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

Section	Weight	Objectives
Databricks Lakehouse Platform	24%	- Unity Catalog - Delta Lake - Data Management - Lakehouse Architecture
Data Processing	28%	- ETL Pipelines - Structured Streaming - Data Transformation - Spark SQL
Data Modeling and Storage	20%	- File Formats - Data Modeling - Storage Optimization
Data Quality and Governance	12%	- Data Quality - Governance - Data Lineage
Monitoring and Troubleshooting	16%	- Troubleshooting - Monitoring - Performance Optimization

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q180-Q185):

NEW QUESTION # 180
A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers, one driver of type i3.xlarge, and should use the '14.3.x- scala2.12' runtime. Which command should the data engineer use?

A. databricks clusters add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
B. databricks compute add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
C. databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name Data Engineer_cluster
D. databricks clusters create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name DataEngineer_cluster

Answer: D

Explanation:
The correct Databricks CLI command to create a new cluster is databricks clusters create. You specify the runtime with --spark-version (here '14.3.x-scala2.12'), the number of workers with -- num-workers, the node type with --node-type-id, and the cluster name with --cluster-name. This command properly initializes the cluster with the desired configuration.

NEW QUESTION # 181
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When a database is being created, make sure that the LOCATION keyword is used.
B. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
C. When the workspace is being configured, make sure that external cloud object storage has been mounted.
D. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
E. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.

Answer: E

Explanation:
To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored.
For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION `/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the
`/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.

NEW QUESTION # 182
A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?

A. Move the table definition into a separate function, and make calls to this function using different input parameters inside the for loop.
B. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
C. Wrap the for loop inside another table definition, using generalized names and properties to replace with those from the inner table definition.
D. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.

Answer: A

Explanation:
In the provided refactored code, the for loop dynamically attempts to define multiple tables, but the use of a loop within the DLT (@dlt.table) decorator does not work properly because it results in a single function reference being overwritten for each iteration. This leads to an incorrect DAG because all the table definitions end up pointing to the last iteration of the loop.

NEW QUESTION # 183
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:

The table daily_store_sales contains all the information needed to update store_sales_summary.
The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

A. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
B. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
C. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.
D. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
E. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.

Answer: D

NEW QUESTION # 184
What is the first line of a Databricks Python notebook when viewed in a text editor?

A. # MAGIC %python
B. -- Databricks notebook source
C. // Databricks notebook source
D. %python
E. # Databricks notebook source

Answer: E

Explanation:
https://docs.databricks.com/en/notebooks/notebook-export-import.html#import-a-file-and-convert- it-to-a-notebook

NEW QUESTION # 185
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