze and optimize the DAX queries that power your visuals for performance improvements. Here’s how you might proceed:

Open DAX Studio and connect it to your Power BI report.

Capture the DAX query generated by the Orders By Date visual.

Use the Performance Analyzer feature within DAX Studio to analyze the query.

Look for inefficiencies or long-running operations.

Optimize the DAX query by simplifying measures, removing unnecessary calculations, or improving iterator functions.

Test the optimized query to ensure it reduces the overall duration.

ISEMPTY(CALCULATETABLE('Order Item')) checks if there are no related rows in 'Order Item' for a customer. Wrapping this in NOT returns true if the customer has at least one order. This avoids calculating row counts and instead just checks existence, which is more efficient for performance. This reduces execution time and meets the goal.

VACUUM in Delta Lake removes unreferenced files older than a retention period (default 7 days, but configurable, e.g., 30 days).

It ensures that the transaction log remains consistent and ACID properties are maintained.

OPTIMIZE with V-Order or Z-Order improves file organization and query performance, but it does not remove unused files.

Deleting files manually from OneLake would break the transaction log consistency.

Correct answer: C. Run the VACUUM command.

To validate the data seen by the HR manager, you should use the 'Test as role' feature in Power BI service. This allows you to see the data exactly as it would appear for the HR role, considering the dynamic RLS setup. Here is how you would proceed:

Navigate to the Power BI service and locate Model1.

Access the dataset settings for Model1.

Find the security/RLS settings where you configured the roles.

Use the 'Test as role' feature to simulate the report viewing experience as the HR role.

Review the data and the filters applied to ensure that the RLS is functioning correctly.

If discrepancies are found, adjust the RLS expressions or the role membership as needed.

Comprehensive Detailed Explanation

We need to query a KQL database table named Readings to generate output that includes both the current reading and the previous reading values and datetime for each row.

Step 1: Understanding Requirements

The output table shows:

City, Area, MeterReading, Datetime

PrevMeterReading, PrevDatetime

The prev() function in KQL is used to access the value of a column from the previous row (based on order).

Step 2: Query Construction

We start with filtering:

Readings

| where City == "Copenhagen"

| sort by Datetime

To add columns that calculate the previous reading and previous datetime, we use extend:

| extend PrevMeterReading = prev(MeterReading), PrevDatetime = prev(Datetime)

extend creates new columns.

prev() pulls the previous row’s values.

Finally, we only want specific columns in the output. For this, we use project:

| project City, Area, MeterReading, Datetime, PrevMeterReading, PrevDatetime

Step 3: Completed Query

Readings

| where City == "Copenhagen"

| sort by Datetime

| extend PrevMeterReading = prev(MeterReading), PrevDatetime = prev(Datetime)

| project City, Area, MeterReading, Datetime, PrevMeterReading, PrevDatetime

Why This is Correct

extend adds calculated columns.

prev() gives access to the previous row.

project selects only the required columns.

This matches exactly the table shown in the question.

References

Kusto Query Language - extend operator

Kusto Query Language - project operator

prev() function in KQL

The PoC is planned to be completed using a Fabric trial capacity, which implies that users involved in the PoC should be able to try paid features. However, this should be limited to specific security groups involved in the PoC to prevent the entire organization from accessing these features before the trial is proven successful (A). The ability for users to create Fabric items should also be enabled for specific security groups to ensure that only the relevant team members participating in the PoC can create items in the Fabric environment (E).

You should use CREATE VIEW to make the pricing group logic available for T-SQL queries.

The CASE statement should be used to determine the pricing group based on the list price.

The T-SQL statement should create a view that classifies products into pricing groups based on the list price. The CASE statement is the correct conditional logic to assign each product to the appropriate pricing group. This view will standardize the pricing group logic across different databases and semantic models.

To meet the technical requirement that data loading activities must ensure the raw and cleansed data is updated completely before populating the dimensional model, you would need a mechanism that allows for ordered execution. A pipeline in Microsoft Fabric with dependencies set between activities can ensure that activities are executed in a specific sequence. Once set up, the pipeline can be scheduled to run at the required intervals (hourly or daily depending on the data source).

For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made based on security and performance considerations as per the case study provided.

Authentication method: The data should be accessed securely, and given that row-level security (RLS) is required for users executing T-SQL queries, you should use an authentication method that supports RLS. Service principal authentication is suitable for automated and secure access to the data, especially when the access needs to be controlled programmatically and is not tied to a specific user's credentials.

Mode: The report needs to show data as soon as it is updated in the data store, and it should only contain data from the current and previous year. DirectQuery mode allows for real-time reporting without importing data into the model, thus meeting the need for up-to-date data. It also allows for RLS to be implemented and enforced at the data source level, providing the necessary security measures.

