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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. A developer is trying to join two tables, sales.purchases_fct and sales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in the purchases_fct table that do not exist in the customer_dim table are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

A) fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
B) fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
C) fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
D) fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))

2. An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?

A) spark.read.orc("/file/test_data.orc").selected("col1", "col2")
B) spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
C) spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")
D) spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")

3. 6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?

A) CPU Cores
B) Executors
C) Driver Nodes
D) Worker Nodes

4. A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A) Optimizing the layout of Delta files on disk
B) Collecting persistent table statistics and storing them in the metastore for future use
C) Dynamically switching join strategies
D) Improving the performance of single-stage Spark jobs

5. A Spark application is experiencing performance issues in client mode because the driver is resource-constrained.
How should this issue be resolved?

A) Increase the driver memory on the client machine
B) Switch the deployment mode to cluster mode
C) Add more executor instances to the cluster
D) Switch the deployment mode to local mode

Solutions: