Tag: T-SQL

DP-700, Microsoft Certification, Microsoft Fabric June 3, 2026

Identify and resolve T-SQL errors (DP-700 Exam Prep)

This post is a part of the DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric Exam Prep Hub.
This topic falls under these sections:
Monitor and optimize an analytics solution (30–35%)
   --> Identify and resolve errors
      --> Identify and resolve T-SQL errors

Note that there are 10 practice questions (with answers) at the end of each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available from the hub's main page below the exam topics section.

Introduction

T-SQL (Transact-SQL) is one of the primary languages used in Microsoft Fabric for querying, transforming, loading, and managing data within Warehouses, SQL analytics endpoints, and other SQL-based workloads. As organizations increasingly use Fabric Warehouses and Lakehouses for analytics, data engineers must be able to identify, troubleshoot, and resolve T-SQL errors efficiently.

For the DP-700 exam, you should understand common T-SQL error types, methods for diagnosing failures, troubleshooting techniques, query optimization considerations, and best practices for preventing errors before they occur.

Understanding T-SQL Errors

A T-SQL error occurs when SQL code cannot execute successfully due to syntax problems, data issues, permissions, resource constraints, or logical mistakes.

Errors generally fall into several categories:

Syntax errors
Object-related errors
Data type conversion errors
Constraint violations
Permission errors
Runtime errors
Query performance issues
Transaction-related errors

Successful troubleshooting requires identifying which category the error belongs to.

Syntax Errors

Syntax errors occur when SQL statements violate T-SQL language rules.

Example

			
SELECT CustomerID CustomerName
FROM Customers

In this example, the comma between columns is missing.

Correct version:

			
SELECT CustomerID, CustomerName
FROM Customers

Common Syntax Issues

Missing commas
Missing parentheses
Incorrect keyword order
Misspelled SQL commands
Unclosed quotation marks
Invalid aliases

Troubleshooting Tips

Read the error message carefully.
Verify SQL keyword spelling.
Check punctuation.
Format code for readability.
Validate parentheses and quotes.

Object Name Errors

These occur when SQL references objects that do not exist or cannot be found.

Example

			
SELECT *
FROM CustomerData

If CustomerData does not exist:

Invalid object name 'CustomerData'

Common Causes

Incorrect table names
Misspelled object names
Dropped tables
Wrong schema references

Example:

			
SELECT *
FROM Sales.CustomerData

instead of:

			
SELECT *
FROM dbo.CustomerData

Troubleshooting Tips

Verify object existence.
Check schema names.
Review recent deployments.
Validate database context.

Column Name Errors

These occur when queries reference nonexistent columns.

Example

			
SELECT CustomerAge
FROM Customers

If CustomerAge does not exist:

Invalid column name 'CustomerAge'

Common Causes

Renamed columns
Typographical errors
Schema changes
Incorrect aliases

Resolution

Review table definitions and confirm column names.

Data Type Conversion Errors

These errors occur when SQL cannot convert data between incompatible types.

Example

SELECT CAST('ABC' AS INT)

Result:

Conversion failed when converting value 'ABC' to data type int.

Common Causes

Invalid numeric values
Incorrect date formats
String-to-number conversions
String-to-date conversions

Safer Approach

Use:

SELECT TRY_CAST('ABC' AS INT)

Result:

NULL

instead of an error.

Best Practice

Use:

TRY_CAST()
TRY_CONVERT()
Data validation logic

Null-Related Errors

Null values frequently cause unexpected query behavior.

Example

			
SELECT Revenue / Quantity
FROM Sales

If Quantity contains zero or NULL values:

Divide-by-zero errors
Unexpected NULL results

Resolution

Use defensive coding:

			
SELECT Revenue / NULLIF(Quantity,0)
FROM Sales

SELECT ISNULL(Quantity,1)

when appropriate.

Constraint Violations

Constraints enforce data integrity.

