Question # 1
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. Parse the Delta Lake transaction log to identify all newly written data files.
| B. 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.
| C. 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.
| D. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
|
C. 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.
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.
References:
• Delta Lake Documentation on Time Travel: Delta Time Travel
• Delta Lake Versioning: Delta Lake Versioning Guide
Question # 2
| Which is a key benefit of an end-to-end test?
| | A. It closely simulates real world usage of your application. | | B. It pinpoint errors in the building blocks of your application. | | C. It provides testing coverage for all code paths and branches | | D. It makes it easier to automate your test suite |
A. It closely simulates real world usage of your application.
Explanation:
End-to-end testing is a methodology used to test whether the flow of an
application, from start to finish, behaves as expected. The key benefit of an end-to-end test
is that it closely simulates real-world, user behavior, ensuring that the system as a whole
operates correctly.
References:
Software Testing: End-to-End Testing
Question # 3
| 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 data is saved to a table, make sure that a full file path is specified alongside the
Delta format. | | D. When tables are created, make sure that the EXTERNAL keyword is used in the
CREATE TABLE statement. | | E. When the workspace is being configured, make sure that external cloud object storage
has been mounted. |
D. When tables are created, make sure that the EXTERNAL keyword is used in the
CREATE TABLE statement.
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.
References:
https://docs.databricks.com/delta/delta-batch.html#create-a-table
https://docs.databricks.com/delta/delta-batch.html#drop-a-table
Question # 4
| A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate
records.
In addition to de-duplicating records within the batch, which of the following approaches
allows the data engineer to deduplicate data against previously processed records as it is
inserted into a Delta table?
| | A. Set the configuration delta.deduplicate = true. | | B. VACUUM the Delta table after each batch completes. | | C. Perform an insert-only merge with a matching condition on a unique key | | D. Perform a full outer join on a unique key and overwrite existing data. | | E. Rely on Delta Lake schema enforcement to prevent duplicate records. |
C. Perform an insert-only merge with a matching condition on a unique key
Explanation:
To deduplicate data against previously processed records as it is inserted
into a Delta table, you can use the merge operation with an insert-only clause. This allows
you to insert new records that do not match any existing records based on a unique key,
while ignoring duplicate records that match existing records. For example, you can use the
following syntax:
MERGE INTO target_table USING source_table ON target_table.unique_key =
source_table.unique_key WHEN NOT MATCHED THEN INSERT *
This will insert only the records from the source table that have a unique key that is not
present in the target table, and skip the records that have a matching key. This way, you
can avoid inserting duplicate records into the Delta table.
References:
https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-usingmerge
https://docs.databricks.com/delta/delta-update.html#insert-only-merge
Question # 5
| Which configuration parameter directly affects the size of a spark-partition upon ingestion
of data into Spark? | | A. spark.sql.files.maxPartitionBytes | | B. spark.sql.autoBroadcastJoinThreshold | | C. spark.sql.files.openCostInBytes | | D. spark.sql.adaptive.coalescePartitions.minPartitionNum | | E. spark.sql.adaptive.advisoryPartitionSizeInBytes |
A. spark.sql.files.maxPartitionBytes
Explanation:
This is the correct answer because spark.sql.files.maxPartitionBytes is a
configuration parameter that directly affects the size of a spark-partition upon ingestion of
data into Spark. This parameter configures the maximum number of bytes to pack into a
single partition when reading files from file-based sources such as Parquet, JSON and
ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in
size, unless there are too many small files or only one large file. Verified References:
[Databricks Certified Data Engineer Professional], under “Spark Configuration”
Question # 6
| Which of the following is true of Delta Lake and the Lakehouse? | | A. Because Parquet compresses data row by row. strings will only be compressed when a
character is repeated multiple times. | | B. Delta Lake automatically collects statistics on the first 32 columns of each table which
are leveraged in data skipping based on query filters. | | C. Views in the Lakehouse maintain a valid cache of the most recent versions of source
tables at all times. | | D. Primary and foreign key constraints can be leveraged to ensure duplicate values are
never entered into a dimension table. | | E. Z-order can only be applied to numeric values stored in Delta Lake tables |
B. Delta Lake automatically collects statistics on the first 32 columns of each table which
are leveraged in data skipping based on query filters.
