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

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."

This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company’s data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees. Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs. Verified References: [Databricks Certified Data Engineer Professional], under “Databricks Workspace” section; Databricks Documentation, under “Choose a region” section.

When triggering a job run using the Databricks CLI, the run_id field in the response represents a globally unique identifier for that particular run of the job. This run_id is distinct from the job_id. While the job_id identifies the job definition and is constant across all runs of that job, the run_id is unique to each execution and is used to track and query the status of that specific job run within the Databricks environment. This distinction allows users to manage and reference individual executions of a job directly.