Common constraints:

Primary keys
Foreign keys
Unique constraints
Check constraints
NOT NULL constraints

Example

			
INSERT INTO Customers
(CustomerID)
VALUES (100)

If CustomerID already exists:

Violation of PRIMARY KEY constraint

Resolution

Check existing data.
Validate uniqueness.
Use MERGE or UPSERT patterns.

Foreign Key Errors

Example

Orders table references Customers table.

Attempting to insert:

			
INSERT INTO Orders
(CustomerID)
VALUES (9999)

when CustomerID 9999 does not exist produces:

Foreign key constraint violation

Resolution

Load parent tables first.

Verify referential integrity before loading.

Permission Errors

Users may not have required access rights.

Example

			
SELECT *
FROM SalesData

Error:

The SELECT permission was denied.

Common Causes

Missing permissions
Incorrect roles
Revoked access
Workspace security changes

Troubleshooting

Verify:

Workspace roles
SQL permissions
Object-level permissions

Runtime Errors

Runtime errors occur while queries execute successfully syntactically but fail during processing.

Examples:

Divide-by-zero
Overflow errors
Resource exhaustion
Timeout failures

Example

SELECT 100 / 0

Produces:

Divide by zero error encountered.

Resolution

Validate input values before execution.

Transaction Errors

Transactions ensure consistency during data modifications.

Example

			
BEGIN TRANSACTION
UPDATE Inventory
SET Quantity = Quantity - 10
COMMIT

If an error occurs before COMMIT, the transaction may remain open.

Best Practice

Use:

			
BEGIN TRY
   BEGIN TRANSACTION
   -- work here
   COMMIT TRANSACTION
END TRY
BEGIN CATCH
   ROLLBACK TRANSACTION
END CATCH

		

This pattern is commonly tested on certification exams.

Query Timeout Errors

Long-running queries may exceed execution limits.

Symptoms:

Query never completes
Timeout messages
Resource throttling

Common causes:

Large table scans
Missing filters
Excessive joins
Poor query design

Troubleshooting

Review:

Execution plans
Join strategies
Data volume
Filtering logic

Resource and Capacity Issues

Fabric workloads share compute resources.

Symptoms include:

Slow execution
Query failures
Capacity throttling

Common causes:

Insufficient capacity
Excessive concurrency
Large transformations

Resolution

Scale capacity
Optimize queries
Reduce unnecessary processing

Troubleshooting T-SQL Errors Systematically

A structured approach is essential.

Step 1: Read the Error Message

Many errors explicitly identify:

Object names
Column names
Data types
Constraint violations

Step 2: Identify the Error Category

Determine whether the issue is:

Syntax
Permissions
Data
Performance
Transaction-related

Step 3: Reproduce the Problem

Use smaller datasets when possible.

Step 4: Isolate the Failure

Test:

Individual joins
Filters
Aggregations
Conversions

Step 5: Validate Assumptions

Confirm:

Tables exist
Columns exist
Data types match
Permissions are correct

Using TRY…CATCH for Error Handling

T-SQL supports structured exception handling.

Example:

			
BEGIN TRY
    SELECT 100 / 0
END TRY
BEGIN CATCH
    PRINT ERROR_MESSAGE()
END CATCH

		

Benefits:

Better diagnostics
Controlled error handling
Cleaner ETL workflows

Performance-Related Error Diagnosis

Not all issues generate explicit errors.

Poor performance may indicate:

Missing filters
Excessive joins
Cartesian products
Inefficient aggregations

Watch for:

Long-running queries
Excessive scans
Resource bottlenecks

Common DP-700 Exam Scenarios

You may encounter questions involving:

Invalid object names
Data conversion failures
Permission denials
Constraint violations
Query timeouts
Transaction rollbacks
Divide-by-zero errors
Schema changes breaking SQL code
TRY_CAST versus CAST behavior
TRY…CATCH implementation

Best Practices

Validate Data Before Loading

Prevent conversion failures.

