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Databricks Certified Professional Data Engineer Exam Sample Questions (Q106-Q111):

NEW QUESTION # 106
You are currently working on reloading customer_sales tables using the below query
1. INSERT OVERWRITE customer_sales
2. SELECT * FROM customers c
3. INNER JOIN sales_monthly s on s.customer_id = c.customer_id
After you ran the above command, the Marketing team quickly wanted to review the old data that was in the table. How does INSERT OVERWRITE impact the data in the customer_sales table if you want to see the previous version of the data prior to running the above statement?

A. Overwrites the current version of the data but clears all historical versions of the data, so you can not time travel to previous versions.
B. By default, overwrites the data and schema, you cannot perform time travel
C. Appends the data to the current version, you can time travel to previous versions
D. Overwrites the data in the table, all historical versions of the data, you can not time travel to previous versions
E. Overwrites the data in the table but preserves all historical versions of the data, you can time travel to previous versions

Answer: E

Explanation:
Explanation
The answer is, INSERT OVERWRITE Overwrites the current version of the data but preserves all historical versions of the data, you can time travel to previous versions.
1.INSERT OVERWRITE customer_sales
2.SELECT * FROM customers c
3.INNER JOIN sales s on s.customer_id = c.customer_id
Let's just assume that this is the second time you are running the above statement, you can still query the prior version of the data using time travel, and any DML/DDL except DROP TABLE creates new PARQUET files so you can still access the previous versions of data.
SQL Syntax for Time travel
SELECT * FROM table_name as of [version number]
with customer_sales example
SELECT * FROM customer_sales as of 1 -- previous version
SELECT * FROM customer_sales as of 2 -- current version
You see all historical changes on the table using DESCRIBE HISTORY table_name Note: the main difference between INSERT OVERWRITE and CREATE OR REPLACE TABLE(CRAS) is that CRAS can modify the schema of the table, i.e it can add new columns or change data types of existing columns. By default INSERT OVERWRITE only overwrites the data.
INSERT OVERWRITE can also be used to update the schema when
spark.databricks.delta.schema.autoMerge.enabled is set true if this option is not enabled and if there is a schema mismatch command INSERT OVERWRITEwill fail.
Any DML/DDL operation(except DROP TABLE) on the Delta table preserves the historical ver-sion of the data.

NEW QUESTION # 107
The team has decided to take advantage of table properties to identify a business owner for each table, which of the following table DDL syntax allows you to populate a table property identifying the business owner of a table CREATE TABLE inventory (id INT, units FLOAT)

A. SET TBLPROPERTIES business_owner = 'supply chain'
CREATE TABLE inventory (id INT, units FLOAT)
B. CREATE TABLE inventory (id INT, units FLOAT)
SET PROPERTY (business_owner = 'supply chain')
C. CREATE TABLE inventory (id INT, units FLOAT)
SET TAG (business_owner = 'supply chain')
D. CREATE TABLE inventory (id INT, units FLOAT)
SET (business_owner = 'supply chain')
E. TBLPROPERTIES (business_owner = 'supply chain')

Answer: E

Explanation:
Explanation
CREATE TABLE inventory (id INT, units FLOAT) TBLPROPERTIES (business_owner = 'supply chain') Table properties and table options (Databricks SQL) | Databricks on AWS Alter table command can used to update the TBLPROPERTIES ALTER TABLE inventory SET TBLPROPERTIES(business_owner , 'operations')

NEW QUESTION # 108
A table in the Lakehouse namedcustomer_churn_paramsis used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

A. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
B. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.
C. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
D. Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
E. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.

Answer: A

Explanation:
Explanation
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with an existing cluster id and a notebook task, but also specifies a new cluster spec with some configurations. According to the documentation, if both an existing cluster id and a new cluster spec are provided, then a new cluster will be created for each run of the job with those configurations, and then terminated after completion. Therefore, the logic defined in the referenced notebook will be executed three times on new clusters with those configurations. Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; Databricks Documentation, under
"JobsClusterSpecNewCluster" section.

NEW QUESTION # 109
What steps need to be taken to set up a DELTA LIVE PIPELINE as a job using the workspace UI?

A. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the pipeline JSON file
B. DELTA LIVE TABLES do not support job cluster
C. Use Pipeline creation UI, select a new pipeline and job cluster
D. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook

Answer: D

Explanation:
Explanation
The answer is,
Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook.
Create a pipeline
To create a new pipeline using the Delta Live Tables notebook:
1.Click Workflows in the sidebar, click the Delta Live Tables tab, and click Create Pipeline.
2.Give the pipeline a name and click to select a notebook.
3.Optionally enter a storage location for output data from the pipeline. The system uses a de-fault location if you leave Storage Location empty.
4.Select Triggered for Pipeline Mode.
5.Click Create.
The system displays the Pipeline Details page after you click Create. You can also access your pipeline by clicking the pipeline name in the Delta Live Tables tab.

NEW QUESTION # 110
You are asked to create a model to predict the total number of monthly subscribers for a specific magazine.
You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years
worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building
a predictive model for subscribers?

A. Logistic regression
B. TF-IDF
C. Decision trees
D. Linear regression

Answer: D

NEW QUESTION # 111
......

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