Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

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 Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified References: [Databricks Certified Data Engineer Professional], under “Language Interoperability” section; Databricks Documentation, under “Mix languages” section.

The correct answer is A. withWatermark(“event_time”, “10 minutes”). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:

Watermarking:

Windowed aggregations: