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最新的 Databricks Certification Associate-Developer-Apache-Spark-3.5 免費考試真題:

1. 47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?

A) Broadcast join, as df2 is smaller than the default broadcast threshold.
B) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.
C) Shuffle join, because AQE is not enabled, and Spark uses a static query plan.
D) Shuffle join because no broadcast hints were provided.

2. 46 of 55.
A data engineer is implementing a streaming pipeline with watermarking to handle late-arriving records.
The engineer has written the following code:
inputStream \
.withWatermark("event_time", "10 minutes") \
.groupBy(window("event_time", "15 minutes"))
What happens to data that arrives after the watermark threshold?

A) Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
B) Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
C) The watermark ensures that late data arriving within 10 minutes of the latest event time will be processed and included in the windowed aggregation.
D) Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.

3. Given a DataFrame df that has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

A) 20
B) Same number as the cluster executors
C) 1
D) 10

4. 38 of 55.
A data engineer is working with Spark SQL and has a large JSON file stored at /data/input.json.
The file contains records with varying schemas, and the engineer wants to create an external table in Spark SQL that:
Reads directly from /data/input.json.
Infers the schema automatically.
Merges differing schemas.
Which code snippet should the engineer use?

A) CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeSchema 'true');
B) CREATE TABLE users
USING json
OPTIONS (path '/data/input.json');
C) CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', inferSchema 'true');
D) CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeAll 'true');

5. 49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) APPEND
B) REPLACE
C) AGGREGATE
D) COMPLETE

問題與答案：