Use TRY_CAST

Avoid runtime conversion errors.

Implement Error Handling

Use TRY…CATCH blocks.

Load Data in Correct Order

Prevent foreign key violations.

Follow Naming Standards

Reduce object-reference errors.

Monitor Query Performance

Identify bottlenecks early.

Test Incrementally

Validate code before production deployment.

Document Schema Changes

Prevent downstream query failures.

DP-700 Exam Tips

Remember:

Syntax errors occur before execution.
Runtime errors occur during execution.
TRY_CAST returns NULL rather than failing.
Foreign key errors typically indicate missing parent records.
Permission errors require security review.
TRY…CATCH provides structured error handling.
Constraint violations protect data integrity.
Timeout errors often indicate performance problems.
Transaction handling should include rollback logic.
Many troubleshooting questions begin by examining the exact error message.

Practice Exam Questions

Question 1

A query returns the error:

Invalid object name 'SalesData'

What is the most likely cause?

A. The referenced table does not exist or is incorrectly named.

B. A primary key violation occurred.

C. The query exceeded memory limits.

D. A data type conversion failed.

Correct Answer: A

Explanation: This error indicates SQL cannot locate the referenced object. Verify table names, schemas, and database context.

Question 2

What is the primary advantage of using TRY_CAST instead of CAST?

A. It executes faster.

B. It automatically creates indexes.

C. It prevents duplicate records.

D. It returns NULL when conversion fails instead of generating an error.

Correct Answer: D

Explanation: TRY_CAST safely handles invalid conversions by returning NULL rather than stopping query execution.

Question 3

A query produces:

Invalid column name 'CustomerAge'

What should you check first?

A. Query timeout settings

B. Whether the referenced column exists in the table

C. Capacity utilization

D. Transaction isolation level

Correct Answer: B

Explanation: Invalid column errors typically indicate a misspelled, renamed, or nonexistent column.

Question 4

Which type of constraint prevents duplicate values from being inserted into a key column?

A. Foreign key constraint

B. Check constraint

C. NOT NULL constraint

D. Primary key constraint

Correct Answer: D

Explanation: Primary key constraints enforce uniqueness and prevent duplicate key values.

Question 5

A user receives:

The SELECT permission was denied.

What is the most likely cause?

A. Missing access permissions

B. Invalid syntax

C. Data type mismatch

D. Foreign key violation

Correct Answer: A

Explanation: Permission errors occur when a user lacks required access rights.

Question 6

Which statement is most likely to generate a divide-by-zero error?

SELECT COUNT(*)

SELECT Revenue / Quantity

where Quantity contains zero values.

SELECT TOP 10 *

SELECT CustomerID

Correct Answer: B

Explanation: Dividing by a value of zero generates a runtime error.

Question 7

A data engineer wants transactions to automatically roll back when an error occurs. Which approach is recommended?

A. Use nested views

B. Use temporary tables

C. Use TRY…CATCH with ROLLBACK TRANSACTION

D. Use SELECT DISTINCT

Correct Answer: C

Explanation: TRY…CATCH combined with rollback logic is a standard error-handling pattern.

Question 8

A foreign key violation occurs during an INSERT operation. What is the most likely explanation?

A. A referenced parent record does not exist.

B. A column name is misspelled.

C. A query timeout occurred.

D. An index is fragmented.

Correct Answer: A

Explanation: Foreign key constraints require matching parent records.

Question 9

A query executes successfully but takes several minutes to complete. Which category best describes the issue?

A. Syntax error

B. Constraint violation

C. Permission error

D. Performance problem

Correct Answer: D

Explanation: Long execution times generally indicate optimization or resource issues rather than functional errors.

Question 10

What should be your first troubleshooting step when a T-SQL query fails?