Explanation:
https://docs.delta.io/2.0.0/table-properties.html
Delta Lake automatically collects statistics on the first 32 columns of each table, which are
leveraged in data skipping based on query filters1. Data skipping is a performance
optimization technique that aims to avoid reading irrelevant data from the storage
layer1. By collecting statistics such as min/max values, null counts, and bloom filters, Delta
Lake can efficiently prune unnecessary files or partitions from the query plan1. This can
significantly improve the query performance and reduce the I/O cost.
The other options are false because:
Parquet compresses data column by column, not row by row2. This allows for
better compression ratios, especially for repeated or similar values within a
column2.
Views in the Lakehouse do not maintain a valid cache of the most recent versions
of source tables at all times3. Views are logical constructs that are defined by a
SQL query on one or more base tables3. Views are not materialized by default,
which means they do not store any data, but only the query definition3. Therefore,
views always reflect the latest state of the source tables when queried3. However,
views can be cached manually using the CACHE TABLE or CREATE TABLE AS
SELECT commands.
Primary and foreign key constraints can not be leveraged to ensure duplicate
values are never entered into a dimension table. Delta Lake does not support
enforcing primary and foreign key constraints on tables. Constraints are logical
rules that define the integrity and validity of the data in a table. Delta Lake relies on
the application logic or the user to ensure the data quality and consistency.
Z-order can be applied to any values stored in Delta Lake tables, not only numeric
values. Z-order is a technique to optimize the layout of the data files by sorting
them on one or more columns. Z-order can improve the query performance by
clustering related values together and enabling more efficient data skipping. Zorder can be applied to any column that has a defined ordering, such as numeric,
string, date, or boolean values.
References: Data Skipping, Parquet Format, Views, [Caching], [Constraints], [Z-Ordering]
Question # 7
| An upstream system has been configured to pass the date for a given batch of data to the
Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter
to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above
code block? | | A. date = spark.conf.get("date") | | B. input_dict = input()
date= input_dict["date"] | | C. import sys
date = sys.argv[1] | | D. date = dbutils.notebooks.getParam("date") | | E. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date") |
E. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
Explanation:
The code block that should be used to create the date Python variable used
in the above code block is:
dbutils.widgets.text(“date”, “null”) date = dbutils.widgets.get(“date”)
This code block uses the dbutils.widgets API to create and get a text widget named “date”
that can accept a string value as a parameter1. The default value of the widget is “null”,
which means that if no parameter is passed, the date variable will be “null”. However, if a
parameter is passed through the Databricks Jobs API, the date variable will be assigned
the value of the parameter. For example, if the parameter is “2021-11-01”, the date variable
will be “2021-11-01”. This way, the notebook can use the date variable to load data from
the specified path.
The other options are not correct, because:
Option A is incorrect because spark.conf.get(“date”) is not a valid way to get a
parameter passed through the Databricks Jobs API. The spark.conf API is used to
get or set Spark configuration properties, not notebook parameters2.
Option B is incorrect because input() is not a valid way to get a parameter passed
through the Databricks Jobs API. The input() function is used to get user input
from the standard input stream, not from the API request3.
Option C is incorrect because sys.argv1 is not a valid way to get a parameter
passed through the Databricks Jobs API. The sys.argv list is used to get the
command-line arguments passed to a Python script, not to a notebook4.
Option D is incorrect because dbutils.notebooks.getParam(“date”) is not a valid
way to get a parameter passed through the Databricks Jobs API. The
dbutils.notebooks API is used to get or set notebook parameters when running a
notebook as a job or as a subnotebook, not when passing parameters through the
API5.
References: Widgets, Spark Configuration, input(), sys.argv, Notebooks
Databricks Databricks-Certified-Professional-Data-Engineer Exam Dumps
5 out of 5
Pass Your Databricks Certified Data Engineer Professional Exam in First Attempt With Databricks-Certified-Professional-Data-Engineer Exam Dumps. Real Databricks Certification Exam Questions As in Actual Exam!
— 120 Questions With Valid Answers
— Updation Date : 15-Apr-2025
— Free Databricks-Certified-Professional-Data-Engineer Updates for 90 Days
— 98% Databricks Certified Data Engineer Professional Exam Passing Rate
PDF Only Price 49.99$
19.99$
Buy PDF
Speciality
Additional Information
Testimonials
Related Exams
- Number 1 Databricks Databricks Certification study material online
- Regular Databricks-Certified-Professional-Data-Engineer dumps updates for free.
- Databricks Certified Data Engineer Professional Practice exam questions with their answers and explaination.
