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Databricks Certified Data Engineer Professional Sample Questions:

1. The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

A) The RDD Block Name included the '' annotation signaling failure to cache
B) The number of Cached Partitions> the number of Spark Partitions
C) Size on Disk is> 0
D) Size on Disk is < Size in Memory
E) On Heap Memory Usage is within 75% of off Heap Memory usage

2. A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that, over time, data being transmitted from the bicycle sensors fail to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
"valid_lat": "(lat IS NOT NULL)",
"valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?

A) @dlt.table
@dlt.expect_all_or_drop(rules)
def trips_data_quarantine():
return spark.readStream.table("raw_trips_data")
B) @dlt.view
@dlt.expect_or_drop("lat_long_present", "(lat IS NOT NULL AND long IS NOT NULL)") def trips_data_quarantine():
return spark.readStream.table("ride_and_go.telemetry.trips")
C) @dlt.table(name="trips_data_quarantine")
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.filter(expr(quarantine_rules))
)
D) @dlt.table(partition_cols=["is_quarantined", ])
@dlt.expect_all(rules)
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.withColumn("is_quarantined", expr(quarantine_rules))
)

3. A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of information is critical to this decision?

A) Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.
B) Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.
C) Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
D) Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
E) Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.

4. A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A) The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
B) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
C) Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
D) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
E) All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.

5. A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A) awaitArrival("event_time", "10 minutes")
B) await("event_time + `10 minutes'")
C) delayWrite("event_time", "10 minutes")
D) slidingWindow("event_time", "10 minutes")
E) withWatermark("event_time", "10 minutes")

Solutions: