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

In Databricks notebooks, you can use the %pip install command in a notebook cell to install a Python package. This will install the package on all nodes in the currently active cluster at the notebook level. It is a feature provided by Databricks to facilitate the installation of Python libraries for the notebook environment specifically.

This is the correct answer because it efficiently updates the account current table with only the most recent value for each user id. The code filters records in account history using the last updated field and the most recent hour processed, which means it will only process the latest batch of data. It also filters by the max last login by user id, which means it will only keep the most recent record for each user id within that batch. Then, it writes a merge statement to update or insert the most recent value for each user id into account current, which means it will perform an upsert operation based on the user id column. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; Databricks Documentation, under “Upsert into a table using merge” section.

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."