Based on these considerations, the selections should be:

Authentication method: Service principal authentication

Mode: DirectQuery

You need to recommend which type of Fabric capacity SKU meets the data analytics requirements for the Research division.

Requirement: “The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing.”

Fabric capacity SKUs:

F (Fabric) = dedicated Fabric capacity, available on pay-as-you-go with per-minute billing.

P (Premium) and EM are Power BI capacities (not Fabric-native).

A refers to Azure Analysis Services capacity.

The only correct option is F SKU.

Comprehensive Detailed Explanation

When assigning permissions to roles in a Fabric workspace for analytics workloads, you must align permissions with the responsibilities and tools typically used by each persona:

1. Data Engineers

Their primary tasks include building and transforming data pipelines, as well as making datasets usable by others.

The appropriate permission is Build Reports on the default dataset, because this allows them to create and manage reports using the curated semantic model while preparing data for analysts and scientists.

2. Data Analysts

Analysts work heavily with Apache Spark for exploratory analysis, cleansing, and shaping data.

The correct permission is Read All Apache Spark, as this grants them the ability to query and analyze Spark-based data directly without elevated permissions that are unnecessary for their role.

3. Data Scientists

Data scientists often use SQL analytics endpoints to run queries, train models, and integrate data into machine learning workflows.

The correct permission is Read All SQL analytics endpoint data, since it enables direct programmatic access to the SQL endpoint, which is needed for advanced modeling and experimentation.

Summary of Selections:

DataEngineers → Build Reports on the default dataset

DataAnalysts → Read All Apache Spark

DataScientists → Read All SQL analytics endpoint data

References (Microsoft Fabric – DP-600 exam scope):

Workspace roles and permissions in Microsoft Fabric

SQL analytics endpoint in Fabric

Apache Spark in Microsoft Fabric

Both a dataflow (A) and a Stored procedure activity in a pipeline (B) are capable of creating and populating a date dimension table. A dataflow can perform the transformation needed to create the date dimension, and it aligns with the preference for using low-code tools for data ingestion when possible. A Stored procedure could be written to generate the necessary date dimension data and executed within a pipeline, which also adheres to the technical requirements for the PoC.

A lakehouse (C) should be recommended for the AnalyticsPOC workspace. It combines the capabilities of a data warehouse with the flexibility of a data lake. A lakehouse supports semi-structured and unstructured data and allows for T-SQL and Python read access, fulfilling the technical requirements outlined for Litware. References = For further understanding, Microsoft's documentation on the lakehouse architecture provides insights into how it supports various data types and analytical operations.

The measure should use the AVERAGE function to calculate the average value.

It should reference the Response Value column from the 'Survey' table.

The 'Number of months' should be used to define the period for the average calculation.

To calculate the average overall satisfaction score using DAX, you would need to use the AVERAGE function on the response values related to satisfaction questions. The DATESINPERIOD function will help in calculating the rolling average over the last 12 months.

For ingesting customer data into the data store in the AnalyticsPOC workspace, a dataflow (D) should be recommended. Dataflows are designed within the Power BI service to ingest, cleanse, transform, and load data into the Power BI environment. They allow for the low-code ingestion and transformation of data as needed by Litware's technical requirements. References = You can learn more about dataflows and their use in Power BI environments in Microsoft's Power BI documentation.

Comprehensive Detailed Explanation

The question asks: Which syntax should you use in a Fabric notebook to access the Research division data for Productline1?

Key Background from the Case

In Productline1ws, a lakehouse named Lakehouse1 is created.

In Lakehouse1, a shortcut is created to storage1, named ResearchProduct.

Storage1 contains the Research division data for Productline1 in Delta format.

Requirement: All data in lakehouses must be presented as managed tables in Lakehouse explorer.

Analyzing the Syntax Options

Option A:

spark.sql("SELECT * FROM Lakehouse1.ResearchProduct")

This syntax directly queries the ResearchProduct shortcut within Lakehouse1 using Spark SQL.

Since the shortcut points to Delta data, Spark can directly query it.

This is the correct way to retrieve Productline1 data from Lakehouse1.

Option B:

spark.sql("SELECT * FROM Lakehouse1.productline1.ResearchProduct")

This introduces an extra schema-like path (productline1) that is not part of the shortcut name.

Incorrect, because the shortcut was created as ResearchProduct inside Lakehouse1, not under another schema.

Option C:

external_table('Tables/ResearchProduct')

external_table is not the correct way to access a Lakehouse shortcut.