A. Rebuild all indexes

B. Read and analyze the error message

C. Increase Fabric capacity

D. Delete and recreate the table

Correct Answer: B

Explanation: The error message often identifies the exact source of the problem and should always be reviewed first.

Go to the DP-700 Exam Prep Hub main page.

DP-700, Microsoft Certification, Microsoft Fabric June 3, 2026

Choose Between Dataflows Gen2, Notebooks, KQL, and T-SQL for data transformation (DP-700 Exam Prep)

This post is a part of the DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric Exam Prep Hub.
This topic falls under these sections:
Ingest and transform data (30–35%)
   --> Ingest and transform batch data
      --> Choose Between Dataflows Gen2, Notebooks, KQL, and T-SQL for data transformation

Note that there are 10 practice questions (with answers) at the end of each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available from the hub's main page below the exam topics section.

Introduction

Microsoft Fabric provides multiple technologies for transforming data. One of the most common challenges for a Data Engineer is determining which transformation tool is best suited for a specific business requirement.

The DP-700 exam frequently tests your ability to select the appropriate transformation technology based on:

Data volume
Data complexity
Required programming skills
Data source type
Performance requirements
Real-time versus batch processing needs
User expertise
Maintainability

The four most important transformation technologies covered in the exam are:

Dataflows Gen2
Notebooks
KQL
T-SQL

Although all four can transform data, they are optimized for different workloads and use cases.

Understanding their strengths, limitations, and ideal scenarios is critical for success on the DP-700 exam.

Overview of Transformation Technologies

Technology	Primary Purpose	Best For
Dataflows Gen2	Low-code ETL	Business-friendly transformations
Notebooks	Advanced engineering and Spark processing	Large-scale data engineering
T-SQL	Relational transformations	Warehouses and SQL workloads
KQL	Real-time analytics and telemetry processing	Logs and streaming data

Dataflows Gen2

What Are Dataflows Gen2?

Dataflows Gen2 are low-code data transformation tools within Microsoft Fabric that use Power Query.

They allow users to:

Connect to data sources
Clean data
Transform data
Load data into Fabric destinations

without writing significant amounts of code.

Transformation Engine

Dataflows Gen2 use:

Power Query
M Language (behind the scenes)

Most transformations are performed through a graphical interface.

Typical Transformations

Examples include:

Renaming columns
Removing duplicates
Filtering rows
Merging datasets
Splitting columns
Data type conversions
Calculated columns

When to Use Dataflows Gen2

Choose Dataflows Gen2 when:

Low-code development is desired
Data volumes are moderate
Business analysts participate in development
Transformations are relatively straightforward
Self-service data preparation is required

Examples:

Preparing Excel data
Cleaning CSV files
Combining multiple business datasets
Standard ETL processes

Advantages

Low-Code Experience

Minimal coding required.

Large Connector Library

Supports numerous source systems.

Easy Maintenance

Visual transformation steps are easier to understand.

Integration with Fabric

Loads directly into:

Lakehouses
Warehouses
Other Fabric destinations

Limitations

Less Flexible

Complex logic may become difficult.

Not Ideal for Very Large Data Volumes

Spark-based solutions often scale better.

Limited Advanced Programming

Compared to notebooks.

Notebooks

What Are Notebooks?

Notebooks are code-based development environments that support:

PySpark
Python
Scala
Spark SQL
R

within Microsoft Fabric.

Transformation Engine

Notebooks execute on Spark clusters.

This enables:

Distributed processing
Parallel execution
Large-scale transformations

Typical Transformations

Examples:

Complex joins
Data enrichment
Machine learning preparation
Feature engineering
Data quality validation
Custom business logic

When to Use Notebooks

Choose notebooks when:

Large data volumes exist
Spark processing is required
Advanced transformations are needed
Custom programming is necessary
Machine learning integration is planned

Examples:

Processing billions of records
Data science workflows
Medallion architecture pipelines
Complex transformations

Advantages

Massive Scalability

Handles large datasets efficiently.

