Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam Dumps
Databricks Certified Associate Developer for Apache Spark 3.5 – Python
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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Sample Questions
Question # 154 of 55. What is the benefit of Adaptive Query Execution (AQE)?
A. It allows Spark to optimize the query plan before execution but does not adapt during runtime.
B. It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
C. It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
D. It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
Question # 2
54 of 55. What is the benefit of Adaptive Query Execution (AQE)?
A. It allows Spark to optimize the query plan before execution but does not adapt during runtime.
B. It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
C. It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
D. It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
Question # 3
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. AGGREGATE
B. COMPLETE
C. REPLACE
D. APPEND
Question # 4
48 of 55. A data engineer needs to join multiple DataFrames and has written the following code: from pyspark.sql.functions import broadcast data1 = [(1, "A"), (2, "B")] data2 = [(1, "X"), (2, "Y")] data3 = [(1, "M"), (2, "N")] df1 = spark.createDataFrame(data1, ["id", "val1"]) df2 = spark.createDataFrame(data2, ["id", "val2"]) df3 = spark.createDataFrame(data3, ["id", "val3"]) df_joined = df1.join(broadcast(df2), "id", "inner") \ .join(broadcast(df3), "id", "inner") What will be the output of this code?
A. The code will work correctly and perform two broadcast joins simultaneously to join df1 with df2, and then the result with df3.
B. The code will fail because only one broadcast join can be performed at a time.
C. The code will fail because the second join condition (df2.id == df3.id) is incorrect.
D. The code will result in an error because broadcast() must be called before the joins, not inline.
Question # 5
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. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.
B. Shuffle join, because AQE is not enabled, and Spark uses a static query plan.
C. Shuffle join because no broadcast hints were provided.
D. Broadcast join, as df2 is smaller than the default broadcast threshold.
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