DP-700 Practice Test 10 — 10 Questions

Question 1

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?

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

Accepted Answer

Fabric Notebooks using Spark. In a Medallion architecture, the transition from Bronze to Silver typically involves cleaning, filtering, and converting data into the Delta format. Spark Notebooks are the preferred tool for this stage because they offer massive parallel processing capabilities and deep integration with the Delta Lake API, which is essential for maintaining performance as data volumes grow.

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Power BI Dataflows (Gen1)

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Data Factory Pipelines with a Lookup activity

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Direct T-SQL 'SELECT INTO' from the SQL Endpoint

Question 2

You notice that your Delta table is experiencing slow read performance due to file fragmentation. Which command should you run to optimize the physical storage layout?

Accepted Answer

OPTIMIZE TABLE. The OPTIMIZE command is the standard Delta Lake operation to compact small files into larger, optimally sized files. This reduces the metadata overhead and allows for more efficient sequential reads. In Fabric, running this periodically is a core maintenance task that ensures high-performance analytics, as it directly addresses the 'small file problem' that frequently impacts Lakehouse performance when data is ingested in high-frequency, low-volume batches.

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REORG TABLE

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VACUUM TABLE

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ANALYZE TABLE

Question 3

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'?

Accepted Answer

Run a Spark Notebook with the OPTIMIZE command on the table.. The 'small files problem' occurs when a large number of tiny files create excessive metadata overhead for the Spark engine. In Fabric, you can resolve this by using the 'Optimize' command or by configuring the Spark engine to compact files during the write process. This combines the small files into larger, more efficient Parquet files.

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Use the Copy Activity with the 'Preserve Hierarchy' setting.

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Convert the JSON files to Avro format before ingestion.

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Increase the number of partitions in the Spark DataFrame.

Question 4

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

Accepted Answer

Data Pipeline. Data Factory pipelines in Fabric provide a powerful, low-code interface for orchestrating end-to-end data integration. By dragging and dropping activities such as Copy, Notebook, and Dataflow, engineers can build robust, repeatable workflows. This feature is fundamental for simplifying the management of complex data pipelines, allowing teams to focus on logic and flow rather than writing extensive custom code for orchestration and scheduling.

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KQL Queryset

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Power BI Report

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Mirroring

Question 5

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

Accepted Answer

Use the alerts in the monitoring hub. Fabric pipeline monitoring can be enhanced using alerts configured within the monitoring hub or by utilizing Microsoft Fabric's integration with Azure Monitor/Log Analytics. By setting up alert rules based on pipeline status, you ensure proactive notification, allowing for rapid response and troubleshooting. This is a critical operational practice for maintaining high-reliability data systems, as it reduces downtime and ensures data quality standards are consistently met.

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Manually refresh the monitor tab

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Check the SQL database logs

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Run a daily Notebook to check status

Question 6

Which Fabric tool allows you to visually track the total compute usage of your workspace over a specific timeframe?

Accepted Answer

Capacity Metrics app. The Fabric Capacity Metrics app is the central dashboard for monitoring compute utilization. It provides visualizations that track usage against your provisioned capacity, making it easy to identify spikes and sustained high-load periods. This tool is vital for administrators to ensure that their capacity is sized correctly and that they are not consistently hitting their resource limits, which would cause throttling and performance degradation across the organization's analytical workloads.

Answer

OneLake monitoring dashboard

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Pipeline monitoring view

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Azure Advisor

Question 7

You are monitoring an ingestion pipeline and notice it frequently fails with a 'Timeout' error. What is the most likely cause?

Accepted Answer

The source system is taking too long to respond.. Timeout errors in ingestion pipelines are usually caused by source systems failing to respond within the expected time window or the copy activity reaching the default threshold. Increasing the timeout duration or optimizing the data fetch process can resolve this. This is a common operational hurdle when dealing with heterogeneous source systems that may have variable performance characteristics or intermittent network connectivity, requiring careful tuning of the pipeline's connection parameters.

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The target table schema has changed.

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The pipeline has incorrect user credentials.

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The data is already in the target table.

Question 8

Refer to the exhibit. The refresh operation for a Power BI semantic model took 45 minutes to complete. What is the most efficient way to reduce the refresh time for this specific model?

Accepted Answer

Configure incremental refresh.. Incremental refresh is the most effective strategy for large datasets. Instead of reloading all 5 million rows every time, you configure the model to load only new or updated data based on a date/time column. This dramatically reduces the amount of data processed per refresh, lowering execution time and resource utilization on the Fabric capacity while keeping reports up to date.

Answer

Upgrade to a higher capacity SKU.

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Use Power BI Desktop to manually refresh.

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Delete all historical data.

Question 9

Which configuration would you adjust to improve the performance of a Spark job that is consistently failing due to 'Out of Memory' (OOM) errors during aggregation?

Accepted Answer

Increase the executor memory configuration.. OOM errors during aggregation occur because the data partition is too large to fit in the executor's memory during the shuffle or group-by operation. Increasing the memory per executor or increasing the number of partitions (to make each partition smaller) are standard fixes. By spreading the load, you prevent any single executor from being overwhelmed, ensuring that the aggregation can complete without hitting the rigid memory limits of the compute nodes.

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Increase the driver memory.

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Change the table compression to None.

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Switch to a different Spark pool version.

Question 10

You are managing a large-scale data ingestion pipeline that runs every hour. Recently, the pipeline has started to fail with 'Concurrency Limit Exceeded' errors. You have several other pipelines running in the same workspace. How should you optimize the environment to resolve this error?

Accepted Answer

Implement a staggered schedule for the pipelines in the workspace.. Fabric capacities have limits on the number of concurrent operations that can run. When multiple pipelines or activities start simultaneously, they can exceed these limits. Implementing a more staggered schedule or using pipeline concurrency settings ensures that the workload stays within the allowed limits of the capacity SKU without failing.

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Use the 'Invoke Pipeline' activity with 'Wait on completion' disabled.

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Increase the 'Max concurrent runs' setting in the pipeline properties.

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Switch all pipelines to use the 'Small' Spark pool size.