- Our commitment to your success continues through your exam with 24/7 support.
- Free Databricks-Certified-Professional-Data-Engineer exam dumps updates for 90 days
- 97% more cost effective than traditional training
- Databricks Certified Data Engineer Professional Practice test to boost your knowledge
- 100% correct Databricks Certification questions answers compiled by senior IT professionals
Databricks Databricks-Certified-Professional-Data-Engineer Braindumps
Realbraindumps.com is providing Databricks Certification Databricks-Certified-Professional-Data-Engineer braindumps which are accurate and of high-quality verified by the team of experts. The Databricks Databricks-Certified-Professional-Data-Engineer dumps are comprised of Databricks Certified Data Engineer Professional questions answers available in printable PDF files and online practice test formats. Our best recommended and an economical package is Databricks Certification PDF file + test engine discount package along with 3 months free updates of Databricks-Certified-Professional-Data-Engineer exam questions. We have compiled Databricks Certification exam dumps question answers pdf file for you so that you can easily prepare for your exam. Our Databricks braindumps will help you in exam. Obtaining valuable professional Databricks Databricks Certification certifications with Databricks-Certified-Professional-Data-Engineer exam questions answers will always be beneficial to IT professionals by enhancing their knowledge and boosting their career.
Yes, really its not as tougher as before. Websites like Realbraindumps.com are playing a significant role to make this possible in this competitive world to pass exams with help of Databricks Certification Databricks-Certified-Professional-Data-Engineer dumps questions. We are here to encourage your ambition and helping you in all possible ways. Our excellent and incomparable Databricks Databricks Certified Data Engineer Professional exam questions answers study material will help you to get through your certification Databricks-Certified-Professional-Data-Engineer exam braindumps in the first attempt.
Pass Exam With Databricks Databricks Certification Dumps. We at Realbraindumps are committed to provide you Databricks Certified Data Engineer Professional braindumps questions answers online. We recommend you to prepare from our study material and boost your knowledge. You can also get discount on our Databricks Databricks-Certified-Professional-Data-Engineer dumps. Just talk with our support representatives and ask for special discount on Databricks Certification exam braindumps. We have latest Databricks-Certified-Professional-Data-Engineer exam dumps having all Databricks Databricks Certified Data Engineer Professional dumps questions written to the highest standards of technical accuracy and can be instantly downloaded and accessed by the candidates when once purchased. Practicing Online Databricks Certification Databricks-Certified-Professional-Data-Engineer braindumps will help you to get wholly prepared and familiar with the real exam condition. Free Databricks Certification exam braindumps demos are available for your satisfaction before purchase order. The data engineering landscape is rapidly evolving, and
Databricks, a unified platform for data engineering and machine learning, is at
the forefront. Earning the Databricks-Certified-Professional-Data-Engineer
validates your expertise in using Databricks to tackle complex data engineering
challenges. This article equips you with everything you need to know about the
exam, including its details, career prospects, and valuable resources for your
preparation journey.
Exam Overview:
The Databricks-Certified-Professional-Data-Engineer exam
assesses your ability to leverage Databricks for advanced data engineering tasks. It delves into
your understanding of the platform itself, along with its developer tools like
Apache Spark, Delta Lake, MLflow, and the Databricks CLI and REST API. Heres a
breakdown of the key areas covered in the exam:
- Databricks
Tooling (20%) – This section evaluates your proficiency in using Databricks notebooks,
clusters, jobs, libraries, and other core functionalities.
- Data
Processing (30%) – Your expertise in building and optimizing data
pipelines using Spark SQL and Python (both batch and incremental
processing) will be tested.
- Data
Modeling (20%) – This section assesses your ability to design and
implement data models for a lakehouse architecture, leveraging your
knowledge of data modeling concepts.
- Security
and Governance (10%) – The exam probes your understanding of securing
and governing data pipelines within the Databricks environment.
- Monitoring
and Logging (10%) – Your skills in monitoring and logging data
pipelines for performance and troubleshooting will be evaluated.
- Testing
and Deployment (10%) – This section focuses on your ability to
effectively test and deploy data pipelines within production environments.
Why Get Certified?
The Databricks-Certified-Professional-Data-Engineer
certification validates your proficiency in a highly sought-after skillset.
Here are some compelling reasons to pursue this certification:
- Career
Advancement: The certification
demonstrates your expertise to employers, potentially opening doors to
better job opportunities and promotions.
