Free Databricks Databricks-Certified-Data-Analyst-Associate Exam Dumps Questions & Answers

A Data Analyst is working on employees_df and needs to add a new column where a 10% tax is calculated on the salary. Additionally, the DataFrame contains the column age, which is not needed.
Which code fragment adds the tax column and removes the age column?

• A.employees_df = employees_df.withColumn( " tax " , lit(0.1) * col( " salary " )).dropField( " age " )
• B.employees_df = employees_df.withColumn( " tax " , employees_df.salary * 10).drop( " age " )
• C.employees_df = employees_df.withColumn( " tax " , employees_df.salary * 10).dropField( " age " )
• D.employees_df = employees_df.withColumn( " tax " , employees_df.salary * 0.1).drop( " age " )

Consider the following two statements:
Statement 1:

Statement 2:
Which of the following describes how the result sets will differ for each statement when they are run in Databricks SQL?

• A.There is no difference between the result sets for both statements.
• B.When the first statement is run, all rows from the customers table will be returned and only the customer_id from the orders table will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.
• C.Both statements will fail because Databricks SQL does not support those join types.
• D.The first statement will return all data from the customers table and matching data from the orders table. The second statement will return all data from the orders table and matching data from the customers table. Any missing data will be filled in with NULL.
• E.When the first statement is run, only rows from the customers table that have at least one match with the orders table on customer_id will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.

A data analyst has set up a SQL query to run every four hours on a SQL endpoint, but the SQL endpoint is taking too long to start up with each run.
Which of the following changes can the data analyst make to reduce the start-up time for the endpoint while managing costs?

• A.Increase the SQL endpoint cluster size
• B.Use a Serverless SQL endpoint
• C.Reduce the SQL endpoint cluster size
• D.Turn off the Auto stop feature
• E.Increase the minimum scaling value

Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

• A.( spark.table( " sales " ) .groupBy( " store " ) .agg(sum( " sales " )) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " complete " ) .table( " newSales " ))
• B.( spark.table( " sales " ) .filter(col( " units " ) > 0) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
• C.( spark.table( " sales " ) .withColumn( " avgPrice " , col( " sales " ) / col( " units " )) .writeStream .option ( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
• D.( spark.table( " sales " ) .withColumn( " avgPrice " , col( " sales " ) / col( " units " )) .writeStream .option ( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
• E.Option E
• F.Option A
• G.( spark.readStream.load(rawSalesLocation) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
• H.Option B
• I.Option C
• J.Option D

A data analyst at an e-commerce company needs to process daily sales data. The data consists of approximately 50,000 records stored in a single CSV file, totaling about 20 MB. The analyst needs to perform aggregations and generate a summary report.
Which approach could the data analyst use in this situation?

• A.Set up a Hadoop ecosystem with HDFS and MapReduce for distributed processing.
• B.Use a local Python script with the pandas library to read and analyze the CSV file.
• C.Deploy a real-time streaming solution using Spark Streaming to process incoming data.
• D.Implement Apache Spark with a distributed cluster to process the data in parallel.