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Question # 1
Which two components function in the DB platform architecture's control plane? (Choose two.)
A. Virtual Machines B. Compute Orchestration C. Serverless Compute D. Compute E. Unity Catalog
Answer: BE Explanation:
Question # 2
Identify the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE for a constraint violation.
A data engineer has created an ETL pipeline using Delta Live table to manage their company travel
reimbursement detail, they want to ensure that the if the location details has not been provided by
the employee, the pipeline needs to be terminated.
How can the scenario be implemented?
A. CONSTRAINT valid_location EXPECT (location = NULL) B. CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL UPDATE C. CONSTRAINT valid_location EXPECT (location != NULL) ON DROP ROW D. CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL
Answer: B Explanation:
Question # 3
Which method should a Data Engineer apply to ensure Workflows are being triggered on schedule?
A. Scheduled Workflows require an always-running cluster, which is more expensive but reduces processing latency. B. Scheduled Workflows process data as it arrives at configured sources. C. Scheduled Workflows can reduce resource consumption and expense since the cluster runs only long enough to execute the pipeline. D. Scheduled Workflows run continuously until manually stopped.
Answer: C Explanation:
Question # 4
Identify a scenario to use an external table.
A Data Engineer needs to create a parquet bronze table and wants to ensure that it gets stored in a
specific path in an external location.
Which table can be created in this scenario?
A. An external table where the location is pointing to specific path in external location. B. An external table where the schema has managed location pointing to specific path in external location. C. A managed table where the catalog has managed location pointing to specific path in external location. D. A managed table where the location is pointing to specific path in external location.
Answer: A Explanation:
Question # 5
Identify how the count_if function and the count where x is null can be used
Consider a table random_values with below data.
What would be the output of below query?
select count_if(col > 1) as count_
a. count(*) as count_b.count(col1) as count_c from random_values col1
012
NULL -
23
A. 3 6 5 B. 4 6 5 C. 3 6 6 D. 4 6 6
Answer: A Explanation:
Question # 6
A data engineer needs access to a table new_uable, but they do not have the correct permissions.
They can ask the table owner for permission, but they do not know who the table owner is.
Which approach can be used to identify the owner of new_table?
A. There is no way to identify the owner of the table B. Review the Owner field in the table's page in the cloud storage solution C. Review the Permissions tab in the table's page in Data Explorer D. Review the Owner field in the table's page in Data Explorer
Answer: D Explanation: To find the owner of a table in Databricks, one can utilize the Data Explorer feature. The Data Explorer provides detailed information about various data objects, including tables. By navigating to the specific table's page in Data Explorer, a data engineer can review the Owner field, which identifies the individual or role that owns the table. This information is crucial for obtaining the necessary permissions or for any administrative actions related to the table. Reference: Databricks documentation on Data Explorer: Using Data Explorer in Databricks
Question # 7
A data engineer wants to create a new table containing the names of customers who live in France.
They have written the following command:
CREATE TABLE customersInFrance
_____ AS
SELECT id,
firstName,
lastName
FROM customerLocations
WHERE country = 'FRANCE';
A senior data engineer mentions that it is organization policy to include a table property indicating
that the new table includes personally identifiable information (Pll).
Which line of code fills in the above blank to successfully complete the task?
A. COMMENT "Contains PIT B. 511 C. "COMMENT PII" D. TBLPROPERTIES PII
Answer: D Explanation: To include a property indicating that a table contains personally identifiable information (PII), the TBLPROPERTIES keyword is used in SQL to add metadata to a table. The correct syntax to define a table property for PII is as follows: CREATE TABLE customersInFrance USING DELTA TBLPROPERTIES ('PII' = 'true') AS SELECT id, firstName, lastName FROM customerLocations WHERE country = 'FRANCE'; The TBLPROPERTIES ('PII' = 'true') line correctly sets a table property that tags the table as containing personally identifiable information. This is in accordance with organizational policies for handling sensitive information. Reference: Databricks documentation on Delta Lake: Delta Lake on Databricks
Question # 8
A data engineer needs to create a table in Databricks using data from their organization's existing
SQLite database. They run the following command:
CREATE TABLE jdbc_customer360
USING
OPTIONS (
url "jdbc:sqlite:/customers.db", dbtable "customer360"
)
Which line of code fills in the above blank to successfully complete the task?
