Databricks-Certified-Professional-Data-Engineer Reliable Braindumps, New Databricks-Certified-Professional-Data-Engineer Test Sims

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The modern Databricks world is changing its dynamics at a fast pace. To stay and compete in this challenging market, you have to learn and enhance your in-demand skills. Fortunately, with the Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer) certification exam you can do this job nicely and quickly. To do this you just need to enroll in the Databricks-Certified-Professional-Data-Engineer certification exam and put all your efforts to pass the Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer) certification exam. After successful competition of the Databricks Databricks-Certified-Professional-Data-Engineer certification, the certified candidates can put their career on the right track and achieve their professional career objectives in a short time period.

Databricks Certified Professional Data Engineer certification exam is intended for data engineers, data architects, and other IT professionals who work with big data technologies. Databricks-Certified-Professional-Data-Engineer Exam covers a wide range of topics, including data ingestion, data transformation, data storage, and data analysis. It also covers the use of Databricks tools and technologies such as Databricks Delta, Databricks Runtime, and Apache Spark.

Databricks Certified Professional Data Engineer Certification Exam is a rigorous and comprehensive certification exam that validates a professional's competence in Databricks data engineering principles and techniques. With the exponential growth of data management and processing, data professionals need certification to show that they are experts in handling data. With Databricks Certified Professional Data Engineer certification, data engineers can confidently build, implement and maintain effective and scalable data engineering solutions.

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Databricks Certified Professional Data Engineer certification exam is suitable for data engineers, data architects, and data scientists who are responsible for building and managing data pipelines and workflows. Databricks-Certified-Professional-Data-Engineer Exam is designed to test the knowledge and skills required to design, implement, and manage data engineering workflows using Databricks. Candidates must have a solid understanding of data engineering concepts such as data modeling, data integration, data transformation, and data storage.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
B. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
C. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
D. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
E. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.

Answer: E

Explanation:
This is the correct answer because it describes what will occur when this code is executed. The code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format("delta").mode("overwrite") to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Write to Delta tables" section.

NEW QUESTION # 63
A developer has successfully configured credential for Databricks Repos and cloned a remote Git repository.
Hey don not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.
Use Response to pull changes from the remote Git repository commit and push changes to a branch that appeared as a changes were pulled.

A. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
B. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
C. Use Repos to merge all differences and make a pull request back to the remote repository.
D. Use repos to merge all difference and make a pull request back to the remote repository.

Answer: B

Explanation:
In Databricks Repos, when a user does not have privileges to make changes directly to the main branch of a cloned remote Git repository, the recommended approach is to create a new branch within the Databricks workspace. The developer can then make changes in this new branch, commit those changes, and push the new branch to the remote Git repository. This workflow allows for isolated development without affecting the main branch, enabling the developer to propose changes via a pull request from the new branch to the main branch in the remote repository. This method adheres to common Git collaboration workflows, fostering code review and collaboration while ensuring the integrity of the main branch.
References:
* Databricks documentation on using Repos with Git: https://docs.databricks.com/repos.html

NEW QUESTION # 64
The data analyst team had put together queries that identify items that are out of stock based on orders and replenishment but when they run all together for final output the team noticed it takes a really long time, you were asked to look at the reason why queries are running slow and identify steps to improve the performance and when you looked at it you noticed all the code queries are running sequentially and using a SQL endpoint cluster. Which of the following steps can be taken to resolve the issue?
Here is the example query
1.--- Get order summary
2.create or replace table orders_summary
3.as
4.select product_id, sum(order_count) order_count
5.from
6. (
7. select product_id,order_count from orders_instore
8. union all
9. select product_id,order_count from orders_online
10. )
11.group by product_id
12.-- get supply summary
13.create or repalce tabe supply_summary
14.as
15.select product_id, sum(supply_count) supply_count
16.from supply
17.group by product_id
18.
19.-- get on hand based on orders summary and supply summary
20.
21.with stock_cte
22.as (
23.select nvl(s.product_id,o.product_id) as product_id,
24. nvl(supply_count,0) - nvl(order_count,0) as on_hand
25.from supply_summary s
26.full outer join orders_summary o
27. on s.product_id = o.product_id
28.)
29.select *
30.from
31.stock_cte
32.where on_hand = 0

A. Increase the cluster size of the SQL endpoint.
B. Increase the maximum bound of the SQL endpoint's scaling range.
C. Turn on the Auto Stop feature for the SQL endpoint.
D. Turn on the Serverless feature for the SQL endpoint and change the Spot Instance Pol-icy to "Reliability Optimized."
E. Turn on the Serverless feature for the SQL endpoint.

Answer: A

Explanation:
Explanation
The answer is to increase the cluster size of the SQL Endpoint, here queries are running sequentially and since the single query can not span more than one cluster adding more clusters won't improve the query but rather increasing the cluster size will improve performance so it can use additional compute in a warehouse.
In the exam please note that additional context will not be given instead you have to look for cue words or need to understand if the queries are running sequentially or concurrently. if the que-ries are running sequentially then scale up(more nodes) if the queries are running concurrently (more users) then scale out(more clusters).
Below is the snippet from Azure, as you can see by increasing the cluster size you are able to add more worker nodes.

SQL endpoint scales horizontally(scale-out) and vertically (scale-up), you have to understand when to use what.
Scale-up-> Increase the size of the cluster from x-small to small, to medium, X Large....
If you are trying to improve the performance of a single query having additional memory, additional nodes and cpu in the cluster will improve the performance.
Scale-out -> Add more clusters, 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.
SQL endpoint
A picture containing diagram Description automatically generated

NEW QUESTION # 65
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

Which solution would improve the performance?

Answer: C

Explanation:
When joining a stream of advertisement impressions with a stream of user clicks, you want to minimize the state that you need to maintain for the join. Option A suggests using a left outer join with the condition that clickTime == impressionTime, which is suitable for correlating events that occur at the exact same time.
However, in a real-world scenario, you would likely need some leeway to account for the delay between an impression and a possible click. It's important to design the join condition and the window of time considered to optimize performance while still capturing the relevant user interactions. In this case, having the watermark can help with state management and avoid state growing unbounded by discarding old state data that's unlikely to match with new data.

NEW QUESTION # 66
A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production.
How can the data engineer run unit tests against function that work with data in production?

A. Define units test and functions within the same notebook
B. Run unit tests against non-production data that closely mirrors production
C. Define and unit test functions using Files in Repos
D. Define and import unit test functions from a separate Databricks notebook

Answer: B

Explanation:
The best practice for running unit tests on functions that interact with data is to use a dataset that closely mirrors the production data. This approach allows data engineers to validate the logic of their functions without the risk of affecting the actual production data. It's important to have a representative sample of production data to catch edge cases and ensure the functions will work correctly when used in a production environment.
Reference:
Databricks Documentation on Testing: Testing and Validation of Data and Notebooks

NEW QUESTION # 67
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Databricks-Certified-Professional-Data-Engineer Reliable Braindumps, New Databricks-Certified-Professional-Data-Engineer Test Sims