Flexible Programming

Supports multiple languages.

Machine Learning Integration

Works with Spark ML libraries.

Advanced Data Engineering

Ideal for enterprise-scale pipelines.

Limitations

Requires Coding Skills

Less accessible for business users.

More Complex Development

Compared to Dataflows Gen2.

T-SQL

What Is T-SQL?

T-SQL (Transact-SQL) is Microsoft’s extension of SQL.

Fabric Warehouses and SQL endpoints support T-SQL for:

Querying
Transforming
Managing relational data

Transformation Techniques

Common operations include:

			
SELECT
JOIN
GROUP BY
CASE
CTE
MERGE
WINDOW FUNCTIONS

		

When to Use T-SQL

Choose T-SQL when:

Data resides in a Warehouse
Relational transformations are required
SQL expertise already exists
Dimensional models are being built

Examples:

Fact table loading
Dimension updates
Data warehouse ETL
Reporting data preparation

Advantages

Familiar Language

Widely used by data professionals.

Excellent Relational Processing

Optimized for structured data.

Strong Performance

Particularly for warehouse workloads.

Easy Integration

Works naturally with BI tools.

Limitations

Less Suitable for Unstructured Data

Not ideal for files and raw data.

Limited Distributed Processing

Compared to Spark.

KQL

What Is KQL?

Kusto Query Language (KQL) is designed for:

Log analytics
Telemetry analysis
Real-time data processing
Event analytics

KQL is commonly used in:

KQL Databases
Eventhouse
Real-Time Intelligence

Typical Transformations

Examples include:

Filtering events
Aggregations
Pattern detection
Time-series analysis
Stream transformations

When to Use KQL

Choose KQL when:

Working with telemetry data
Processing logs
Analyzing streaming events
Building real-time dashboards

Examples:

Sensor monitoring
Application logs
Security analytics
Operational monitoring

Advantages

Optimized for Time-Series Data

Excellent for event-driven workloads.

Fast Query Performance

Handles large event volumes efficiently.

Real-Time Analytics

Supports low-latency analysis.

Limitations

Not a General ETL Tool

Less suitable for traditional batch ETL.

Not Designed for Dimensional Modeling

Warehouses are generally better for reporting models.

Comparing Transformation Technologies

Requirement	Dataflows Gen2	Notebooks	T-SQL	KQL
Low-Code Development	Excellent	Poor	Moderate	Moderate
Large-Scale Processing	Moderate	Excellent	Good	Excellent
Relational Transformations	Moderate	Good	Excellent	Limited
Streaming Analytics	Limited	Moderate	Poor	Excellent
Machine Learning Support	Poor	Excellent	Poor	Limited
Telemetry Analytics	Poor	Moderate	Poor	Excellent
Business User Friendly	Excellent	Poor	Moderate	Moderate
Advanced Programming	Limited	Excellent	Moderate	Limited

Decision Framework

Choose Dataflows Gen2 When:

Low-code ETL is preferred
Business users are involved
Data volumes are moderate
Transformations are straightforward

Choose Notebooks When:

Spark processing is required
Data volumes are large
Complex transformations exist
Machine learning is involved

Choose T-SQL When:

Working with a Warehouse
Building dimensional models
SQL skills are available
Data is highly structured

Choose KQL When:

Processing logs
Analyzing telemetry
Supporting streaming analytics
Building operational monitoring solutions

Common DP-700 Scenario Questions

Scenario 1

A business analyst needs to combine Excel spreadsheets and remove duplicate rows using a visual interface.

Best choice:

Dataflows Gen2

Scenario 2

A data engineer must transform billions of records stored in a Lakehouse.

Best choice:

Notebook

Scenario 3

A warehouse team must populate fact and dimension tables.

Best choice:

T-SQL

Scenario 4

An operations team analyzes millions of application log events each hour.

