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

1. A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columnssensor_id,temperature, andtimestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.
Which code implementation achieves the requirement?
Options from the images provided:

A)

B)

C)

D)

2. Given the following code snippet inmy_spark_app.py:

What is the role of the driver node?

A) The driver node only provides the user interface for monitoring the application
B) The driver node holds the DataFrame data and performs all computations locally
C) The driver node stores the final result after computations are completed by worker nodes
D) The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes

3. Given the code:

df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp")," ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?

A) When the groupBy transformation is applied
B) When the count action is applied
C) When the filter transformation is applied
D) When the show action is applied

4. A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:

Which of the following code snippets will read all the data within the directory structure?

A) df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")
B) df = spark.read.parquet("/path/events/data/")
C) df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")
D) df = spark.read.parquet("/path/events/data/*")

5. A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

A) Use an accumulator to record the maximum time on the driver
B) Broadcast a variable to share the maximum time among workers
C) Use an RDD action like reduce() to compute the maximum time
D) Configure the Spark UI to automatically collect maximum times

問題與答案：