QUIZ DATABRICKS - DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER - HIGH HIT-RATE EXAM DATABRICKS CERTIFIED PROFESSIONAL DATA ENGINEER EXAM TESTKING

Quiz Databricks - Databricks-Certified-Professional-Data-Engineer - High Hit-Rate Exam Databricks Certified Professional Data Engineer Exam Testking

Quiz Databricks - Databricks-Certified-Professional-Data-Engineer - High Hit-Rate Exam Databricks Certified Professional Data Engineer Exam Testking

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Databricks Certified Professional Data Engineer certification is globally recognized by various industries, including finance, healthcare, government, and technology. Databricks Certified Professional Data Engineer Exam certification validates the candidate's knowledge level in data engineering solutions and qualifies them to work with Databricks's technology. The certified professional can optimize and manage their organization's data in the cloud using Databricks, which results in timely and informed decisions.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
Which of the following SQL statement can be used to query a table by eliminating duplicate rows from the query results?

  • A. SELECT * FROM table_name GROUP BY * HAVING COUNT(*) < 1
  • B. SELECT DISTINCT * FROM table_name
  • C. SELECT DISTINCT_ROWS (*) FROM table_name
  • D. SELECT DISTINCT * FROM table_name HAVING COUNT(*) > 1
  • E. SELECT * FROM table_name GROUP BY * HAVING COUNT(*) > 1

Answer: B

Explanation:
Explanation
The answer is SELECT DISTINCT * FROM table_name


NEW QUESTION # 36
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame namedpredswith the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

  • A.
  • B. preds.write.mode("append").saveAsTable("churn_preds")
  • C.
  • D. preds.write.format("delta").save("/preds/churn_preds")
  • E.

Answer: B


NEW QUESTION # 37
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is namedstore_saies_summaryand the schema is as follows:

The tabledaily_store_salescontains all the information needed to updatestore_sales_summary. The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
Ifdaily_store_salesis implemented as a Type 1 table and thetotal_salescolumn might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in thestore_sales_summarytable?

  • A. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.
  • B. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
  • C. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
  • D. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
  • E. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.

Answer: A

Explanation:
The daily_store_sales table contains all the information needed to update store_sales_summary. The schema of the table is:
store_id INT, sales_date DATE, total_sales FLOAT
The daily_store_sales table is implemented as a Type 1 table, which means that old values are overwritten by new values and no history is maintained. The total_sales column might be adjusted after manual data auditing, which means that the data in the table may change over time.
The safest approach to generate accurate reports in the store_sales_summary table is to use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update. Structured Streaming is a scalable and fault-tolerant stream processing engine built on Spark SQL. Structured Streaming allows processing data streams as if they were tables or DataFrames, using familiar operations such as select, filter, groupBy, or join. Structured Streaming also supports output modes that specify how to write the results of a streaming query to a sink, such as append, update, or complete. Structured Streaming can handle both streaming and batch data sources in a unified manner.
The change data feed is a feature of Delta Lake that provides structured streaming sources that can subscribe to changes made to a Delta Lake table. The change data feed captures both data changes and schema changes as ordered events that can be processed by downstream applications or services. The change data feed can be configured with different options, such as starting from a specific version or timestamp, filtering by operation type or partition values, or excluding no-op changes.
By using Structured Streaming to subscribe to the change data feed for daily_store_sales, one can capture and process any changes made to the total_sales column due to manual data auditing. By applying these changes to the aggregates in the store_sales_summary table with each update, one can ensure that the reports are always consistent and accurate with the latest data. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Structured Streaming" section; Databricks Documentation, under "Delta Change Data Feed" section.


NEW QUESTION # 38
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

  • A. When tables are created, make sure that the external keyword is used in the create table statement.
  • B. When the workspace is being configured, make sure that external cloud object storage has been mounted.
  • C. Whenever a table is being created, make sure that the location keyword is used.
  • D. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
  • E. Whenever a database is being created, make sure that the location keyword is used

Answer: C

Explanation:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it.
Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Create an external table" section.


NEW QUESTION # 39
Below sample input data contains two columns, one cartId also known as session id, and the second column is called items, every time a customer makes a change to the cart this is stored as an array in the table, the Marketing team asked you to create a unique list of item's that were ever added to the cart by each customer, fill in blanks by choosing the appropriate array function so the query produces below expected result as shown below.
Schema: cartId INT, items Array<INT>
Sample Data

1.SELECT cartId, ___ (___(items)) as items
2.FROM carts GROUP BY cartId
Expected result:
cartId items
1 [1,100,200,300,250]

  • A. FLATTEN, COLLECT_UNION
  • B. ARRAY_DISTINCT, ARRAY_UNION
  • C. ARRAY_UNION, COLLECT_SET
  • D. ARRAY_UNION, ARRAY_DISTINT
  • E. ARRAY_UNION, FLATTEN

Answer: C

Explanation:
Explanation
COLLECT SET is a kind of aggregate function that combines a column value from all rows into a unique list ARRAY_UNION combines and removes any duplicates, Graphical user interface, application Description automatically generated with medium confidence


NEW QUESTION # 40
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