Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam Dumps : Databricks Certified Associate Developer for Apache Spark 3.5 – Python

According to Spark’s watermarking rules:

“Records that are older than the watermark (event time < current watermark) are considered too late and are dropped.”

So, if a record’s event_time is earlier than (max event_time seen so far - 10 minutes), it is discarded.

Spark supports multiple broadcast joins in a single query plan, as long as each broadcasted DataFrame is small enough to fit under the configured threshold.

Execution Plan:

Spark broadcasts df2 to all executors.

Joins df1 (big) with broadcasted df2.

Then broadcasts df3 and performs another join with the intermediate result.

The result is efficient and avoids shuffling large data.

Why the other options are incorrect:

B: Multiple broadcast joins are supported in Spark 3.x.

C: The join condition is correct since all use id as the key.

D: broadcast() can be used inline; it’s valid syntax.

In Spark SQL, the function date_sub(startDate, days) returns the date that is days before startDate.

If the days parameter is negative, Spark interprets it as subtracting a negative number, which effectively adds days to the date.

Example:

from pyspark.sql.functions import date_sub, lit

df = spark.createDataFrame([( "2024-10-01", )], ["dt"])

df.select(date_sub("dt", -5).alias("new_date")).show()

Output:

+----------+

| new_date |

+----------+

|2024-10-06|

+----------+

Why the other options are incorrect:

B: No error occurs; negative values are supported.

C: The start date changes if days ≠ 0.

D: Subtracting days would move the date backward, not forward.