DP-700 Exam Dumps - Implementing Data Engineering Solutions Using Microsoft Fabric

When reading data from shortcuts in Fabric (in this case, from a lakehouse like Lakehouse1), the cache for shortcuts helps by storing the data locally for quick access. The last accessed timestamp and the cache expiration rules determine whether data is fetched from the cache or from the source (Google Cloud Storage, in this case).

Products: The ProductFile.parquet was last accessed 12 hours ago. Since the cache has data available for up to 12 hours, it is likely that this data will be retrieved from the cache, as it hasn't been too long since it was last accessed.

Stores: The StoreFile.json was last accessed 4 hours ago, which is within the cache retention period. Therefore, this data will also be retrieved from the cache.

Trips: The TripsFile.csv was last accessed 48 hours ago. Given that it's outside the typical caching window (assuming the cache has a maximum retention period of around 24 hours), it would not be retrieved from the cache. Instead, it will likely require a fresh read from the source.

Problem Overview:

Table1 has 2,000 small Parquet files (1 MB each).

Query performance suffers when the table contains numerous small files because the query engine must process each file individually, leading to significant overhead.

Solution:

To improve performance, file compaction is necessary to reduce the number of small files and create larger, optimized files.

Commands and Their Roles:

OPTIMIZE Command:

- Compacts small Parquet files into larger files to improve query performance.

- It supports optional features like V-Order, which organizes data for efficient scanning.

VACUUM Command:

- Removes old, unreferenced data files and metadata from the Delta table.

- Running VACUUM after OPTIMIZE ensures unnecessary files are cleaned up, reducing storage overhead and improving performance.

To copy data from an on-premises Microsoft SQL Server database (Database1) to a warehouse (Warehouse1) in Fabric, a data pipeline is the most appropriate tool. A data pipeline in Fabric is designed to move data between various data sources and destinations, including on-premises databases like SQL Server, and cloud-based storage like Fabric warehouses. The data pipeline can handle the connection through an on-premises data gateway, which is required to access on-premises data. This solution facilitates the orchestration of data movement and transformations if needed.

Adding an aggregation like make_list() would require additional processing and memory, which could make the query slower.

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