Which of the following data lakehouse features results in improved data quality over a traditional data lake?

A data lakehouse supports ACID-compliant transactions.

A data lakehouse provides storage solutions for structured and unstructured data.

A data lakehouse allows the use of SQL queries to examine data.

A data lakehouse stores data in open formats.

A data lakehouse enables machine learning and artificial Intelligence workloads.

A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos. Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

Databricks Repos supports the use of multiple branches

Databricks Repos automatically saves development progress

Databricks Repos allows users to revert to previous versions of a notebook

Databricks Repos provides the ability to comment on specific changes

Databricks Repos is wholly housed within the Databricks Lakehouse Platform

A data engineer has left the organization. The data team needs to transfer ownership of the data engineer’s Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team. Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?

Databricks account representative

A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL. Which of the following commands could the data engineering team use to access sales in PySpark?

There is no way to share data between PySpark and SQL

spark.sql("sales")D. spark.delta .table("sales")

Which of the following commands will return the location of database customer360?

ALTER DATABASE customer360 SET DBPROPERTIES ('location' = '/user'};

Which of the following benefits is provided by the array functions from Spark SQL?

An ability to work with complex, nested data ingested from JSON files

An ability to work with data in a variety of types at once

An ability to work with data within certain partitions and windows

An ability to work with time-related data in specified intervals

An ability to work with an array of tables for procedural automation

A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task. Which of the following approaches could be used by the data engineering team to complete this task?

They could wrap the queries using PySpark and use Python’s control flow system to determine when to run the final query.

They could submit a feature request with Databricks to add this functionality.

They could only run the entire program on Sundays.

They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.

They could redesign the data model to separate the data used in the final query into a new table.

A data engineer runs a statement every day to copy the previous day’s sales into the table transactions. Each day’s sales are in their own file in the location "/transactions/raw". Today, the data engineer runs the following command to complete this task: After running the command today, the data engineer notices that the number of records in table transactions has not changed. Which of the following describes why the statement might not have copied any new records into the table?

The previous day’s file has already been copied into the table.

The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.

The names of the files to be copied were not included with the FILES keyword.

The PARQUET file format does not support COPY INTO.

The COPY INTO statement requires the table to be refreshed to view the copied rows.

A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables. Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?

CREATE TABLE all_transactions AS SELECT FROM march_transactions UNION SELECT FROM april_transactions;

CREATE TABLE all_transactions AS SELECT FROM march_transactions INNER JOIN SELECT FROM april_transactions;

CREATE TABLE all_transactions AS SELECT FROM march_transactions OUTER JOIN SELECT FROM april_transactions;

CREATE TABLE all_transactions AS SELECT FROM march_transactions INTERSECT SELECT from april_transactions;

CREATE TABLE all_transactions AS SELECT FROM march_transactions MERGE SELECT FROM april_transactions;

A data engineer only wants to execute the final block of a Python program if the Python variable day_of_week is equal to 1 and the Python variable review_period is True. Which of the following control flow statements should the data engineer use to begin this conditionally executed code block?

if day_of_week == 1 and review_period:

if day_of_week = 1 and review_period:

if day_of_week = 1 and review_period = "True":

if day_of_week == 1 and review_period == "True":

if day_of_week = 1 & review_period: = "True":

A data engineer is attempting to drop a Spark SQL table my_table. The data engineer wants to delete all table metadata and data. They run the following command: DROP TABLE IF EXISTS my_table - While the object no longer appears when they run SHOW TABLES, the data files still exist. Which of the following describes why the data files still exist and the metadata files were deleted?

The table’s data was larger than 10 GB

The table’s data was smaller than 10 GB

The table did not have a location

A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE. The table is configured to run in Production mode using the Continuous Pipeline Mode. Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.

All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused

All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.

In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?

Checkpointing and Write-ahead Logs

Structured Streaming cannot record the offset range of the data being processed in each trigger.

Replayable Sources and Idempotent Sinks

Write-ahead Logs and Idempotent Sinks

Checkpointing and Idempotent Sinks

Which of the following describes the relationship between Gold tables and Silver tables?

Gold tables are more likely to contain aggregations than Silver tables.

Gold tables are more likely to contain valuable data than Silver tables.

Gold tables are more likely to contain a less refined view of data than Silver tables.

Gold tables are more likely to contain more data than Silver tables.

Gold tables are more likely to contain truthful data than Silver tables.

Which of the following describes the relationship between Bronze tables and raw data?

Bronze tables contain raw data with a schema applied.

Bronze tables contain less data than raw data files.

Bronze tables contain more truthful data than raw data.

Bronze tables contain aggregates while raw data is unaggregated.

Bronze tables contain a less refined view of data than raw data.

