Free Practice Questions for Microsoft DP-600 Exam

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.

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

Yes, the DESCRIBE HISTORY statement does meet the goal. It provides information on the history of operations, including maintenance tasks, performed on a Delta table. References = The functionality of the DESCRIBE HISTORY statement can be verified in the Delta Lake documentation.

When creating a shortcut to an Azure Data Lake Storage Gen2 account in a lakehouse, you should use the abfss (Azure Blob File System Secure) connection string and the dfs (Data Lake File System) endpoint. The abfss is used for secure access to Azure Data Lake Storage, and the dfs endpoint indicates that the Data Lake Storage Gen2 capabilities are to be used.

To reduce memory usage and refresh time, splitting the OrderDateTime into separate date and time columns (A) can help optimize the model because date/time data types can be more memory-intensive than separate date and time columns. Moreover, replacing TotalSalesAmount with a measure (D) instead of a calculated column ensures that the calculation is performed at query time, which can reduce the size of the model as the value is not stored but calculated on the fly. References = The best practices for optimizing Power BI models are detailed in the Power BI documentation, which recommends using measures for calculations that don't need to be stored and adjusting data types to improve performance.

In Microsoft Fabric, semantic models (formerly datasets) can be managed using external tools like Tabular Editor. To push or publish changes directly to a Fabric workspace from Tabular Editor, the workspace must support XMLA endpoint connectivity with read-write access.

Explanation of Each Option

A. For Workspace1, enable Git integration.

Git integration allows source control and versioning of items in a Fabric workspace. It is intended for lifecycle management of artifacts and does not provide the connectivity required for Tabular Editor to publish semantic model changes.

B. For Workspace1, create a managed private endpoint.

A managed private endpoint provides secure connectivity between Fabric and external data sources. It is not related to the ability to publish semantic models through XMLA.

C. For Model1, enable external sharing.

External sharing governs who can access the model outside the organization. This does not affect publishing workflows from developer tools.

D. For Capacity1, set XMLA Endpoint to Read Write.

This is the correct choice. The XMLA endpoint provides a programmatic connection point for external tools like Tabular Editor. By default, the XMLA endpoint is enabled only in read-only mode. Changing the setting to Read Write allows developers to push changes, update metadata, and deploy semantic models directly from Tabular Editor into the Fabric workspace.

Summary

To publish changes to a Fabric semantic model using Tabular Editor, the XMLA endpoint of the assigned capacity must be set to Read Write. Without this configuration, Tabular Editor can only connect in read-only mode and cannot deploy updates.

References

Use the XMLA endpoint in Microsoft Fabric

Manage capacities in Microsoft Fabric

Tabular Editor and XMLA endpoint

When creating a OneLake shortcut to Amazon S3, authentication uses the standard AWS access key ID + secret access key pair.

SAS token is for Azure Storage, not AWS.

Certificate thumbprint is not used for S3.

Access ID is incorrect naming; the correct term is access key ID.

Correct answers: B and D.

Comprehensive Detailed Explanation

Step 1: Understanding the requirement

We need a KQL query that:

Reads data from Weatherdata.

Orders by Datetime.

Calculates Delta_temperature as the difference between the current row and the preceding row.

Returns the final columns: Datetime, Lat, Long, Temperature, Delta_temperature.

Step 2: Evaluate the provided statements

Weatherdata → Starts the query with the source table.

| sort by Datetime asc → Ensures chronological order, required for prev() to work correctly.

| extend Delta_temperature = Temperature - prev(Temperature) → Computes difference between the current row and the previous row’s temperature.

| project Datetime, Lat, Long, Temperature, Delta_temperature → Selects the required output columns.

Step 3: Incorrect choices (to avoid)

next(Temperature,1) → Would calculate the difference with the next row, not the previous.

prev(Temperature,2) → Would skip one row, giving the difference with two rows back, which is not needed.

summarize … by Datetime → Aggregates the data; not required here since we just need row-level differences.

Step 4: Final Correct Sequence

Weatherdata

| sort by Datetime asc

| extend Delta_temperature = Temperature - prev(Temperature)

| project Datetime, Lat, Long, Temperature, Delta_temperature

References

prev() function in KQL

extend operator in KQL

project operator in KQL

INSERT INTO … SELECT and SELECT INTO both physically copy all rows, which is heavy and not minimal.

CREATE TABLE AS SELECT (CTAS) also copies the data.

CREATE TABLE … AS CLONE OF creates a shallow clone in Fabric Warehouse, meaning it references the existing data without duplicating it, minimizing storage and copying.

Correct answer: B.

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