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Databricks-Certified-Professional-Data-Engineer exam is a comprehensive assessment that evaluates a candidate's ability to design, implement, and manage data pipelines, as well as leverage advanced analytics and machine learning techniques on the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and requires candidates to complete a hands-on project that demonstrates their ability to build a data solution on the Databricks platform.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series ofVACUUMcommands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

Answer: D

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum


NEW QUESTION # 105
A junior data engineer on your team has implemented the following code block.

The viewnew_eventscontains a batch of records with the same schema as theeventsDelta table.
Theevent_idfield serves as a unique key for this table.
When this query is executed, what will happen with new records that have the sameevent_idas an existing record?

Answer: D

Explanation:
Explanation
This is the correct answer because it describes what will happen with new records that have the same event_id as an existing record when the query is executed. The query uses the INSERT INTO command to append new records from the view new_events to the table events. However, the INSERT INTO command does not check for duplicate values in the primary key column (event_id) and does not perform any update or delete operations on existing records. Therefore, if there are new records that have the same event_id as an existing record, they will be ignored and not inserted into the table events. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Append data using INSERT INTO" section.


NEW QUESTION # 106
Which statement regarding spark configuration on the Databricks platform is true?

Answer: B

Explanation:
When Spark configuration properties are set for an interactive cluster using the Clusters UI in Databricks, those configurations are applied at the cluster level. This means that all notebooks attached to that cluster will inherit and be affected by these configurations. This approach ensures consistency across all executions within that cluster, as the Spark configuration properties dictate aspects such as memory allocation, number of executors, and other vital execution parameters. This centralized configuration management helps maintain standardized execution environments across different notebooks, aiding in debugging and performance optimization.
References:
* Databricks documentation on configuring clusters: https://docs.databricks.com/clusters/configure.html


NEW QUESTION # 107
A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

Answer: C

Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Metastore" section; Databricks Documentation, under "Managed and external tables" section.


NEW QUESTION # 108
You had worked with the Data analysts team to set up a SQL Endpoint(SQL warehouse) point so they can easily query and analyze data in the gold layer, but once they started consuming the SQL Endpoint(SQL warehouse) you noticed that during the peak hours as the number of users increase you are seeing queries taking longer to finish, which of the following steps can be taken to resolve the issue?
*Please note Databricks recently renamed SQL endpoint to SQL warehouse.

Answer: B

Explanation:
Explanation
the answer is,
They can increase the maximum bound of the SQL endpoint's scaling range, when you increase the maximum bound you can add more clusters to the warehouse which can then run additional queries that are waiting in the queue to run, focus on the below explanation that talks about Scale-out.
The question is looking to test your ability to know how to scale a SQL Endpoint(SQL Warehouse) and you have to look for cue words or need to understand if the queries are running sequentially or concurrently. if the queries are running sequentially then scale up(Size of the cluster from 2X-Small to 4X-Large) if the queries are running concurrently or with more users then scale out(add more clusters).
SQL Endpoint(SQL Warehouse) Overview: (Please read all of the below points and the below diagram to understand )
1.A SQL Warehouse should have at least one cluster
2.A cluster comprises one driver node and one or many worker nodes
3.No of worker nodes in a cluster is determined by the size of the cluster (2X -Small ->1 worker, X-Small ->2 workers.... up to 4X-Large -> 128 workers) this is called Scale up
4.A single cluster irrespective of cluster size(2X-Smal.. to ...4XLarge) can only run 10 queries at any given time if a user submits 20 queries all at once to a warehouse with 3X-Large cluster size and cluster scaling (min
1, max1) while 10 queries will start running the remaining 10 queries wait in a queue for these 10 to finish.
5.Increasing the Warehouse cluster size can improve the performance of a query, example if a query runs for 1 minute in a 2X-Small warehouse size, it may run in 30 Seconds if we change the warehouse size to X-Small.
this is due to 2X-Small has 1 worker node and X-Small has 2 worker nodes so the query has more tasks and runs faster (note: this is an ideal case example, the scalability of a query performance depends on many factors, it can not always be linear)
6.A warehouse can have more than one cluster this is called Scale out. If a warehouse is con-figured with X-Small cluster size with cluster scaling(Min1, Max 2) Databricks spins up an additional cluster if it detects queries are waiting in the queue, If a warehouse is configured to run 2 clusters(Min1, Max 2), and let's say a user submits 20 queries, 10 queriers will start running and holds the remaining in the queue and databricks will automatically start the second cluster and starts redirecting the 10 queries waiting in the queue to the second cluster.
7.A single query will not span more than one cluster, once a query is submitted to a cluster it will remain in that cluster until the query execution finishes irrespective of how many clusters are available to scale.
Please review the below diagram to understand the above concepts:

SQL endpoint(SQL Warehouse) scales horizontally(scale-out) and vertical (scale-up), you have to understand when to use what.
Scale-out -> to add more clusters for a SQL endpoint, change max number of clusters If you are trying to improve the throughput, being able to run as many queries as possible then having an additional cluster(s) will improve the performance.
Databricks SQL automatically scales as soon as it detects queries are in queuing state, in this example scaling is set for min 1 and max 3 which means the warehouse can add three clusters if it detects queries are waiting.

During the warehouse creation or after you have the ability to change the warehouse size (2X-Small....to
...4XLarge) to improve query performance and the maximize scaling range to add more clusters on a SQL Endpoint(SQL Warehouse) scale-out, if you are changing an existing warehouse you may have to restart the warehouse to make the changes effective.

How do you know how many clusters you need(How to set Max cluster size)?
When you click on an existing warehouse and select the monitoring tab, you can see warehouse utilization information(see below), there are two graphs that provide important information on how the warehouse is being utilized, if you see queries are being queued that means your warehouse can benefit from additional clusters. Please review the additional DBU cost associated with adding clusters so you can take a well balanced decision between cost and performance.


NEW QUESTION # 109
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