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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Lakehouse Platform Concepts	10-15%	- Explain data governance and security concepts - Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits
Topic 2: Delta Lake Fundamentals	20-25%	- Explain Delta Lake features and benefits - Understand ACID transactions and time travel - Write to and read from Delta tables - Create and manage Delta tables
Topic 3: Python for Data Engineering	10-15%	- Work with Spark APIs in Python - Implement user-defined functions (UDFs) - Use PySpark for data processing
Topic 4: Apache Spark Data Processing Fundamentals	20-25%	- Create and use Spark DataFrames - Use Spark SQL for data processing - Work with structured data types (arrays, maps, structs) - Apply transformations and actions on DataFrames
Topic 5: Spark SQL and DataFrames	15-20%	- Aggregate and group data - Write and execute Spark SQL queries - Join and union DataFrames - Handle null values and data quality
Topic 6: Data Pipeline Architecture	15-20%	- Understand ELT vs ETL patterns - Implement incremental data processing - Monitor and optimize pipeline performance - Design data pipelines for batch and streaming

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q200-Q205):

NEW QUESTION # 200
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?

A. They can set up an Alert with a custom template.
B. They can set up an Alert without notifications.
C. They can set up an Alert with a new email alert destination.
D. They can set up an Alert with a new webhook alert destination.
E. They can set up an Alert with one-time notifications.

Answer: D

Explanation:
A webhook alert destination is a notification destination that allows Databricks to send HTTP POST requests to a third-party endpoint when an alert is triggered. This enables the data engineer to integrate Databricks alerts with their preferred messaging or collaboration platform, such as Slack, Microsoft Teams, or PagerDuty. To set up a webhook alert destination, the data engineer needs to create and configure a webhook connector in their messaging platform, and then add the webhook URL to the Databricks notification destination. After that, the data engineer can create an alert for their Databricks SQL query, and select the webhook alert destination as the notification destination. The alert can be configured with a custom condition, such as when the number of stores with $0 in sales is greater than zero, and a custom message template, such as "Alert: {number_of_stores} stores have $0 in sales". The alert can also be configured with a recurrence interval, such as every hour, to check the query result periodically. When the alert condition is met, the data engineer and their team will receive a notification via the messaging webhook, with the custom message and a link to the Databricks SQL query. The other options are either not suitable for sending notifications via a messaging webhook (A, B, E), or not suitable for sending recurring notifications . Reference: Databricks Documentation - Manage notification destinations, Databricks Documentation - Create alerts for Databricks SQL queries, Databricks Documentation - Configure alert conditions and messages.

NEW QUESTION # 201
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?

A. DELTA
B. org.apache.spark.sql.jdbc
C. sqlite
D. autoloader
E. org.apache.spark.sql.sqlite

Answer: B

Explanation:
CREATE TABLE new_employees_table
USING JDBC
OPTIONS (
url "<jdbc_url>",
dbtable "<table_name>",
user '<username>',
password '<password>'
) AS
SELECT * FROM employees_table_vw
https://docs.databricks.com/external-data/jdbc.html#language-sql

NEW QUESTION # 202
A data engineering project involves processing large batches of data on a daily schedule using ETL. The jobs are resource-intensive and vary in size, requiring a scalable, cost-efficient compute solution that can automatically scale based on the workload.
Which compute approach will satisfy the needs described?

A. All-Purpose Cluster
B. Job Cluster
C. Dedicated Cluster
D. Databricks SQL Serverless

Answer: B

NEW QUESTION # 203
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.

The code block used by the data engineer is below:
Which line of code should the data engineer use to fill in the blank if the data engineer only wants the query to execute a micro-batch to process data every 5 seconds?

A. trigger(once= " 5 seconds " )
B. trigger(continuous= " 5 seconds " )
C. trigger( " 5 seconds " )
D. trigger(processingTime= " 5 seconds " )

Answer: D

NEW QUESTION # 204
A data engineer is inspecting an ETL pipeline based on a Pyspark job that consistently encounters performance bottlenecks. Based on developer feedback, the data engineer assumes the job is low on compute resources. To pinpoint the issue, the data engineer observes the Spark Ul and finds out the job has a high CPU time vs Task time.
Which course of action should the data engineer take?

A. High CPU time vs Task time means an under-utilized cluster. The data engineer may need to repartition data to spread the jobs more evenly throughout the cluster.
B. High CPU time vs Task time means over-utilized memory and the need to increase parallelism
C. High CPU time vs Task time means a CPU over-utilized job. The data engineer may need to consider executor and core tuning or resizing the cluster
D. High CPU time vs Task time means efficient use of cluster and no change needed

Answer: C

NEW QUESTION # 205
......

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