Shortcuts in Lakehouses appear as tables and can be queried using Spark SQL directly.

Option D:

spark.sql("SELECT * FROM Lakehouse1.productline1.ResearchProduct")

Same issue as Option B, includes a schema path that does not exist.

Correct Choice

Since the shortcut to ResearchProduct was created inside Lakehouse1, and Spark SQL can query it directly, the correct syntax is:

spark.sql("SELECT * FROM Lakehouse1.ResearchProduct")

That matches Option A.

References

Microsoft Fabric Lakehouse – Shortcuts

Query data in a lakehouse using Spark SQL

Delta format support in Microsoft Fabric

In Azure DevOps workspace (project) security settings, role assignments determine access levels for groups:

Viewer (equivalent to the Readers security group): Grants read-only access to work items, code, pipelines, and other artifacts. Ideal for review-only scenarios where users need to view but not edit content.

Contributor (equivalent to the Contributors security group): Allows full edit and contribute privileges, such as updating work items, code reviews, and pipeline runs. Suitable for active participation in reviews that may involve feedback or changes.

Based on the group names implying review-focused access, Viewer fits Group1 for passive observation, while Contributor fits Group2 for interactive review tasks. These align with default Azure DevOps permissions for secure, least-privilege access.

 Calculation groups are a Tabular Object Model (TOM) feature.

 They cannot be authored in Power BI Desktop or the service.

 They are implemented using Tabular Editor.

Requirement: “The number of rows added to the Orders table during refreshes must be minimized.”

The Orders table has a sequential OrderID.

To minimize refresh size, you must only import new rows, not the entire table.

This requires retrieving the maximum OrderID currently loaded, then only loading rows with a greater OrderID.

So, the correct solution is to use a pipeline (ADF) with a dataflow to retrieve the maximum OrderID.

Answer: C. an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderID column in the destination lakehouse

You need to ensure that Contoso can use version control to meet the data analytics requirements and the general requirements.

Requirement: “All the semantic models and reports for the Research division must use version control that supports branching.”

Fabric supports Git integration (Azure Repos or GitHub) for semantic models and reports.

Storing in Data Lake Gen2 or OneDrive does not provide version control with branching.

The correct action is to integrate workspaces with a Git repository.

Answer: D. Modify the settings of the Research division workspaces to use an Azure Repos repository.

Comprehensive Detailed Explanation

From the case study:

Research division data for Productline2 is currently in CSV format in storage2.

Requirement: “All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.”

In Fabric lakehouses, managed tables are stored in Delta format inside the Tables folder.

Step 1: Reading the source

df = spark.read.format("csv") \

options(header="true", inferSchema="true") \

load("abfss://storage1.dfs.core.windows.net/files/productline2")

This correctly ingests the CSV source from ADLS Gen2.

Step 2: Writing to Lakehouse as a managed table

You must write the data in Delta format to ensure it is queryable and managed within the lakehouse.

The correct path is under Tables/, because this is where Fabric automatically manages Lakehouse managed tables.

The target table should be named productline2, so the correct path is:

df.write.mode("overwrite").format("delta").save("Tables/productline2")

Why not other options?

CSV or Parquet formats would not create a managed Lakehouse table; they would just create files.

Writing to productline2 directly (without Tables/) would store unmanaged files in the Lakehouse Files area, not managed tables.

Writing to Tables/research/productline2 adds an unnecessary subdirectory and is not the standard structure for managed tables.

Correct Final Code

df.write.mode("overwrite").format("delta").save("Tables/productline2")

References

Managed tables in Microsoft Fabric Lakehouse

Delta Lake support in Fabric

Spark write to Lakehouse

Comprehensive Detailed Explanation

The requirement is to logically group the Research division workspaces and support filtering in the OneLake data hub.

Why Domain?

Microsoft Fabric allows you to assign workspace domains (e.g., Finance, HR, Research).

This provides a logical grouping of workspaces within the organization.

Domains are the only supported way to enable filtering in OneLake data hub by department.

Capacity or tenant grouping would not allow filtering by department in OneLake.

Why OneLake data hub?

The OneLake data hub is the centralized interface to browse and discover data assets across the organization.

With workspace domains configured, users can filter directly in the OneLake data hub by domain (e.g., “Research”).

The Fabric Admin portal and Entra admin center are used for administration, not for workspace filtering in data discovery.

Correct Selection for the Hotspot

Grouping method: Domain

Tool: OneLake data hub

References

Workspace domains in Microsoft Fabric

Discover and filter data in OneLake data hub

Final Answer:

Grouping method = Domain

Tool = OneLake data hub