Databricks-Certified-Data-Engineer-Associate Exam Dumps - Databricks Certification Questions and Answers

 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. References: Schedule a query, Schedule a query - Azure Databricks - Databricks SQL

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.

One of the benefits of the Databricks Lakehouse Platform embracing open source technologies is that it avoids vendor lock-in. This means that customers can use the same open source tools and frameworks across different cloud providers, and migrate their data and workloads without being tied to a specific vendor. The Databricks Lakehouse Platform is built on open source projects such as Apache Spark™, Delta Lake, MLflow, and Redash, which are widely used and trusted by millions of developers. By supporting these open source technologies, the Databricks Lakehouse Platform enables customers to leverage the innovation and community of the open source ecosystem, and avoid the risk of being locked into proprietary or closed solutions. The other options are either not related to open source technologies (A, B, C, D), or not benefits of the Databricks Lakehouse Platform (A, B). References: Databricks Documentation - Built on open source, Databricks Documentation - What is the Lakehouse Platform?, Databricks Blog - Introducing the Databricks Lakehouse Platform.

Delta Sharing is an open protocol developed by Databricks that enables secure, real-time sharing of data across organizations without requiring recipients to have a Databricks account. It is specifically designed for scenarios where external partners need read-only access to data stored in Delta format. With Delta Sharing, data providers create a share and grant access to recipients via a secure mechanism (such as a profile file or URL-based access), ensuring that the data cannot be modified by the consumer. This approach eliminates the need for data duplication or manual file transfers, maintaining data freshness and governance. Option B is incorrect because Unity Catalog governs internal access within an organization, not external sharing without accounts. Option C is inefficient and breaks data lineage and freshness. Option D violates security best practices by granting excessive permissions. Delta Sharing ensures fine- grained access control, auditability, and secure external collaboration , making it the correct solution for this requirement.

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 The spark.sql method can be used to execute SQL queries programmatically and return the result as a DataFrame. The spark.sql method accepts a string argument that contains a valid SQL statement. The data engineer can use a formatted string literal (f-string) to insert the Python variable table_name into the SQL query. The other methods are either invalid or not suitable for running SQL queries. References: Running SQL Queries Programmatically, Formatted string literals, spark.sql

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.

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]

In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is “sqlite” because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, “sqlite” would be appropriate here. References:

Create a table using JDBC

JDBC connection string

SQLite JDBC driver