Microsoft Fabric Analytics Engineer (DP-600) Exam Dumps & Questions 2026 Microsoft Fabric Analytics Engineer (DP-600) Practice Tests 2026. Contains 800+ exam questions to pass the exam in first attempt. SkillCertPro offers real exam questions for practice for all major IT certifications. For a full set of 880 questions. Go to http s://skillcertpro.com/product/microsoft - fabric - analytics - engineer - dp - 600 - exam - questions/ SkillCertPro offers detailed explanations to each question which helps to understand the concepts better. It is recommended to score above 85% in SkillCertPro exams before attempting a real exam. SkillCertPro updates exam questions every 2 weeks. You will get life time access and life time free updates SkillCertPro assures 100% pass guarantee in first attempt. Below are the free 10 sample questions. Question 1: You have a Fabric tenant that contains a lakehouse named Lakehouse1. You have a large 1 TB dataset in an external data source. You need to recommend a method to ingest the dataset into Lakehouse1. The solution must provide the highest throughput. The solution must be suitable for developers who prefer the low-code/no-code option. What should you recommend? A. Use the Copy data activity of a pipeline to copy the data. B. The Copy data activity provides the best performance when copying data from large datasets. C. Use Dataflow Gen2 to import the files and load them to a table. D. Use Lakehouse explorer to upload the files directly. E. Use Fabric notebooks and PySpark to load the data to a DataFrame and write the results to a location in Lakehouse1. Answer: A Explanation: The correct answer is: A. Use the Copy data activity of a pipeline to copy the data. Explanation: The Copy Data activity in a data pipeline is the most efficient and recommended method for ingesting large datasets into a Fabric lakeh ouse. It‘s designed for high - throughput data transfers and provides several optimization techniques to accelerate the process. Here‘s why it‘s the best choice: High Throughput: The Copy Data activity can leverage parallel processing to transfer data in chunks, significantly reducing the overall ingestion time. Low-Code/No-Code: The activity provides a user-friendly interface for configuring the data source, destination, and transformation settings, making it easy to use without extensive coding knowledge. Optimized Data Transfer: It employs efficient data transfer protocols and compression techniques to minimize network traffic and improve performance. Built-in Performance Tuning: The activity provides various settings and configurations to optimize data transfer speed, including batch size, concurrency, and error handling. While other options like Dataflow Gen2, Lakehouse Explorer, and Spark notebooks can be used for data ingestion, they might not be as efficient or user- friendly for large-scale data transfers. Dataflow Gen2 is better suited for data transformation and integration, while Lakehouse Explorer and Spark notebooks are more suitable for smaller datasets or interactive data analysis. By using the Copy Data activity, you can ensure a fast and efficient data ingestion process, minimizing downtime and maximizing productivity. Question 2: You have a Fabric tenant that contains a lakehouse named Lakehouse1. A SELECT query from a managed Delta table in Lakehouse1 takes longer than expected to complete. The table receives new records daily and must keep change history for seven days. You notice that the table contains 1,000 Parquet files that are each 1 MB. You need to improve query performance and reduce storage costs. What should you do from Lakehouse explorer? A. Select Maintenance and run the OPTIMIZE command as well as the VACUUM command with a retention policy of seven days. B. Select Maintenance and run the OPTIMIZE command. C. Manually delete any files that have a creation date that is older than seven days. D. Select Maintenance and run the VACUUM command with a retention policy of seven days. Answer: A Explanation: The ideal file size for Fabric engines is between 128 MB and 1 GB. This improves query performance since it reduces the need to scan numerous small files. OPTIMIZE compacts and rewrites the files into fewer larger files. VACUUM removes older Parquet files that are no longer in use. While this reduces the storage size, it by itself does not reduce the number of active files that must be scanned. Question 3: You have a Fabric workspace named Workspace1 that contains a data pipeline named Pipeline1. You plan to use the Office 365 Outlook activity to send an email message each time Pipeline1 experiences issues with pipeline connectors. You need to connect the Office 365 Outlook activity to each main pipeline activity. The solution must minimize the number of email messages sent by the activity. Which connection should you use for the Office 365 Outlook activity? Select only one answer. A. On completion B. On skip C. On fail D. On success Answer: C Explanation: The correct answer is: C. On fail By connecting the Office 365 Outlook activity to each main pipeline activity using the On fail connection, you ensure that an email is sent only when a specific activity fails. This minimizes the number of email notifications and helps you focus on the critical issues that require attention. Here‘s a breakdown of why other options are not suitable: A. On completion: This connection would trigger the email notification for every pipeline run, regardless of success or failure. This could lead to excessive notifications, especially for successful runs. B. On skip: This connection would trigger the email notification only if an activity is skipped. This is not relevant to the goal of notifying about pipeline failures. D. On success: This connection would trigger the email notification for every successful pipeline run, which is not necessary and could lead to unnecessary notifications. Question 4: You have a complex Microsoft Power BI report that retrieves data from a Microsoft SQL Server database. You plan to use Power Query Editor to apply a series of transformations to shape the data. You need to make sure to use transformations that ensure that query folding is still in place. Which transformation prevents query folding? Select only one answer. A. Unpivot B. Adding index columns C. Keep rows D. Pivot Answer: B Explanation: The correct answer is: B. Adding index columns Adding index columns can significantly impact query folding in Power Query. When you add an index column, Power BI often needs to perform additional calculations and transformations on the data, which can hinder query optimization and prevent query folding. Query folding allows Power BI to push parts of the query logic down to the underlying data source, reducing the amount of data transferred and processed in the Power BI service. By avoiding transformations that hinder query folding, you can improve the performance and scalability of your reports. Why other options are less likely to prevent query folding: A. Unpivot: Unpivoting is a common data transformation technique that can often be optimized by Power BI. C. Keep rows: This transformation is typically used to filter rows based on specific criteria. It‘s not inherently a performance bottleneck. D. Pivot: Pivoting data can sometimes impact query folding, but it‘s often possible to optimize the transformation to maintain query folding. While it‘s important to be mindful of transf ormations that can hinder query folding, it‘s also essential to balance data modeling requirements with performance considerations. In many cases, it‘s possible to achieve both by carefully designing the data model and optimizing the DAX queries. Question 5: You have a Parquet file named Customers.parquet uploaded to the Files section of a Fabric lakehouse. You plan to use Data Wrangler to view basic summary statistics for the data before you load it to a Delta table. You open a notebook in the lakehouse. You need to load the data to a pandas DataFrame. Which PySpark code should you run to complete the task? A.df = pandas.read_parquet(“/lakehouse/default/Files/Customers.parquet“) B. import pandas as pd df = pd.read_parquet(“/lakehouse/Files/Customers.parquet“) C. import pandas as pd df = pd.read_parquet(“/lakehouse/default/Files/Customers.parquet“) D. df = pandas.read_parquet(“/lakehouse/Files/Customers.parquet“) Answer: C Explanation: Python import pandas as pd df = pd.read_parquet(“/lakehouse/default/Files/Customers.parquet“) Here‘s a breakdown of the code: Import pandas: This line imports the pandas library, which provides data analysis and manipulation tools for Python. Read Parquet File: The pd.read_parquet() function reads the Parquet file located at the specified path and loads it into a pandas DataFrame. The path /lakehouse/default/Files/Customers.parquet is the default location for files uploaded to a Fabric lakehouse. Once the data is loaded into a pandas DataFrame, you can use various pandas functions and methods to explore and analyze the data, including calculating summary statistics, visualizing data, and performing data cleaning and transformation. Why other options are incorrect: A, B, D: These options have incorrect paths to the Parquet file. The correct path format for a Fabric lakehouse is /lakehouse/default/Files/filename.parquet. By using the correct path and the pd.read_parquet() function, you can efficiently load the Parquet data into a pandas DataFrame and start your data analysis. For a full set of 880 questions. Go to https://skillcertpro.com/product/microsoft - fabric - analytics - engineer - dp - 600 - exam - questions/ SkillCertPro offers detailed explanations to each question which helps to understand the concepts better. It is recommended to score above 85% in SkillCertPro exams before attempting a real exam. SkillCertPro updates exam questions every 2 weeks. You will get life time access and life time free updates SkillCertPro assures 100% pass guarantee in first attempt. Question 6: You have a Fabric warehouse named Warehouse1. You discover a SQL query that performs