Databricks Databricks-Certified-Associate-Data-Engineer

You are currently working on a project that requires the use of SQL and Python in a given notebook, what would be your approach

Data science team members are using a single cluster to perform data analysis, although cluster size was chosen to handle multiple users and auto-scaling was enabled, the team realized queries are still running slow, what would be the suggested fix for this?

At the end of the inventory process a file gets uploaded to the cloud object storage, you are asked to build a process to ingest data which of the following method can be used to ingest the data incrementally, schema of the file is expected to change overtime ingestion process should be able to handle these changes automatically. Below is the auto loader to command to load the data, fill in the blanks for successful execution of below code.

spark.readStream

.format("cloudfiles")

.option("_______",”csv)

.option("_______", ‘dbfs:/location/checkpoint/’)

.load(data_source)

.writeStream

.option("_______",’ dbfs:/location/checkpoint/’)

.option("_______", "true")

.table(table_name))

When you drop a managed table using SQL syntax DROP TABLE table_name how does it impact metadata, history, and data stored in the table?

When working with AUTO LOADER you noticed that most of the columns that were inferred as part of loading are string data types including columns that were supposed to be integers, how can we fix this?