Ingest and Transform Data

You are designing a Fabric pipeline to copy data from an on-premises SQL Server to a OneLake Lakehouse. Which integration runtime configuration ensures the highest throughput for large datasets?

Refer to the exhibit. The Copy activity fails to infer the schema correctly from the source files. What is the most likely cause?

You need to perform a complex transformation that involves multiple joins across tables of varying sizes in Fabric. Which tool provides the best balance between performance and ease of use?

You have a large Delta table that is frequently queried. You notice that queries are slow due to small file overhead. Which command should you run to optimize this table?

You are configuring a Fabric Pipeline. Which TWO activities can be used to perform conditional logic within your data ingestion workflow?

You need to ingest data from an API into a Fabric Lakehouse. The API requires a token-based authentication refreshed every hour. What is the most efficient way to handle this?

You are designing a data lakehouse architecture. Which file format is recommended for optimal performance in Fabric?

What is the primary function of the 'Validate' activity in a Fabric pipeline?

When ingesting data from an external source, what is the best practice for handling sensitive data?

Refer to the exhibit. You are using Structured Streaming to ingest data into a Delta table. Why is the checkpoint path required?

Which Fabric feature allows you to monitor the status and history of your data pipeline runs?

You are processing a large dataset in a Notebook and encounter frequent 'Shuffle' operations that slow down the job. What is the most likely cause?

You are building a data pipeline in Microsoft Fabric using a Copy Activity to ingest millions of small JSON files from an Azure Data Lake Storage Gen2 container into a Lakehouse table. The performance is significantly slower than expected due to high request latencies. Which optimization technique should you implement?

You are configuring a medallion architecture in Microsoft Fabric using Lakehouse Delta tables. You need to implement an incremental data load pattern from a bronze table to a silver table using a PySpark notebook. Which TWO actions must you perform to ensure idempotency and high performance? Choose exactly two.

You are designing a data pipeline in Microsoft Fabric using Data Factory. You need to ingest data from an on-premises SQL Server database into a Lakehouse. Which integration component must be deployed to facilitate secure connectivity between the on-premises network and Microsoft Fabric?

You are developing a Notebook in Microsoft Fabric to process high-volume sensor data. You need to optimize performance using Delta Lake features. Which TWO actions should you perform? (Choose two)

Refer to the exhibit. You are configuring a Dataflow Gen2 to move data. Based on the JSON configuration, what happens if the incoming data contains an extra column not defined in the destination schema?

You are using a Copy Activity in Microsoft Fabric Data Factory. You need to ensure that the data is encrypted both in transit and at rest. Which setting should you verify?

You are performing a large-scale data migration into a Fabric Lakehouse using a Spark Notebook. You notice that the job is failing with an 'Out of Memory' error. What is the most effective way to address this while utilizing PySpark?

You need to automate the ingestion of files from an Azure Blob Storage container into a Fabric Lakehouse. Which THREE components are required to build this automated pipeline? (Choose three)

You are configuring a Fabric pipeline to perform incremental loads from a source system. Which approach is considered the best practice to track changes efficiently?

Refer to the exhibit. Given the retry policy configuration, what happens if the source SQL database is temporarily unavailable during the first attempt?

You are designing a data transformation layer using Spark in Microsoft Fabric. Which THREE of the following are primary benefits of using Delta Lake format over traditional Parquet files? (Choose three)

You are monitoring a Fabric pipeline. You need to identify which activity failed within the pipeline execution. Which tool should you use?

Refer to the exhibit. You are loading this file into a Lakehouse. You notice that the column headers contain special characters, and the data is failing to load correctly. What should you do?

You are building a pipeline and need to ensure that a downstream activity only runs if a previous activity succeeds. Which pipeline feature should you use?

You need to access data from an external Azure Data Lake Storage (ADLS) Gen2 account in a Fabric Lakehouse without duplicating the physical data. Which feature should you use?

You are building a Dataflow Gen2 to ingest a 50 GB CSV file from an Azure Storage account into a Fabric Lakehouse. You want to ensure the ingestion uses the 'Fast Copy' feature. What must you do?

