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

1. 1 of 55. A data scientist wants to ingest a directory full of plain text files so that each record in the output DataFrame contains the entire contents of a single file and the full path of the file the text was read from.
The first attempt does read the text files, but each record contains a single line. This code is shown below:
txt_path = "/datasets/raw_txt/*"
df = spark.read.text(txt_path) # one row per line by default
df = df.withColumn("file_path", input_file_name()) # add full path
Which code change can be implemented in a DataFrame that meets the data scientist's requirements?

A) Add the option lineSep=", " to the text() function.
B) Add the option lineSep to the text() function.
C) Add the option wholetext to the text() function.
D) Add the option wholetext=False to the text() function.


2. In the code block below, aggDF contains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) aggregate
B) replace
C) append
D) complete


3. 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('cust_id') == F.col('customer_id'))
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('customer_id') == F.col('custid'), 'left')
D) fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))


4. 31 of 55.
Given a DataFrame df that has 10 partitions, after running the code:
df.repartition(20)
How many partitions will the result DataFrame have?

A) 5
B) 10
C) 20
D) Same number as the cluster executors


5. An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A) Fetch the logs by running a Spark job with the spark-sql CLI tool.
B) Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
C) Locate the executor logs on the Spark master node, typically under the /tmp directory.
D) Use the command spark-submit with the -verbose flag to print the logs to the console.


Solutions:

Question # 1
Answer: C
Question # 2
Answer: D
Question # 3
Answer: C
Question # 4
Answer: C
Question # 5
Answer: B

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