Optimize and Troubleshoot Data Storage and Data Processing DP-203 Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   A pipeline run in Azure Data Factory fails because an external REST API call returns a 429 (Too Many Requests) status. What is the most likely cause?

   • AThe API has rate limiting that has been exceeded.
   • BThe API requires authentication and the token is invalid.
   • CThe API endpoint is not reachable due to network issues.
   • DThe API response format is not as expected.
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2. 2expert · hard

   An Azure Data Factory pipeline runs a Copy activity that loads data from Azure Blob Storage to a dedicated SQL pool. The pipeline fails intermittently with a 'SQL pool is busy' error. The team wants to reduce the chance of failure without changing the data or the SQL pool's DWU. Which configuration should they apply?

   • AAdd a retry policy to the Copy activity with a longer interval.
   • BEnable result-set caching on the SQL pool.
   • CUse a staging table and swap.
   • DCreate a non-clustered index on the destination table.
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4. 3application · medium

   A Spark job in Azure Synapse Analytics fails with an error message: 'Container killed on request. Exit code is 143'. The job processes a large dataset with a shuffle-heavy operation. The cluster has 5 executors with 8 GB memory each. Which is the most likely cause and the appropriate fix?

   • AThe cluster was autoscaled down; disable autoscale.
   • BThe executor ran out of memory; increase executor memory or reduce shuffle partitions.
   • CThe data source was inaccessible; check the storage account keys.
   • DThe job hit a timeout; increase the timeout setting.
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5. 4expert · hard

   An Azure Data Factory pipeline runs a copy activity from an Azure SQL Database to Azure Data Lake Storage Gen2. The pipeline fails intermittently with the error 'Failure happened on 'Source' side. ErrorCode=SqlFailedToConnect'. The SQL Database is healthy and other pipelines succeed. You need to reduce the failure rate without changing the source or destination. What should you do?

   • ASet the copy activity's fault tolerance to skip the failing rows.
   • BChange the copy activity to use a staging copy via Azure Blob Storage.
   • CUse a self-hosted integration runtime instead of the Azure integration runtime.
   • DIncrease the copy activity's retry count and retry interval.
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6. 5expert · hard

   A data lake stores IoT sensor data in Delta Lake format. The table is partitioned by date and contains millions of small files. A nightly Spark job aggregates the data by sensor type. The job is slow due to small files. The team also notices that some partitions have more data than others, causing skew. The team wants to improve performance and balance the workload. Which approach should they take?

   • AIncrease the number of shuffle partitions to 1000.
   • BRepartition the table by sensor type.
   • CUse a larger cluster with more executors.
   • DRun OPTIMIZE with ZORDER BY sensor type on the Delta table.
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