Databricks Databricks-Certified-Professional-Data-Engineer Exam Bootcamp | Exam Databricks-Certified-Professional-Data-Engineer Sample

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

Section	Weight	Objectives
Topic 1: Delta Lake	20-25%	- Delta Lake fundamentals 1. ACID transactions 2. Time travel and data versioning 3. Optimize and Z-order - Delta Lake operations 1. Merge, update, delete operations 2. Delta Live Tables 3. Schema evolution and enforcement
Topic 2: Data Ingestion	15-20%	- Batch ingestion methods 1. Spark APIs for ingestion 2. Integration with external systems 3. DBR autoloader - Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals
Topic 3: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Differences between data lake, data warehouse, and lakehouse 2. Data governance fundamentals 3. Bronze, silver, gold data layers
Topic 4: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Monitoring and alerting 2. Jobs and job scheduling 3. Task dependencies and orchestration
Topic 5: Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. DataFrame operations and transformations 2. Window functions 3. Spark SQL queries and functions - Python and SQL for data engineering 1. Built-in and user-defined functions 2. Performance optimization techniques 3. Spark APIs in Python

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q45-Q50):

NEW QUESTION # 45
How are Delt tables stored?

A. A Directory where parquet data files are stored, all of the metadata is stored in parquet files
B. A Directory where parquet data files are stored in Data plane, a sub directory _delta_log where meta data, history and log is stored in control pane.
C. A Directory where parquet data files are stored, a sub directory _delta_log where meta data, and the transaction log is stored as JSON files.
D. A Directory where parquet data files are stored, all of the meta data is stored in memory
E. Data is stored in Data plane and Metadata and delta log are stored in control pane

Answer: C

Explanation:
Explanation
The answer is A Directory where parquet data files are stored, a sub directory _delta_log where meta data, and the transaction log is stored as JSON files.
Timeline Description automatically generated

NEW QUESTION # 46
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

A. libraries
B. fs
C. configure
D. jobs
E. workspace

Answer: A

Explanation:
The libraries command group allows you to install, uninstall, and list libraries on Databricks clusters. You can use the libraries install command to install a custom Python Wheel on a cluster by specifying the --whl option and the path to the wheel file. For example, you can use the following command to install a custom Python Wheel named mylib-0.1-py3-none-any.whl on a cluster with the id 1234-567890-abcde123:
databricks libraries install --cluster-id 1234-567890-abcde123 --whl dbfs:/mnt/mylib/mylib-0.1-py3-none-any.
whl
This will upload the custom Python Wheel to the cluster and make it available for use with a production job.
You can also use the libraries uninstall command to uninstall a library from a cluster, and the libraries list command to list the libraries installed on a cluster.
References:
* Libraries CLI (legacy): https://docs.databricks.com/en/archive/dev-tools/cli/libraries-cli.html
* Library operations: https://docs.databricks.com/en/dev-tools/cli/commands.html#library-operations
* Install or update the Databricks CLI: https://docs.databricks.com/en/dev-tools/cli/install.html

NEW QUESTION # 47
An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

A. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
B. An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
C. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
D. A managed table will be created in the DBFS root storage container.
E. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.

Answer: B

Explanation:
https://docs.databricks.com/en/lakehouse/data-objects.html

NEW QUESTION # 48
A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources.
Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table.
Given the current implementation, which method can be used?

A. Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.
B. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
C. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
D. Parse the Delta Lake transaction log to identify all newly written data files.

Answer: A

Explanation:
Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table.
:
Delta Lake Documentation on Time Travel: Delta Time Travel
Delta Lake Versioning: Delta Lake Versioning Guide

NEW QUESTION # 49
While investigating a data issue, you wanted to review yesterday's version of the table using below command, while querying the previous version of the table using time travel you realized that you are no longer able to view the historical data in the table and you could see it the table was updated yesterday based on the table history(DESCRIBE HISTORY table_name) command what could be the reason why you can not access this data?
SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)

A. By default, historical data is cleaned every 180 days in DELTA
B. You currently do not have access to view historical data
C. A command VACUUM table_name RETAIN 0 was ran on the table
D. Time travel is disabled
E. Time travel must be enabled before you query previous data

Answer: C

Explanation:
Explanation
The answer is, VACUUM table_name RETAIN 0 was ran
The VACUUM command recursively vacuums directories associated with the Delta table and re-moves data files that are no longer in the latest state of the transaction log for the table and are older than a retention threshold. The default is 7 Days.
When VACUUM table_name RETAIN 0 is ran all of the historical versions of data are lost time travel can only provide the current state.

NEW QUESTION # 50
......

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