poorly, and you notice that table statistics are out of date. You need to manually update the statistics to improve query performance for the table. Which column statistics should you update? Select only one answer. A. columns used in GROUP BY clauses B. columns with high cardinality C. primary key columns D. columns of the VARCHAR and NVARCHAR data type Answer: A Explanation: The correct answer is: A. columns used in GROUP BY clauses Explanation: Updating statistics on columns used in GROUP BY clauses is crucial for query optimization. This is because the query optimizer relies on statistics to estimate the number of rows and data distribution for each group. Accurate statistics help the optimizer choose the most efficient execution plan, leading to improved query performance. Here‘s a breakdown of why other options are less relevant: B. columns with high cardinality: While updating statistics on high-cardinality columns can be beneficial in some cases, it‘s not always necessary. The primary focus should be on columns used in GROUP BY, JOIN, and WHERE clauses. C. primary key columns: Primary key columns are often indexed, and their statistics are typically updated automatically. Updating statistics on primary keys might not have a significant impact on query performance. D. columns of the VARCHAR and NVARCHAR data type: While updating statistics on these columns can be helpful, it‘s not always necessary. The focus should be on columns that are frequently used in filtering, joining, and grouping operations. Question 7: You have a Fabric tenant that contains a workspace named Workspace1. Workspace 1 contains a lakehouse named Lakehouse1. You plan to use Microsoft SQL Server Management Studio (SSMS) to write SQL queries against Lakehouse1. Where can you find the SQL connection string for Lakehouse1? Select only one answer. A. in the Workspace settings under Azure connections B. in the Lakehouse settings under Copy SQL connection string C. in the Workspace settings under Data connections D. in the Tenant settings under Microsoft Fabric Answer: B Explanation: The correct answer is: B. in the Lakehouse settings under Copy SQL connection string Explanation: To connect to a Fabric lakehouse using SQL Server Management Studio (SSMS), you need the appropriate SQL connection string. This connection string can be found in the Lakehouse settings under the Copy SQL connection string option. Once you have the connection string, you can configure a new SQL Server database connection in SSMS, providing the connection string and necessary authentication details. This will allow you to execute SQL queries against the lakehouse and access the data stored within it. Why other options are incorrect: A. in the Workspace settings under Azure connections: Azure connections in the workspace settings are typically used for connecting to external data sources like Azure SQL Database, Azure Synapse Analytics, or other cloud services. They are not directly related to connecting to a Fabric lakehouse. C. in the Workspace settings under Data connections: Data connections in the workspace settings are used to connect to data sources for Power BI reports and other data analysis tools. They are not directly related to SQL Server connections. D. in the Tenant settings under Microsoft Fabric: Tenant settings are global settings for the entire Fabric tenant and do not provide specific connection strings for individual lakehouses. Question 8: You have Azure Databricks tables and a Fabric lakehouse. You need to create a new Fabric artifact to combine data from both architectures. The solution must use data pipelines for the Azure Databricks data and shortcuts for the existing Fabric lakehouse. What Fabric artifact should you create? A. Select only one answer. B. a semantic model C. a data warehouse D. a Dataflow Gen2 query E. a lakehouse F. Only Fabric lakehouses can shortcut to other lakehouses. Fabric data warehouses can use data Pipelines but cannot use shortcuts. Answer: E Explanation: The correct answer is: E. a lakehouse Explanation: A lakehouse is the ideal solution for combining data from various sources, including Azure Databricks tables and existing Fabric lakehouse data. Here‘s why: Data Integration: Lakehouses can seamlessly integrate data from different sources, including relational databases, data warehouses, and data lakes. Data Pipelines: You can use data pipelines to ingest data from Azure Databricks into the lakehouse, transforming and cleaning the data as needed. Shortcuts: Lakehouses can create shortcuts to other lakehouses, allowing you to reference and query data from external sources without physically copying the data. Semantic Layer: Lakehouses can provide a semantic layer to simplify data access and analysis, making it easier to understand and work with the combined dataset. By using a lakehouse, you can create a unified data platform that combines the best of both worlds: the flexibility and scalability of a data lake and the structure and queryability of a data warehouse. Why other