- Salary
Boost: Databricks-certified
professionals often
command higher salaries compared to their non-certified counterparts.
- Industry
Recognition: Earning this
certification positions you as a valuable asset in the data engineering
field.
Preparation
Resources:
Realbraindumps.com recognizes the
importance of providing accurate and up-to-date exam preparation materials. We
prioritize quality by:
- Curating content from industry experts: Our team comprises
certified data engineers with extensive experience in the field.
- Regularly updating study materials: We constantly revise our
content to reflect the latest exam format and topics.
- Providing practice tests: Real-world Databricks-Certified-Professional-Data-Engineer
practice tests help you assess your knowledge retention and identify
areas for improvement.
Conclusion: The
Databricks-Certified-Professional-Data-Engineer exam is a challenging but
rewarding pursuit. By focusing on quality study materials, practicing with RealBraindumps,
and honing your skills, you can confidently approach the exam and achieve
success. Remember, a strong foundation in Databricks concepts and best
practices is far more valuable than relying on fake questionable dumps.
Send us mail if you want to check Databricks Databricks-Certified-Professional-Data-Engineer Databricks Certified Data Engineer Professional DEMO before your purchase and our support team will send you in email.
If you don't find your dumps here then you can request what you need and we shall provide it to you.
Bulk Packages
$50
- Get 3 Exams PDF
- Get $33 Discount
- Mention Exam Codes in Payment Description.
Buy 3 Exams PDF
$70
- Get 5 Exams PDF
- Get $65 Discount
- Mention Exam Codes in Payment Description.
Buy 5 Exams PDF
$100
- Get 5 Exams PDF + Test Engine
- Get $105 Discount
- Mention Exam Codes in Payment Description.
Buy 5 Exams PDF + Engine
 Jessica Doe
Databricks Certification
We are providing Databricks Databricks-Certified-Professional-Data-Engineer Braindumps with practice exam question answers. These will help you to prepare your Databricks Certified Data Engineer Professional exam. Buy Databricks Certification Databricks-Certified-Professional-Data-Engineer dumps and boost your knowledge.
FAQs of Databricks-Certified-Professional-Data-Engineer Exam
What
is the Databricks Certified Professional Data Engineer exam about?
This
exam assesses your ability to use Databricks to perform advanced data engineering tasks,
such as building pipelines, data modelling, and working with tools like Apache
Spark and Delta Lake.
Who
should take this exam?
Ideal
candidates are data engineers with at least one year of experience in relevant
areas and a strong understanding of the Databricks platform.
Is
there any required training before taking the exam?
There
are no prerequisites, but Databricks recommends relevant training to ensure
success.
What
is covered in the Databricks Certified Professional Data Engineer exam?
The
exam covers data ingestion, processing, analytics, and visualization using Databricks,
focusing on practical skills in building and maintaining data pipelines.
Does
the exam cover specific versions of Apache Spark or Delta Lake?
The
exam focuses on core functionalities, but for optimal performance, it is
recommended that you be familiar with the latest versions. For the latest
features, refer to Databricks documentation: https://docs.databricks.com/en/release-notes/product/index.html.
How
much weight does the exam give to coding questions vs. theoretical knowledge?
The
exam primarily focuses on applying your knowledge through scenario-based
multiple-choice questions.
Does
the exam focus on using notebooks or libraries like Koalas or MLflow?
While
the focus is not limited to notebooks, you should be familiar with creating and
using notebooks for data engineering tasks on Databricks. Knowledge of
libraries like Koalas and MLflow can be beneficial. For notebooks and
libraries, refer to Databricks documentation: https://docs.databricks.com/en/notebooks/index.html.
Do
RealBraindumps practice questions match the exam format?
Yes, RealBraindumps aims
to mirror the format of the actual Databricks Certified Professional Data
Engineer exam to provide a realistic practice environment for candidates.
Does
RealBraindumps guarantee success in the Databricks Certified Professional Data
Engineer exam?
While
RealBraindumps may offer assurances, success ultimately depends on individual
preparation and understanding of the exam topics and concepts.
Are
there testimonials for RealBraindumps Databricks Certified Professional Data
Engineer preparation material?
RealBraindumps
often showcases testimonials or reviews from individuals who have utilized
their study materials to prepare for the Databricks
Certified Professional Data Engineer exam, providing insights into their
effectiveness.
|
