DP-700 Practice Test 9 — 10 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The ingestion process fails.. With schema validation set to 'strict', Dataflow Gen2 enforces a rigid mapping between source and sink schemas. If the incoming data does not match the defined schema exactly, the ingestion process will fail. This configuration is critical for maintaining high data quality and preventing 'schema drift' in downstream analytical tables, ensuring that automated processes do not break due to unexpected changes in the upstream data structure.

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The extra column is automatically dropped.

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The extra column is added to the destination schema.

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The extra column is logged as a warning.

Question 2

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

Accepted Answer

It stores the offset and state information for fault tolerance.. Checkpointing is essential in Structured Streaming for fault tolerance and exactly-once processing guarantees. It stores the state of the stream, including the offsets of the data processed so far. If a job fails, the checkpoint allows the stream to resume exactly where it left off, preventing data loss or duplication. This is a mandatory component for building reliable streaming pipelines in a production Fabric Lakehouse environment.

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It is used to store the schema definition.

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It increases the throughput of the write operation.

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It is required for overwriting existing tables.

Question 3

You have a Lakehouse in a workspace and want to allow a specific user to query the data using the SQL analytics endpoint, but you do not want them to be able to modify the Lakehouse schema or delete files. Which action should you take?

Accepted Answer

Share the Lakehouse with the user and grant 'Read' and 'ReadData' permissions.. This question explores the granularity of permissions in Microsoft Fabric. Providing access to the SQL analytics endpoint requires specific permissions that differ from the full 'Contributor' access. Understanding how to share items with 'Read' and 'ReadData' permissions is essential for implementing a least-privilege security model.

Answer

Assign the user to the Contributor role in the workspace.

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Grant the user 'Execute' permissions on the workspace capacity.

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Add the user to the 'Member' role and disable 'Share' permissions.

Question 4

A company is using Git integration in Microsoft Fabric to manage their Lakehouse and Report items. They notice that after a developer committed changes to a branch and synced them to the workspace, certain sensitive metadata files are being tracked in the repository. Which file should be modified in the local Git repository to prevent specific Fabric item metadata from being tracked in the future?

Accepted Answer

.gitignore. Git integration in Fabric synchronizes item definitions between the workspace and a Git provider. To manage which files are ignored or tracked, standard Git practices apply. This is essential for preventing the leakage of sensitive internal metadata or temporary files that should not be part of the version control history of the analytics project.

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fabric.config

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.fabricignore

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item.config.json

Question 5

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

Accepted Answer

The sink type is set to DeltaSink, which requires a pre-defined schema mapping for autoCreate.. The exhibit shows a standard copy activity without a defined dataset mapping or schema definition. When using 'autoCreate' for a Delta sink, Fabric requires a schema definition or an existing mapping to determine column types correctly. Without it, the sink assumes all data is string-based, which often leads to errors when writing to strictly typed Delta tables or results in poor query performance.

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The recursive flag is set to true, causing the sink to attempt to merge multiple schemas.

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AzureBlobFSReadSettings does not support schema inference from delimited files.

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The Copy activity requires an Execute Pipeline activity to initialize the sink.

Question 6

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

Accepted Answer

To ensure a dataset exists before a downstream activity runs.. The Validate activity is used to check the existence or completion of a dataset before proceeding to the next step. It is commonly used to ensure that upstream processes have successfully dropped files into a landing zone. This activity prevents downstream failures by ensuring the data is ready and present, providing a reliable orchestration pattern that keeps pipelines running smoothly and predictably in production.

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To verify the data types of columns in a table.

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To perform a checksum verification of file integrity.

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To compile the pipeline and check for syntax errors.

Question 7

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?

Accepted Answer

Use a watermark column for filtering. Using a watermarking column (such as a 'LastModified' timestamp or an incrementing ID) is the standard industry practice for incremental loading. By querying only rows with a value greater than the previous high-water mark, you significantly reduce the amount of data transferred and processed. This approach minimizes source system load and execution time, making it essential for building performant, cost-effective pipelines that handle large-scale data updates over time.

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Truncate and reload the entire dataset every time

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Compare every row using a hash function

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Delete all data older than one month

Question 8

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

Accepted Answer

Delta Lake. Delta Lake is the gold standard format for Fabric. It is an open-source storage layer that brings ACID transactions to data lakes. It enables high-performance querying and supports time travel, schema evolution, and efficient metadata management. Because Fabric is built on the foundation of Delta Lake, using this format allows you to take advantage of native optimizations, providing superior performance compared to traditional formats like CSV or Parquet alone.

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CSV

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JSON

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Avro

Question 9

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

Accepted Answer

Fabric Monitoring Hub.. The Monitoring Hub in Fabric provides a centralized view of all pipeline runs, including status, duration, and error logs. It is the primary tool for administrators and engineers to troubleshoot failed jobs, monitor performance, and track the history of data movement. Having this visibility is crucial for maintaining a reliable data platform, as it allows for proactive detection and quick resolution of pipeline failures or performance bottlenecks in your environment.

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Power BI Desktop.

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Microsoft Purview.

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Data Warehouse SQL Query Editor.

Question 10

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?

Accepted Answer

Disable the 'first_row_as_header' setting and use custom schema mapping. When dealing with CSV files, column headers must be compatible with the destination system's naming conventions. Often, special characters in source headers are invalid for column names in the target Lakehouse tables. By sanitizing headers or choosing to skip them and using custom column names, you ensure the load completes successfully. This is a common real-world challenge when integrating data from disparate source systems into a unified analytical Lakehouse.

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Change the file encoding to ASCII

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Increase the pipeline retry count

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Convert the CSV to binary format