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Databricks Certified Data Engineer Professional 認定 Databricks-Certified-Data-Engineer-Professional 試験問題:

1. 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) delayWrite("event_time", "10 minutes")
C) slidingWindow("event_time", "10 minutes")
D) withWatermark("event_time", "10 minutes")
E) await("event_time + `10 minutes'")

2. A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

A) No; each of the streams needs to have its own checkpoint directory.
B) Yes; both of the streams can share a single checkpoint directory.
C) No; Delta Lake manages streaming checkpoints in the transaction log.
D) No; only one stream can write to a Delta Lake table.
E) Yes; Delta Lake supports infinite concurrent writers.

3. Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table")
\
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?

A) The event_date column is outside the table's partitioning and Z-ordering scheme.
B) The filter is executed only after the full data scan, preventing data skipping.
C) The Delta table lacks optimization that enables dynamic file pruning.
D) The filter condition involves a data type excluded from data skipping support.

4. A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?

A) All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
B) All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
C) All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
D) All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
E) All records from all columns will be displayed with the values in user_ltv.

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. Events are recorded once per minute per device.
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) to_interval("event_time", "5 minutes").alias("time")
B) window("event_time", "5 minutes").alias("time")
C) lag("event_time", "10 minutes").alias("time")
D) "event_time"
E) window("event_time", "10 minutes").alias("time")

質問と回答：