[Q19-Q41] Exam Questions and Answers for Databricks-Certified-Professional-Data-Engineer Study Guide Questions and Answers!

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Exam Questions and Answers for Databricks-Certified-Professional-Data-Engineer Study Guide Questions and Answers!

Databricks Certified Professional Data Engineer Exam Certification Sample Questions and Practice Exam

NO.19 Which of the following features of data lakehouse can help you meet the needs of both workloads?

Data lakehouse requires very little data modeling.

Data lakehouse combines compute and storage for simple governance.

Data lakehouse provides autoscaling for compute clusters.

Data lakehouse can store unstructured data and support ACID transactions.

Data lakehouse fully exists in the cloud.

NO.20 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?

Drops table from meta store, drops metadata, history, and data in storage.

Drops table from meta store and data from storage but keeps metadata and history in storage

Drops table from meta store, meta data and history but keeps the data in storage

Drops table but keeps meta data, history and data in storage

Drops table and history but keeps meta data and data in storage

NO.21 A new data engineer has started at a company. The data engineer has recently been added to the company’s
Databricks workspace as [email protected]. The data engineer needs to be able to query the table
sales in the database retail. The new data engineer already has been granted USAGE on the database retail.
Which of the following commands can be used to grant the appropriate permissions to the new data engineer?

GRANT USAGE ON TABLE sales TO [email protected];

GRANT SELECT ON TABLE [email protected] TO sales;

GRANT SELECT ON TABLE sales TO [email protected];

GRANT CREATE ON TABLE sales TO [email protected];

GRANT USAGE ON TABLE [email protected] TO sales;

NO.22 Which of the following Structured Streaming queries successfully performs a hop from a Silver to Gold table?

1.(spark.table(“sales”)
2..groupBy(“store”)
3..agg(sum(“sales”))
4..writeStream
5..option(“checkpointLocation”, checkpointPath)
6..outputMode(“complete”)
7..table(“aggregatedSales”) )
(Correct)

1.(spark.table(“sales”)
2..writeStream
3..option(“checkpointLocation”, checkpointPath)
4..outputMode(“complete”)
5..table(“sales”) )

1.(spark.table(“sales”)
2..withColumn(“avgPrice”, col(“sales”) / col(“units”))
3..writeStream
4..option(“checkpointLocation”, checkpointPath)
5..outputMode(“append”)
6..table(“cleanedSales”) )

1.(spark.readStream.load(rawSalesLocation)
2..writeStream
3..option(“checkpointLocation”, checkpointPath)
4..outputMode(“append”)
5..table(“uncleanedSales”) )

1.(spark.read.load(rawSalesLocation)
2. .writeStream
3. .option(“checkpointLocation”, checkpointPath)
4. .outputMode(“append”)
5. .table(“uncleanedSales”) )

NO.23 A data engineer has developed a code block to perform a streaming read on a data source. The code block is
below:
1. (spark
2. .read
3. .schema(schema)
4. .format(“cloudFiles”)
5. .option(“cloudFiles.format”, “json”)
6. .load(dataSource)
7. )
The code block is returning an error.
Which of the following changes should be made to the code block to configure the block to successfully
perform a streaming read?

The .read line should be replaced with .readStream

The .format(“cloudFiles”) line should be replaced with .format(“stream”)

A new .stream line should be added after the spark line

A new .stream line should be added after the .read line

A new .stream line should be added after the .load(dataSource) line

NO.24 You are noticing job cluster is taking 6 to 8 mins to start which is delaying your job to finish on time, what steps you can take to reduce the amount of time cluster startup time

Setup a second job ahead of first job to start the cluster, so the cluster is ready with re-sources when the job starts

Use All purpose cluster instead to reduce cluster start up time

Reduce the size of the cluster, smaller the cluster size shorter it takes to start the clus-ter

Use cluster pools to reduce the startup time of the jobs

Use SQL endpoints to reduce the startup time

NO.25 Which of the following benefits does Delta Live Tables provide for ELT pipelines over standard data pipelines
that utilize Spark and Delta Lake on Databricks?

The ability to write pipelines in Python and/or SQL

The ability to declare and maintain data table dependencies

The ability to automatically scale compute resources

The ability to access previous versions of data tables

The ability to perform batch and streaming queries

NO.26 You are looking to process the data based on two variables, one to check if the department is supply chain and second to check if process flag is set to True

if department = “supply chain” & process:

if department == “supply chain” && process:

if department == “supply chain” & process == TRUE:

if department == “supply chain” & if process == TRUE:

if department == “supply chain” and process:

NO.27 When using the complete mode to write stream data, how does it impact the target table?

Entire stream waits for complete data to write

Stream must complete to write the data

Target table cannot be updated while stream is pending

Target table is overwritten for each batch

Delta commits transaction once the stream is stopped

NO.28 A data engineer wants to create a relational object by pulling data from two tables. The relational object must
be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to
avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?

Delta Table

View

Temporary view

Spark SQL Table

Database

NO.29 What is the type of table created when you issue SQL DDL command CREATE TABLE sales (id int, units int)

Query fails due to missing location

Query fails due to missing format

Managed Delta table

External Table

Managed Parquet table

NO.30 You were asked to create a table that can store the below data, orderTime is a timestamp but the finance team when they query this data normally prefer the orderTime in date format, you would like to create a calculated column that can convert the orderTime column timestamp datatype to date and store it, fill in the blank to complete the DDL.

AS DEFAULT (CAST(orderTime as DATE))

GENERATED ALWAYS AS (CAST(orderTime as DATE))
Correct)

GENERATED DEFAULT AS (CAST(orderTime as DATE))

AS (CAST(orderTime as DATE))

Delta lake does not support calculated columns, value should be inserted into the table as part of the ingestion process

NO.31 Kevin is the owner of the schema sales, Steve wanted to create new table in sales schema called regional_sales so Kevin grants the create table permissions to Steve. Steve creates the new table called regional_sales in sales schema, who is the owner of the table regional_sales

Kevin is the owner of sales schema, all the tables in the schema will be owned by Kevin

Steve is the owner of the table

By default ownership is assigned DBO

By default ownership is assigned to DEFAULT_OWNER

Kevin and Smith both are owners of table

NO.32 A data engineer is overwriting data in a table by deleting the table and recreating the table. Another data
engineer suggests that this is inefficient and the table should simply be overwritten instead.
Which of the following reasons to overwrite the table instead of deleting and recreating the table is incorrect?

Overwriting a table is an atomic operation and will not leave the table in an unfinished state

Overwriting a table maintains the old version of the table for Time Travel

Overwriting a table is efficient because no files need to be deleted

Overwriting a table results in a clean table history for logging and audit purposes

Overwriting a table allows for concurrent queries to be completed while in progress

NO.33 Which of the following statements can successfully read the notebook widget and pass the python variable to a SQL statement in a Python notebook cell?

1.order_date = dbutils.widgets.get(“widget_order_date”)
2.
3.spark.sql(f”SELECT * FROM sales WHERE orderDate = ‘f{order_date }'”)

1.order_date = dbutils.widgets.get(“widget_order_date”)
2.
3.spark.sql(f”SELECT * FROM sales WHERE orderDate = ‘order_date’ “)

1.order_date = dbutils.widgets.get(“widget_order_date”)
2.
3.spark.sql(f”SELECT * FROM sales WHERE orderDate = ‘${order_date }’ “)

1.order_date = dbutils.widgets.get(“widget_order_date”)
2.
3.spark.sql(f”SELECT * FROM sales WHERE orderDate = ‘{order_date}’ “)
(Correct)

1.order_date = dbutils.widgets.get(“widget_order_date”)
2.
3.spark.sql(“SELECT * FROM sales WHERE orderDate = order_date”)

NO.34 You had worked with the Data analysts team to set up a SQL Endpoint(SQL warehouse) point so they can easily query and analyze data in the gold layer, but once they started consuming the SQL Endpoint(SQL warehouse) you noticed that during the peak hours as the number of users increase you are seeing queries taking longer to finish, which of the following steps can be taken to resolve the issue?
*Please note Databricks recently renamed SQL endpoint to SQL warehouse.