Best choice:

KQL

Scenario 5

A machine learning team requires custom Python transformations.

Best choice:

Notebook

Exam Tips

Many DP-700 questions are not asking what can perform a transformation, but what should perform the transformation.

Remember these associations:

Requirement	Best Choice
Visual ETL	Dataflows Gen2
Spark processing	Notebook
Data warehouse transformations	T-SQL
Telemetry and logs	KQL
Machine learning preparation	Notebook
Self-service data preparation	Dataflows Gen2
Streaming analytics	KQL

Practice Exam Questions

Question 1

A business analyst needs to cleanse CSV files using a graphical interface with minimal coding. Which transformation technology should be used?

A. T-SQL

B. Notebook

C. KQL

D. Dataflows Gen2

Answer: D

Explanation

Dataflows Gen2 provide a low-code, visual interface that is ideal for business users and simple ETL processes.

Question 2

A data engineer must process several billion records stored in a Lakehouse using distributed computing.

Which option should be selected?

A. Notebook

B. Dataflows Gen2

C. T-SQL

D. KQL

Answer: A

Explanation

Notebooks leverage Spark for distributed processing and are designed for large-scale data transformations.

Question 3

Which technology is specifically optimized for transforming and analyzing telemetry and log data?

A. Dataflows Gen2

B. Notebook

C. KQL

D. T-SQL

Answer: C

Explanation

KQL is designed for log analytics, telemetry processing, and real-time operational analytics.

Question 4

A team is loading dimension and fact tables within a Fabric Warehouse.

Which transformation technology is most appropriate?

A. Notebook

B. Dataflows Gen2

C. KQL

D. T-SQL

Answer: D

Explanation

T-SQL is the preferred technology for relational transformations in Fabric Warehouses.

Question 5

A company requires machine learning feature engineering using Python libraries.

Which technology should be selected?

A. Notebook

B. Dataflows Gen2

C. T-SQL

D. KQL

Answer: A

Explanation

Notebooks support Python, Spark, and machine learning frameworks, making them ideal for feature engineering.

Question 6

Which technology relies primarily on Power Query transformations?

A. Notebook

B. Dataflows Gen2

C. T-SQL

D. KQL

Answer: B

Explanation

Dataflows Gen2 use Power Query and the M language behind the scenes for data transformations.

Question 7

An operations team needs to perform real-time aggregations on streaming sensor data.

Which option should be used?

A. Dataflows Gen2

B. Notebook

C. KQL

D. T-SQL

Answer: C

Explanation

KQL is optimized for real-time event processing and telemetry analysis.

Question 8

A data engineer needs maximum flexibility to implement custom business logic across multiple data sources.

Which technology is most appropriate?

A. KQL

B. Dataflows Gen2

C. T-SQL

D. Notebook

Answer: D

Explanation

Notebooks provide the highest degree of customization through programming languages such as Python and PySpark.

Question 9

A team already has extensive SQL expertise and needs to transform highly structured relational data in a Warehouse.

Which option is best?

A. Notebook

B. T-SQL

C. Dataflows Gen2

D. KQL

Answer: B

Explanation

T-SQL is optimized for relational transformations and leverages existing SQL skills.

Question 10

Which technology is generally the most business-user-friendly option for creating batch data transformation processes?

A. Notebook

B. KQL

C. T-SQL

D. Dataflows Gen2

Answer: D

Explanation

Dataflows Gen2 provide a visual, low-code experience that is easier for business users and citizen developers than code-based solutions.

DP-700 Exam Summary

When deciding between transformation technologies, focus on the primary workload:

Dataflows Gen2 → Low-code ETL and self-service data preparation
Notebooks → Spark, large-scale processing, advanced engineering, and machine learning
T-SQL → Relational transformations and warehouse development
KQL → Telemetry, logs, time-series analytics, and real-time event processing

A common DP-700 exam strategy is to identify the keywords in the scenario: