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| Section | Objectives |
|---|---|
| Productionizing Data Pipelines | - Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization - Scheduling and monitoring jobs |
| Data Governance and Quality | - Data access control and governance - Unity Catalog basics - Data quality concepts and management |
| Databricks Lakehouse Platform Fundamentals | - Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage |
| Data Processing and Transformations | - PySpark DataFrame transformations - Delta Lake fundamentals (tables, transactions, optimization) - Apache Spark SQL operations (joins, aggregations, filtering) - User-defined functions (UDFs) |
| Data Ingestion and ELT Development | - Handling structured and semi-structured data - Data ingestion using Spark SQL and PySpark - ETL patterns and transformations |
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NEW QUESTION # 238
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?
Answer: A
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.
References:Databricks documentation on JDBC: Connecting to SQL Databases using JDBC
NEW QUESTION # 239
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?
Answer: A
NEW QUESTION # 240
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:
Which of the following lines of code fills in the above blank to successfully complete the task?
Answer: B
Explanation:
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
NEW QUESTION # 241
A company sells products across multiple categories (e.g., Electronics, Clothing) and regions.
The sales team has provided you with a PySpark dataframe named sales_df as below, and the team wants the data engineer to analyze the sales data to help make strategic decisions.
Calculate the total sales amount for each region and store the results in a new dataframe called region_sales.
Given the expected result:
Which code will generate the expected result?
Answer: C
Explanation:
To calculate total sales per region, you group by "region" and apply an aggregation using sum("sales_amount"), aliasing it as total_sales_amount. This produces the expected result of 250 (North), 500 (South), 250 (East), and 400 (West).
NEW QUESTION # 242
Which of the following describes the type of workloads that are always compatible with Auto Loader?
Answer: B
NEW QUESTION # 243
......
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