Databricks DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE Online Practice Questions and Exam Preparation

Databricks DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE Online Questions & Answers

• Question 51:

  Which of the following statements best describes the role of Silver tables in the Medallion architecture?

  A. Store raw ingested data from source systems.
  B. Store cleaned and refined data ready for analysis.
  C. Store business-level aggregated data.
  D. Store archived data for compliance.
  E. Store feature-engineered datasets for ML.
  B. Store cleaned and refined data ready for analysis.

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  Explanation

  Silver tables act as the "cleansed and conformed" data layer between Bronze (raw) and Gold (aggregated) layers.

• Question 52:

  A data engineer needs to develop integration tests for an ETL process and deploy a version-controlled, packaged workflow into production using an external job scheduler. Which tool should the data engineer use for this job?

  A. Databricks Connect
  B. Databricks Asset Bundles
  C. Databricks Command Line Interface
  D. Databricks Software Development Kit
  B. Databricks Asset Bundles

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  Explanation

  Databricks Asset Bundles enable packaging of workflows with version control, allowing integration testing and deployment into production while supporting orchestration by external job schedulers.

• Question 53:

  Which SQL code snippet will correctly demonstrate a Data Definition Language (DDL) operation used to create a table?

  A. CREATE TABLE employees ( id INT, name STRING );
  B. DROP TABLE employees;
  C. ALTER TABLE employees ADD COLUMN salary DECIMAL(10,2);
  D. INSERT INTO employees (id, name) VALUES (1 'Alice');
  A. CREATE TABLE employees ( id INT, name STRING );

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  Explanation

  CREATE TABLE employees (...) is a Data Definition Language (DDL) operation that defines a new table and its schema.

• Question 54:

  A data engineer has written a function in a Databricks Notebook to calculate the population of bacteria in a given medium.

  def calculate_population_of_bacteria (intital_population, exponential_factor):

  return future_population = intital_population ** exponential_factor

  Analysts use this function in the notebook and sometimes provide input arguments of the wrong data type, which can cause errors during execution.

  Which Databricks feature will help the data engineer quickly identify if an incorrect data type has been provided as input?

  A. The Spark User interface has a debug tab that contains the variables that are used in this session.
  B. The Databricks debugger enables breakpoints that will raise an error if the wrong data type is submitted.
  C. The Databricks debugger enables the use of a variable explorer to see at a glance the value of the variables.
  D. The Data Engineer should add print statements to find out what the variable is.
  B. The Databricks debugger enables breakpoints that will raise an error if the wrong data type is submitted.

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  Explanation

  The Databricks debugger supports setting breakpoints that pause execution and allow inspection of variables. If the wrong data type is passed to the function, the debugger raises an error at runtime, helping the engineer quickly identify the issue.

• Question 55:

  A global retail company sells products across multiple categories (e.g., Electronics, Clothing) and regions (e.g., North, South, East, West). The sales team has provided the data engineer with a PySpark dataframe named sales_df as below and the team wants the data engineer to analyze the sales data to help them make strategic decisions.

  

  Calculate the total sales amount for each product category and store the results in a new dataframe called . category_sales

  What will generate the expected result of category_sales?

  

  A. category_sales = sales_df.groupBy("category").agg(sum("sales_amount").alias ("total_sales_amount"))
  B. category_sales = sales_df.sum("sales_amount").groupBy("category").alias ("total_sales_amount"))
  C. category_sales = sales_df.agg(sum("sales_amount").groupBy("category").alias ("total_sales_amount"))
  D. category_sales = sales_df.groupBy("region").agg(sum("sales_amount").alias ("total_sales_amount"))
  A. category_sales = sales_df.groupBy("category").agg(sum("sales_amount").alias ("total_sales_amount"))

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  Explanation

  The correct approach is to group by the product category and then aggregate the sum of sales_amount, aliasing it as total_sales_amount. This produces the expected totals: 500 for Electronics and 900 for Clothing.

• Question 56:

  A dataset has been defined using Delta Live Tables and includes an expectations clause:

  CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE

  What is the expected behavior when a batch of data containing data that violates these constraints is processed?

  A. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
  B. Records that violate the expectation cause the job to fail.
  C. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
  D. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
  E. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
  B. Records that violate the expectation cause the job to fail.

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  Explanation

  The expected behavior when a batch of data containing data that violates the expectation is processed is that the job will fail. This is because the expectation clause has the ON VIOLATION FAIL UPDATE option, which means that if any

  record in the batch does not meet the expectation, the entire batch will be rejected and the job will fail. This option is useful for enforcing strict data quality rules and preventing invalid data from entering the target dataset. Option A is not

  correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and record them as invalid in the event log, the ON

  VIOLATION DROP RECORD option should be used.

  Option C is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and load them into a quarantine table, the

  ON VIOLATION QUARANTINE RECORD option should be used.

  Option D is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and record them as invalid in the event log,

  the ON VIOLATION LOG RECORD option should be used.

  Option E is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and flag them as invalid in a field added to the

  target dataset, the ON VIOLATION FLAG RECORD option should be used.

  References:

  Delta Live Tables Expectations

  [Databricks Data Engineer Professional Exam Guide]

• Question 57:

  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
  D. Review the Owner field in the table's page in Data Explorer

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  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.

  References:Databricks documentation on Data Explorer: Using Data Explorer in Databricks

• Question 58:

  Which two conditions are applicable for governance in Databricks Unity Catalog? (Choose two.)

  A. You can have more than 1 metastore within a databricks account console but only 1 per region.
  B. Both catalog and schema must have a managed location in Unity Catalog provided metastore is not associated with a location
  C. You can have multiple catalogs within metastore and 1 catalog can be associated with multiple metastore
  D. If catalog is not associated with location, it's mandatory to associate schema with managed locations
  E. If metastore is not associated with location, it's mandatory to associate catalog with managed locations AE
  A. You can have more than 1 metastore within a databricks account console but only 1 per region.
  E. If metastore is not associated with location, it's mandatory to associate catalog with managed locations AE

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  Explanation

• Question 59:

  A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:

  DROP TABLE IF EXISTS my_table;

  After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.

  Which of the following describes why all of these files were deleted?

  A. The table was managed
  B. The table's data was smaller than 10 GB
  C. The table's data was larger than 10 GB
  D. The table was external
  E. The table did not have a location
  A. The table was managed

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  Explanation

  The reason why all of the data files and metadata files were deleted from the file system after dropping the table is that the table was managed. A managed table is a table that is created and managed by Spark SQL. It stores both the data and the metadata in the default location specified by the spark.sql.warehouse.dir configuration property. When a managed table is dropped, both the data and the metadata are deleted from the file system. Option B is not correct, as the size of the table's data does not affect the behavior of dropping the table. Whether the table's data is smaller or larger than 10 GB, the data files and metadata files will be deleted if the table is managed, and will be preserved if the table is external. Option C is not correct, for the same reason as option

  B. Option D is not correct, as an external table is a table that is created and managed by the user. It stores the data in a user-specified location, and only stores the metadata in the Spark SQL catalog. When an external table is dropped, only the metadata is deleted from the catalog, but the data files are preserved in the file system. Option E is not correct, as a table must have a location to store the data. If the location is not specified by the user, it will use the default location for managed tables. Therefore, a table without a location is a managed table, and dropping it will delete both the data and the metadata.

  References: Managing Tables [Databricks Data Engineer Professional Exam Guide]

• Question 60:

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

  A. Worker node
  B. JDBC data source
  C. Databricks web application
  D. Databricks Filesystem
  E. Driver node
  C. Databricks web application

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  Explanation

  The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. The compute plane is in your own cloud account and network.

  References: Databricks architecture overview, Security and Trust Center