DP-700 Practice Test 6 — 10 Questions

Question 1

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?

Accepted Answer

On-premises Data Gateway.. The On-premises Data Gateway acts as a bridge, providing quick and secure data transfer between on-premises data (data that isn't in the cloud) and Microsoft cloud services like Fabric. It is a requirement for accessing local data sources that are not publicly accessible over the internet.

Answer

Azure Data Factory Integration Runtime.

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SQL Server Integration Services (SSIS).

Answer

Microsoft Entra Connect.

Question 2

You are monitoring long-running Spark jobs in your Fabric workspace. You want to identify which specific stages of the job are consuming the most time. Which tool should you use?

Accepted Answer

Spark UI. The Spark UI (part of the Fabric monitoring tools) provides a granular view of job execution, including stage-by-stage breakdowns. This allows engineers to identify bottlenecks where specific stages might be skewing or taking longer than expected. Understanding the stage breakdown is essential for tuning Spark performance, as it highlights inefficient operations like excessive shuffling, data skew, or inefficient data partitioning that can delay the overall completion of complex data pipelines.

Answer

Fabric Capacity Metrics app

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OneLake file explorer

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Azure Monitor logs

Question 3

You are monitoring a Microsoft Fabric Lakehouse. You notice that queries against a specific Delta table are performing slowly despite the table having a small data size. You need to identify the root cause of the performance degradation. What should you examine first?

Accepted Answer

The total number of files within the table directory.. High latency in Delta tables often stems from an excessive number of small files, which increases metadata overhead. Optimizing file size via compaction is a standard best practice in Fabric. By examining the table's file metadata, you can determine if a 'small file problem' exists, which is a common performance bottleneck in analytical workloads. Monitoring these metrics allows you to proactively trigger maintenance tasks like OPTIMIZE and VACCUUM to ensure efficient query execution and storage utilization.

Answer

The number of partitions in the table metadata.

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The CPU utilization of the Spark pool.

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The storage account throughput limits.

Question 4

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

Accepted Answer

It enables query folding and performance optimization. Staging-enabled destinations in Dataflow Gen2 allow the mashup engine to perform intermediate data operations in the Fabric lakehouse. This improves performance by enabling query folding and reducing the data transfer load on the source system. It effectively offloads complex transformations to the cloud storage, ensuring that the final data load is optimized and consistently formatted for downstream usage in the Fabric ecosystem.

Answer

It increases the number of concurrent users

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It reduces the cost of storage

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It automatically encrypts the data

Question 5

You are optimizing a Fabric pipeline that processes data from a high-frequency sensor source. You notice significant data skew during the join operation. Which three strategies should you implement to mitigate this skew?

Accepted Answer

Apply salting to the skewed join key.. Data skew occurs when one partition or key carries significantly more data than others, causing some executors to work much longer than others. Using salting to redistribute skewed keys, enabling skewed join optimization, or pre-aggregating the skewed dataset are proven techniques to balance the workload across the cluster. Implementing these strategies ensures that no single worker becomes a bottleneck, leading to more predictable execution times and preventing memory failures in large-scale data processing jobs.

Answer

Increase the number of partitions to the maximum.

Answer

Change the file format to CSV.

Question 6

Refer to the exhibit. You are reviewing the monitoring logs for your Fabric capacity. What is the most likely cause of the error shown in the exhibit?

Accepted Answer

The Fabric capacity is experiencing throttling.. The '429 Too Many Requests' error indicates that the Fabric capacity has reached its limits for concurrent requests or resource consumption. This occurs when the workload exceeds the allocated capacity units (CU) or the throughput limits set for that specific capacity type. To resolve this, you must analyze if the workload needs more resources, requires optimization, or needs to be distributed across different capacities.

Answer

The underlying data source is offline.

Answer

The user does not have read permissions.

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The query contains a syntax error.

Question 7

You have a Fabric pipeline that runs a notebook. The notebook takes longer to start each time. What is the most likely cause?

Accepted Answer

The Spark session initialization is delayed.. Spark notebooks in Fabric utilize 'Serverless Spark' pools. If the pool is not 'warm' or if the session initialization is competing for resources, the startup time increases. Understanding how session management works in Fabric is key to optimizing performance. Pre-warming pools or maintaining session persistence can help reduce these startup latencies, which are often overlooked but contribute significantly to the total end-to-end execution time of automated analytical data pipelines.

Answer

The Lakehouse storage is too full.

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The notebook code has too many comments.

Answer

The user does not have sufficient permissions.

Question 8

Refer to the exhibit. You are reviewing the execution plan for a query running on a Fabric SQL Analytics Endpoint. The query is performing slower than expected. Based on the JSON snippet of the plan, what is the most likely cause of the performance bottleneck?

Accepted Answer

The BroadcastExchange is moving too much data for a large table join.. Analyzing query plans in the SQL Analytics Endpoint is vital for performance tuning. The presence of a BroadcastExchange for a table that results in a very high output count after a HashJoin suggests that the data being moved across the network is excessive. This often happens when the engine incorrectly estimates the size of a table or when data is not properly distributed.

Answer

The RemoteScan is failing to find the Parquet files in OneLake.

Answer

The HashJoin is using an unsupported data type for the join condition.

Answer

OneLake storage is currently in a read-only state due to maintenance.

Question 9

You have a Fabric notebook that uses multiple Spark libraries. You notice that the startup time for your notebook is very slow. What is the best way to optimize this?

Accepted Answer

Use a pre-configured Fabric Environment.. Environment definitions in Microsoft Fabric allow you to pre-configure and pre-install the necessary Spark libraries. By creating an environment and attaching it to your notebook, you eliminate the need to run %pip install commands at runtime. This removes the overhead of installing packages every time the Spark session starts, leading to significantly faster session startup times and more consistent compute configurations for your data engineering tasks.

Answer

Install libraries using %pip at the top of the notebook.

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Store all libraries in a Lakehouse folder.

Answer

Increase the Spark cluster size.

Question 10

Refer to the exhibit. A data engineer is troubleshooting a Spark job that frequently fails with 'Out of Memory' (OOM) errors despite the configuration shown. The job processes a 500GB dataset with many wide transformations. What is the most effective configuration change to resolve the OOM errors?

Accepted Answer

Increase executorMemory and decrease executorCores per executor.. OOM errors in Spark often occur when the ratio of memory to cores is too low for the data being processed, or when the shuffle partitions are too large. While increasing the number of executors helps with parallelism, it doesn't solve memory pressure per task. Adjusting the executor memory or reducing the cores per executor ensures each task has more available heap space.

Answer

Set dynamicAllocation to false and use 50 static executors.

Answer

Increase the executorCores to 8 to allow more tasks to run.

Answer

Set vorderEnabled to false to reduce the memory overhead.