Databricks-Certified-Professional-Data-Engineer Exam Dumps : Databricks Certified Data Engineer Professional Exam

The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

The provided PySpark code performs the following operations:

Reads Data from silver_customer_sales Table:

The code starts by accessing the silver_customer_sales table using the spark.table method.

Groups Data by customer_id:

The .groupBy("customer_id") function groups the data based on the customer_id column.

Aggregates Data:

The .agg() function computes several aggregate metrics for each customer_id:

F.min("sale_date").alias("first_transaction_date"): Determines the earliest sale date for the customer.

F.max("sale_date").alias("last_transaction_date"): Determines the latest sale date for the customer.

F.mean("sale_total").alias("average_sales"): Calculates the average sale amount for the customer.

F.countDistinct("order_id").alias("total_orders"): Counts the number of unique orders placed by the customer.

F.sum("sale_total").alias("lifetime_value"): Calculates the total sales amount (lifetime value) for the customer.

Writes Data to gold_customer_lifetime_sales_summary Table:

The .write.mode("overwrite").table("gold_customer_lifetime_sales_summary") command writes the aggregated data to the gold_customer_lifetime_sales_summary table.

The mode("overwrite") specifies that the existing data in the gold_customer_lifetime_sales_summary table will be completely replaced by the new aggregated data.

Conclusion:

When this code is executed, it reads all records from the silver_customer_sales table, performs the specified aggregations grouped by customer_id, and then overwrites the entire gold_customer_lifetime_sales_summary table with the aggregated results. Therefore, option D accurately describes this process: "The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job."

Partitioning a Delta Lake table is a strategy used to improve query performance by dividing the table into distinct segments based on the values of a specific column. This approach allows queries to scan only the relevant partitions, thereby reducing the amount of data read and enhancing performance.

Considerations for Choosing a Partition Column:

Cardinality: Columns with high cardinality (i.e., a large number of unique values) are generally poor choices for partitioning. High cardinality can lead to a large number of small partitions, which can degrade performance.

Query Patterns: The partition column should align with common query filters. If queries frequently filter data based on a particular column, partitioning by that column can be beneficial.

Partition Size: Each partition should ideally contain at least 1 GB of data. This ensures that partitions are neither too small (leading to too many partitions) nor too large (negating the benefits of partitioning).

Evaluation of Columns:

date:

Cardinality: Typically low, especially if data spans over days, months, or years.

Query Patterns: Many analytical queries filter data based on date ranges.

Partition Size: Likely to meet the 1 GB threshold per partition, depending on data volume.

user_id:

Cardinality: High, as each user has a unique ID.

Query Patterns: While some queries might filter by user_id, the high cardinality makes it unsuitable for partitioning.

Partition Size: Partitions could be too small, leading to inefficiencies.

post_id:

Cardinality: Extremely high, with each post having a unique ID.

Query Patterns: Unlikely to be used for filtering large datasets.

Partition Size: Each partition would be very small, resulting in a large number of partitions.

post_time:

Cardinality: High, especially if it includes exact timestamps.

Query Patterns: Queries might filter by time, but the high cardinality poses challenges.

Partition Size: Similar to user_id, partitions could be too small.

Conclusion:

Given the considerations, the date column is the most suitable candidate for partitioning. It has low cardinality, aligns with common query patterns, and is likely to result in appropriately sized partitions.