A. autoloader B. org.apache.spark.sql.jdbc C. sqlite D. org.apache.spark.sql.sqlite
Answer: B Explanation: To create a table in Databricks using data from an SQLite database, the correct syntax involves specifying the format of the data source. The format in the case of using JDBC (Java Database Connectivity) with SQLite is specified by the org.apache.spark.sql.jdbc format. This format allows Spark to interface with various relational databases through JDBC. Here is how the command should be structured: CREATE TABLE jdbc_customer360 USING org.apache.spark.sql.jdbc OPTIONS ( url "jdbc:sqlite:/customers.db", dbtable "customer360" ) The USING org.apache.spark.sql.jdbc line specifies that the JDBC data source is being used, enabling Spark to interact with the SQLite database via JDBC. Reference: Databricks documentation on JDBC: Connecting to SQL Databases using JDBC
Question # 9
What is stored in a Databricks customer's cloud account?
A. Data B. Cluster management metadata C. Databricks web application D. Notebooks
Answer: A Explanation: In a Databricks customer's cloud account, the primary elements stored include: Data: This is the central type of content stored in the customer's cloud account. Data might include various datasets, tables, and files that are used and managed through Databricks platforms. Notebooks: These are also stored within a customer's cloud account. Notebooks include scripts, notes, and other information necessary for data analysis and processing tasks. Cluster management metadata is indeed managed through the cloud, but it's primarily handled by Databricks rather than stored directly in the customer's account. The Databricks web application itself is not stored within the customer's cloud account; rather, it's a service provided by Databricks. Reference: Databricks documentation: Data in Databricks
Question # 10
Which file format is used for storing Delta Lake Table?
A. Parquet B. Delta C. SV D. JSON
Answer: A Explanation: Delta Lake tables use the Parquet format as their underlying storage format. Delta Lake enhances Parquet by adding a transaction log that keeps track of all the operations performed on the table. This allows features like ACID transactions, scalable metadata handling, and schema enforcement, making it an ideal choice for big data processing and management in environments like Databricks. Reference: Databricks documentation on Delta Lake: Delta Lake Overview
Question # 11
Which of the following describes the type of workloads that are always compatible with Auto
Loader?
A. Dashboard workloads B. Streaming workloads C. Machine learning workloads D. Serverless workloads E. Batch workloads
Answer: B Explanation: Auto Loader is a Structured Streaming source that incrementally and efficiently processes new data files as they arrive in cloud storage. It supports both Python and SQL in Delta Live Tables, which are ideal for building streaming data pipelines. Auto Loader can handle near real-time ingestion of millions of files per hour and provide exactly-once guarantees when writing data into Delta Lake. Auto Loader is not designed for dashboard, machine learning, serverless, or batch workloads, which have different requirements and characteristics. Reference: What is Auto Loader?, Delta Live Tables
Question # 12
Which of the following SQL keywords can be used to convert a table from a long format to a wide
format?
A. PIVOT B. CONVERT C. WHERE D. TRANSFORM E. SUM
Answer: A Explanation: The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1. The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user-defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column. Reference: 1: PIVOT | Databricks on AWS 2: Reshaping Data - Long vs Wide Format | Databricks on AWS 3: CONVERT | Databricks on AWS 4: WHERE | Databricks on AWS 5: TRANSFORM | Databricks on AWS : [SUM | Databricks on AWS]
Question # 13
A data engineering team has noticed that their Databricks SQL queries are running too slowly when
they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be
resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in
this scenario?
A. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to "Reliability Optimized." B. They can turn on the Auto Stop feature for the SQL endpoint. C. They can increase the cluster size of the SQL endpoint. D. They can turn on the Serverless feature for the SQL endpoint. E. They can increase the maximum bound of the SQL endpoint's scaling range
Answer: D Explanation: Option D is the correct answer because it enables the Serverless feature for the SQL endpoint, which allows the endpoint to automatically scale up and down based on the query load. This way, the endpoint can handle more concurrent queries and reduce the time it takes to return results. The Serverless feature also reduces the cold start time of the endpoint, which is the time it takes to start the cluster when a query is submitted to a non-running endpoint. The Serverless feature is available for both AWS and Azure Databricks platforms. Reference: Databricks SQL Serverless, Serverless SQL endpoints, New Performance Improvements in Databricks SQL
Question # 14
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they
have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?
A. Databricks Filesystem B. Jobs C. Dashboards D. Repos E. Data Explorer
Answer: E Explanation: Data Explorer is a graphical interface that allows you to browse, create, and manage data objects such as databases, tables, and views in your workspace. You can also review and modify the permissions on these data objects using Data Explorer. To access Data Explorer, you can click on the Data icon in the sidebar, or use the %sql magic command in a notebook. You can then select a database and a table, and click on the Permissions tab to view and edit the access control lists (ACLs) for the table. You can also use SQL commands such as SHOW GRANT and GRANT to query and modify the permissions on a Delta table. Reference: Data Explorer Access control for Delta tables SHOW GRANT [GRANT]
Question # 15
A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the
notebooks is running slowly in the Job's current run. The data engineer asks a tech lead for help in
identifying why this might be the case.