You are optimizing a Spark Notebook in Fabric that processes large Parquet files. Which TWO actions will improve the performance of data transformations?

Your organization wants to implement near real-time data ingestion from an Azure SQL Database into Fabric for reporting. You must minimize the impact on the source database's performance and avoid manual pipeline scheduling. Which solution should you implement?

Refer to the exhibit. You are reviewing a JSON snippet from a Fabric Data Factory pipeline. What is the result of executing this activity if the target table already exists?

You have a Lakehouse with several Delta tables. You need to provide a SQL-based interface for data analysts to query this data using their existing SQL tools, but you want to ensure they do not accidentally modify the underlying files. What should you use?

When writing data to a Lakehouse table using a Spark Notebook in Microsoft Fabric, which optimization is enabled by default to ensure maximum compatibility and performance across all Fabric engines?

You are configuring a Dataflow Gen2 to perform incremental updates for a large fact table. Which TWO steps are essential to ensure the dataflow only processes new or changed records during each run?

Refer to the exhibit. You have applied these Spark configurations to your Fabric environment. What is the primary benefit of this specific combination of settings when transforming data into Delta tables?

You need to ingest data from an Azure SQL Database that is protected by a firewall and does not allow public network access. Which Fabric feature should you use to establish a secure connection without opening the firewall to all Azure services?

You need to ingest data from an on-premises SQL Server database into a Fabric Lakehouse. What must be installed and configured on a local server to facilitate this connection?

You are configuring a Copy Activity in Fabric Data Factory to ingest data from an external REST API. Which TWO configuration settings are required to ensure data is handled correctly? (Choose two)

You are using Dataflow Gen2 to ingest data from multiple sources. You need to perform a complex transformation that involves merging data from a SQL database and a flat file based on a common key. Which step should you take to achieve this efficiently?

Which file format should you choose for a Lakehouse to ensure the best support for ACID transactions and high-performance analytical queries?

When creating a Dataflow Gen2, what is the primary advantage of using a staging-enabled destination?

Refer to the exhibit. You are running a Fabric Notebook. What is the most likely cause of the error shown?

Which Fabric feature allows you to orchestrate complex data ingestion and transformation workflows using a drag-and-drop interface?

You are monitoring a Data Factory pipeline. What is the best way to be notified when a pipeline fails during an execution?

You are optimizing a Spark job in Fabric. Which TWO of the following techniques will help reduce the execution time? (Choose two)

Which Fabric tool would you use to perform a visual, low-code data transformation that directly results in a clean table in your Lakehouse?

You are optimizing the ingestion of a large dataset into a Microsoft Fabric Lakehouse using a Spark Notebook. To ensure optimal query performance for downstream consumers, you want to apply the V-Order optimization and partition the data. Which TWO actions should you take to achieve this?

A data engineer needs to provide access to data stored in an external Azure Data Lake Storage (ADLS) Gen2 account without moving the data into the Fabric Lakehouse. Which feature of Microsoft Fabric should be used?

You are building a Medallion architecture in Fabric. You have ingested raw CSV files into the 'Files' section of your Bronze Lakehouse. You now need to transform this data and load it into a Delta table in the Silver Lakehouse. Which method provides the best performance and scalability for this transformation?

You are configuring a Data Factory Pipeline to perform an incremental load from an Azure SQL Database to a Fabric Warehouse. Which THREE components or features are essential for implementing a watermark-based incremental load? (Choose THREE)

Refer to the exhibit. You are attempting to publish a Dataflow Gen2 that loads data into a Fabric Warehouse. Based on the error message, what action must you take to successfully publish the dataflow?

You are ingesting thousands of small JSON files into a Fabric Lakehouse. You notice that query performance on the resulting Delta table is very poor. Which ingestion strategy should you implement to resolve the 'small files problem'?

You are designing a data pipeline in Microsoft Fabric to ingest incremental data from an on-premises SQL Server. You need to ensure that only records modified since the last pipeline run are ingested. Which approach should you implement to maintain optimal performance and minimal data redundancy?