options are incorrect: A. Semantic model: Semantic models are used to create a logical data model on top of a data warehouse. They are not suitable for combining data from different sources, especially when one source is a data lake. B. Data warehouse: While data warehouses can be used to store and analyze data, they are typically not designed to handle the variety and volume of data that a lakehouse can. C. Dataflow Gen2 query: Dataflows are primarily used for data ingestion, transformation, and loading. They are not suitable for combining data from different sources, especially when one source is a data lake. D. Spark notebook: Spark notebooks are used for data analysis and machine learning tasks. They are not designed for creating a unified data platform or combining data from different sources. Question 9: You have a Fabric tenant that contains a lakehouse named Lakehouse1. You have an external Snowflake database that contains a table with 200 million rows. You need to use a data pipeline to migrate the database to Lakehouse1. What is the most performant (fastest) method for ingesting data this large (200 million rows) by using a data pipeline? Select only one answer. A. Data Pipeline (Copy data) B. Data Pipeline (Dataflow Gen2) C. Data Pipeline (Lookup) D. Data Pipeline Spark (Notebook) Answer: A Explanation: The correct answer is: A. Data Pipeline (Copy data) Explanation: For a large-scale data migration like this, the Copy Data activity in a data pipeline is the most efficient and performant option. It‘s specifically designed for high - volume data transfers and offers several optimization techniques to accelerate the process: Parallel Processing: The Copy Data activity can leverage parallel processing to transfer data in chunks, significantly reducing the overall ingestion time. Optimized Data Transfer: It employs efficient data transfer protocols and compression techniques to minimize network traffic and improve performance. Built-in Performance Tuning: The activity provides various settings and configurations to optimize data transfer speed, including batch size, concurrency, and error handling. While Dataflow Gen2 is a powerful tool for data transformation and ingestion, it might not be the most efficient choice for a one-time, large-scale data migration. It‘s better suited for ongoing data ingestion and transformatio n pipelines. Other options like Lookup and Spark Notebook are not designed for high-volume data transfer and might not be as performant as the Copy Data activity. By using the Copy Data activity, you can ensure a fast and efficient data migration from the Snowflake database to the Fabric lakehouse. Question 10: You have a Fabric tenant that contains a warehouse named Warehouse1. You have a large 1 TB dataset in an external data source. You need to recommend a method to ingest the dataset into Warehouse1. The solution must provide the highest throughput and support a low-code/no-code development model. What should you recommend? A. Shortcut B. Dataflow Gen2 C. Copy data activity D. Spark notebook Answer: C Explanation: The correct answer is: C. Copy data activity Explanation: Copy data activity is the most efficient and recommended method for ingesting large datasets into a Fabric warehouse. It‘s designed to handle high -throughput data transfers and provides a user-friendly interface for configuring the data transfer process. Here‘s why it‘s the best choice: High Throughput: The Copy Data activity is optimized for large data transfers and can leverage parallel processing to accelerate the ingestion process. Low-Code/No-Code: The activity provides a visual interface for configuring the data source, destination, and transformation settings, making it easy to use without extensive coding knowledge. Flexibility: The Copy Data activity supports various data sources and formats, including CSV, JSON, Parquet, and more. Error Handling and Retry: The activity provides built-in error handling and retry mechanisms to ensure reliable data ingestion. While Dataflow Gen2 is a powerful tool for data transformation and integration, it might not be the most efficient choice for a one-time, large-scale data ingestion task. It‘s better suited for ongoing data pipelines and transformations. Spark notebooks and shortcuts are not designed for high-throughput data ingestion and might not be as efficient as the Copy Data activity. By using the Copy Data activity, you can ensure a fast and efficient data ingestion process, minimizing downtime and maximizing productivity. For a full set of 880 questions. Go to https://skillcertpro.com/product/microsoft - fabric - analytics - engineer - dp - 600 - exam - questions/ SkillCertPro offers detailed explanations to each questio n which helps to understand the concepts better. It is recommended to score above 85% in SkillCertPro exams before attempting a real exam. SkillCertPro updates exam questions every 2 weeks. You will get life time access and life time free updates SkillCe rtPro assures 100% pass guarantee in first attempt.