They can turn on the Serverless feature for the SQL endpoint(SQL warehouse).

They can increase the maximum bound of the SQL endpoint(SQL warehouse) ‘s scaling range.

They can increase the cluster size from 2X-Small to 4X-Large of the SQL end-point(SQL warehouse) .

They can turn on the Auto Stop feature for the SQL endpoint(SQL warehouse) .

They can turn on the Serverless feature for the SQL endpoint(SQL warehouse) and change the Spot Instance Policy from “Cost optimized” to “Reliability Optimized.”

NO.35 Which of the following commands results in the successful creation of a view on top of the delta stream(stream on delta table)?

Spark.read.format(“delta”).table(“sales”).createOrReplaceTempView(“streaming_vw”)

Spark.readStream.format(“delta”).table(“sales”).createOrReplaceTempView(“streaming_vw”)

Spark.read.format(“delta”).table(“sales”).mode(“stream”).createOrReplaceTempView(“streaming_vw”)

Spark.read.format(“delta”).table(“sales”).trigger(“stream”).createOrReplaceTempView(“streaming_vw”)

Spark.read.format(“delta”).stream(“sales”).createOrReplaceTempView(“streaming_vw”)

You can not create a view on streaming data source.

NO.36 Which of the following is a correct statement on how the data is organized in the storage when when managing a DELTA table?

All of the data is broken down into one or many parquet files, log files are broken down into one or many JSON files, and each transaction creates a new data file(s) and log file.
(Correct)

All of the data and log are stored in a single parquet file

All of the data is broken down into one or many parquet files, but the log file is stored as a single json file, and every transaction creates a new data file(s) and log file gets appended.

All of the data is broken down into one or many parquet files, log file is removed once the transaction is committed.

All of the data is stored into one parquet file, log files are broken down into one or many json files.

NO.37 Which of the following statements can be used to test the functionality of code to test number of rows in the table equal to 10 in python?
row_count = spark.sql(“select count(*) from table”).collect()[0][0]

assert (row_count = 10, “Row count did not match”)

assert if (row_count = 10, “Row count did not match”)

assert row_count == 10, “Row count did not match”

assert if row_count == 10, “Row count did not match”

assert row_count = 10, “Row count did not match”

NO.38 You are asked to write a python function that can read data from a delta table and return the Data-Frame, which of the following is correct?

Python function cannot return a DataFrame

Write SQL UDF to return a DataFrame

Write SQL UDF that can return tabular data

Python function will result in out of memory error due to data volume

Python function can return a DataFrame

NO.39 You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex,
Height, Weight, Age and Income as measures and have used 3 clusters. When you create a pair-wise plot of
the clusters, you notice that there is significant overlap between the clusters. What should you do?

Identify additional measures to add to the analysis

Remove one of the measures

Decrease the number of clusters

Increase the number of clusters

NO.40 A data engineer has ingested data from an external source into a PySpark DataFrame raw_df. They need to
briefly make this data available in SQL for a data analyst to perform a quality assurance check on the data.
Which of the following commands should the data engineer run to make this data available in SQL for only
the remainder of the Spark session?

raw_df.createOrReplaceTempView(“raw_df”)

There is no way to share data between PySpark and SQL

raw_df.createTable(“raw_df”)

raw_df.saveAsTable(“raw_df”)

raw_df.write.save(“raw_df”)

NO.41 How do you create a delta live tables pipeline and deploy using DLT UI?

Within the Workspace UI, click on Workflows, select Delta Live tables and create a pipeline and select the notebook with DLT code.

Under Cluster UI, select SPARK UI and select Structured Streaming and click create pipeline and select the notebook with DLT code.

There is no UI, you can only setup DELTA LIVE TABLES using Python and SQL API and select the notebook with DLT code.

Use VS Code and download DBX plugin, once the plugin is loaded you can build DLT pipelines and select the notebook with DLT code.

Within the Workspace UI, click on SQL Endpoint, select Delta Live tables and create pipelinea and select the notebook with DLT code.

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[Q19-Q41] Exam Questions and Answers for Databricks-Certified-Professional-Data-Engineer Study Guide Questions and Answers!

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