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Databricks-Certified-Data-Engineer-Associate Exam Questions
- Topic 1: Databricks Lakehouse Platform: This topic covers the relationship between the data lakehouse and the data warehouse, the improvement in data quality, comparing and contrasting silver and gold tables, elements of the Databricks Platform Architecture, and differentiating between all-purpose clusters and jobs clusters. Moreover, it identifies how cluster software is versioned, how clusters can be filtered, how to use multiple languages, how to run one notebook, how notebooks can be shared, Git operations, and limitations in Databricks Notebooks. Lastly, the topic describes how clusters are terminated, how to use multiple languages, and how Databricks Repos enables CI/CD workflows.
- Topic 2: ELT with Apache Spark: It focuses on extracting data, identifying the prefix, creating a view, duplicating rows, creating a new table, utilizing the dot, parsing JSON, and defining a SQL UDF. Moreover, the topic delves into describing the security model, identifying the location of a function, and identifying the PIVOT.
- Topic 3: Incremental Data Processing: In this topic questions about identifying Delta Lake, benefits of ACID transactions, a scenario to use an external table, location of a table, the benefits of Zordering, the kind of files, CTAS as a solution, the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE, and the necessary component to create a new DLT pipeline. Moreover, the topic also discusses directory structure of Delta Lake files, generated column, adding a table comment, and the benefits of the MERGE command.
- Topic 4: Production Pipelines: It focuses on identifying the advantages of using multiple tasks in Jobs, a suitable scenario where predecessor task should be set up, CRON as an opportunity for scheduling opportunity, and how an alert can be sent via email. The topic also discusses setting up a predecessor task in Jobs, reviewing a task's execution history, and debugging a failed task. Lastly, it delves into setting up a retry policy in case of failure and creating an alert in the case of a failed task.
- Topic 5: Data Governance: It identifies one of the four areas of data governance, Unity Catalog securables, and the cluster security modes. It also discusses how to create a UC-enabled all-purpose cluster and a DBSQL warehouse. The topic explains how to implement data object access control, create a DBSQL warehouse, and e a UC-enabled all-purpose cluster.
Free Databricks Databricks-Certified-Data-Engineer-Associate Exam Actual Questions
Note: Premium Questions for Databricks-Certified-Data-Engineer-Associate were last updated On Dec. 13, 2024 (see below)
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?
2:Reshaping Data - Long vs Wide Format | Databricks on AWS
5:TRANSFORM | Databricks on AWS
: [SUM | Databricks on AWS]
A data engineer needs access to a table new_table, 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 of the following approaches can be used to identify the owner of new_table?
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?
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
A data engineer has created a new database using the following command:
CREATE DATABASE IF NOT EXISTS customer360;
In which of the following locations will the customer360 database be located?
dbfs:/user/hive/warehouse Thereby showing 'dbfs:/user/hive/warehouse/customer360.db
The location of the customer360 database depends on the value of thespark.sql.warehouse.dirconfiguration property, which specifies the default location for managed databases and tables. If the property is not set, the default value isdbfs:/user/hive/warehouse. Therefore, the customer360 database will be located indbfs:/user/hive/warehouse/customer360.db. However, if the property is set to a different value, such asdbfs:/user/hive/database, then the customer360 database will be located indbfs:/user/hive/database/customer360.db. Thus, more information is needed to determine the correct response.
Option A is not correct, asdbfs:/user/hive/database/customer360is not the default location for managed databases and tables, unless thespark.sql.warehouse.dirproperty is explicitly set todbfs:/user/hive/database.
Option B is not correct, asdbfs:/user/hive/warehouseis the default location for the root directory of managed databases and tables, not for a specific database. The database name should be appended with.dbto the directory path, such asdbfs:/user/hive/warehouse/customer360.db.
Option C is not correct, asdbfs:/user/hive/customer360is not a valid location for a managed database, as it does not follow the directory structure specified by thespark.sql.warehouse.dirproperty.
[Databricks Data Engineer Professional Exam Guide]
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.
What is the expected outcome after clicking Start to update the pipeline assuming previously unprocessed data exists and all definitions are valid?
In Delta Live Tables (DLT), when configured to run in Continuous Pipeline Mode, particularly in a production environment, the system is designed to continuously process and update data as it becomes available. This mode keeps the compute resources active to handle ongoing data processing and automatically updates all datasets defined in the pipeline at predefined intervals. Once the pipeline is manually stopped, the compute resources are terminated to conserve resources and reduce costs. This mode is suitable for production environments where datasets need to be kept up-to-date with the latest data.
Reference: Databricks documentation on Delta Live Tables: Delta Live Tables Guide
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