Which of the following approaches can the tech lead use to identify why the notebook is running
slowly as part of the Job?
A. They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook. B. They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook. C. They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook. D. There is no way to determine why a Job task is running slowly. E. They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.
Answer: C Explanation:
Question # 16
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?
A. They can increase the cluster size of the SQL endpoint. B. They can increase the maximum bound of the SQL endpoint's scaling range. C. They can turn on the Auto Stop feature for the SQL endpoint. D. They can turn on the Serverless feature for the SQL endpoint. E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to œReliability Optimized.
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?
A. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint. B. They can set the query's refresh schedule to end after a certain number of refreshes. C. They cannot ensure the query does not cost the organization money beyond the first week of the project's release. D. They can set a limit to the number of individuals that are able to manage the query's refresh schedule. E. They can set the query's refresh schedule to end on a certain date in the query scheduler.
Answer: E Explanation: In Databricks SQL, you can use scheduled query executions to update your dashboards or enable routine alerts. By default, your queries do not have a schedule. To set the schedule, you can use the dropdown pickers to specify the frequency, period, starting time, and time zone. You can also choose to end the schedule on a certain date by selecting the End date checkbox and picking a date from the calendar. This way, you can ensure that the query does not run beyond the first week of the project's release and does not incur any additional cost. Option A is incorrect, as setting a limit to the number of DBUs does not stop the query from running. Option B is incorrect, as there is no option to end the schedule after a certain number of refreshes. Option C is incorrect, as there is a way to ensure the query does not cost the organization money beyond the first week of the project's release. Option D is incorrect, as setting a limit to the number of individuals who can manage the query's refresh schedule does not affect the query's execution or cost. Reference: Schedule a query, Schedule a query - Azure Databricks - Databricks SQL
Question # 18
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?
A. if day_of_week = 1 and review_period: B. if day_of_week = 1 and review_period = "True": C. if day_of_week == 1 and review_period == "True": ] D. if day_of_week == 1 and review_period: E. if day_of_week = 1 & review_period: = "True":
Answer: D Explanation: In Python, the == operator is used to compare the values of two variables, while the = operator is used to assign a value to a variable. Therefore, option A and E are incorrect, as they use the = operator for comparison. Option B and C are also incorrect, as they compare the review_period variable to a string value "True", which is different from the boolean value True. Option D is the correct answer, as it uses the == operator to compare the day_of_week variable to the integer value 1, and the and operator to check if both conditions are true. If both conditions are true, then the final block of the Python program will be executed. Reference: [Python Operators], [Python If ¦ Else
Question # 19
Which of the following benefits is provided by the array functions from Spark SQL?
A. An ability to work with data in a variety of types at once B. An ability to work with data within certain partitions and windows C. An ability to work with time-related data in specified intervals D. An ability to work with complex, nested data ingested from JSON files E. An ability to work with an array of tables for procedural automation
Answer: D Explanation: The array functions from Spark SQL are a subset of the collection functions that operate on array columns1. They provide an ability to work with complex, nested data ingested from JSON files or other sources2. For example, the explode function can be used to transform an array column into multiple rows, one for each element in the array3. The array_contains function can be used to check if a value is present in an array column4. The array_join function can be used to concatenate all elements of an array column with a delimiter. These functions can be useful for processing JSON data that may have nested arrays or objects. Reference: 1: Spark SQL, Built-in Functions - Apache Spark 2: Spark SQL Array Functions Complete List - Spark By Examples 3: Spark SQL Array Functions - Syntax and Examples - DWgeek.com 4: Spark SQL, Built-in Functions - Apache Spark : Spark SQL, Built-in Functions - Apache Spark : [Working with Nested Data Using Higher Order Functions in SQL on Databricks - The Databricks Blog]
Question # 20
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?
A. SELECT * FROM sales B. There is no way to share data between PySpark and SQL. C. spark.sql("sales") D. spark.delta.table("sales") E. spark.table("sales")
Answer: E Explanation: The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.forPath method to load the Delta table from its path2. Reference: 1: SparkSession | PySpark 3.2.0 documentation 2: Welcome to Delta Lake's Python documentation page ” delta-spark 2.4.0 documentation
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