A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table. The cade block used by the data engineer is below: If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

trigger(processingTime="5 seconds")

trigger(continuous="5 seconds")

A dataset has been defined using Delta Live Tables and includes an expectations clause: CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION DROP ROW What is the expected behavior when a batch of data containing data that violates these constraints is processed?

Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.

Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.

Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.

Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.

Records that violate the expectation cause the job to fail.

Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?

CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.

CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.

CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.

CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.

CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.

A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped. Which of the following approaches can the data engineer take to identify the table that is dropping the records?

They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.

They can set up separate expectations for each table when developing their DLT pipeline.

They cannot determine which table is dropping the records.

They can set up DLT to notify them via email when records are dropped.

They can navigate to the DLT pipeline page, click on the “Error” button, and review the present errors.

A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task. Which of the following approaches can the data engineer use to set up the new task?

They can create a new task in the existing Job and then add it as a dependency of the original task.

They can clone the existing task in the existing Job and update it to run the new notebook.

They can create a new task in the existing Job and then add the original task as a dependency of the new task.

They can create a new job from scratch and add both tasks to run concurrently.

They can clone the existing task to a new Job and then edit it to run the new notebook.

An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query. For the first week following the project’s release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project’s release. Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project’s release?

They can set the query’s refresh schedule to end on a certain date in the query scheduler.

They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.

They can set the query’s refresh schedule to end after a certain number of refreshes.

They cannot ensure the query does not cost the organization money beyond the first week of the project’s release.

They can set a limit to the number of individuals that are able to manage the query’s refresh schedule.

A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team’s queries uses the same SQL endpoint. Which of the following approaches can the data engineering team use to improve the latency of the team’s queries?

They can increase the maximum bound of the SQL endpoint’s scaling range.

They can increase the cluster size of the SQL endpoint.

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

They can turn on the Serverless feature for the SQL endpoint.

They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to “Reliability Optimized.”

A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary. Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

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

They can ensure the dashboard’s SQL endpoint matches each of the queries’ SQL endpoints.

They can set up the dashboard’s SQL endpoint to be serverless.

They can reduce the cluster size of the SQL endpoint.

They can ensure the dashboard’s SQL endpoint is not one of the included query’s SQL endpoint.

A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100. Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?

They can set up an Alert with a new webhook alert destination.

They can set up an Alert with a custom template.

They can set up an Alert with a new email alert destination.

They can set up an Alert with one-time notifications.

They can set up an Alert without notifications.

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41 questions

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MULTIPLE CHOICE QUESTION

30 sec • 1 pt

A data organization leader is upset about the data analysis team’s reports being different from the data engineering team’s reports. The leader believes the siloed nature of their organization’s data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?

Both teams would autoscale their work as data size evolves

Both teams would use the same source of truth for their work

Both teams would reorganize to report to the same department

Both teams would be able to collaborate on projects in real-time

Both teams would respond more quickly to ad-hoc requests

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following describes a scenario in which a data team will want to utilize cluster pools?

An automated report needs to be refreshed as quickly as possible.

An automated report needs to be made reproducible.

An automated report needs to be tested to identify errors.

An automated report needs to be version-controlled across multiple collaborators.

An automated report needs to be runnable by all stakeholders.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following is hosted completely in the control plane of the classic Databricks architecture?

Worker node

JDBC data source

Databricks web application

Databricks Filesystem

Driver node

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

The ability to manipulate the same data using a variety of languages

The ability to collaborate in real time on a single notebook

The ability to set up alerts for query failures

The ability to support batch and streaming workloads

The ability to distribute complex data operations

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following describes the storage organization of a Delta table?

Delta tables are stored in a single file that contains data, history, metadata, and other attributes.

Delta tables store their data in a single file and all metadata in a collection of files in a separate location.

Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.

Delta tables are stored in a collection of files that contain only the data stored within the table.

Delta tables are stored in a single file that contains only the data stored within the table.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?

SELECT * FROM my_table WHERE age > 25;

UPDATE my_table WHERE age > 25;

DELETE FROM my_table WHERE age > 25;

UPDATE my_table WHERE age <= 25;

DELETE FROM my_table WHERE age <= 25;

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?

The VACUUM command was run on the table

The TIME TRAVEL command was run on the table

The DELETE HISTORY command was run on the table

The OPTIMIZE command was nun on the table

The HISTORY command was run on the table

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databricks data engineer certificate

41 questions

Which of the following describes a scenario in which a data team will want to utilize cluster pools?

Which of the following is hosted completely in the control plane of the classic Databricks architecture?

Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

Which of the following describes the storage organization of a Delta table?

Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?

Which of the following Git operations must be performed outside of Databricks Repos?

Which of the following data lakehouse features results in improved data quality over a traditional data lake?

A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

Access all questions and much more by creating a free account

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A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?