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Databricks is a leading cloud-based data platform that enables organizations to accelerate innovation and achieve their data-driven goals. To showcase their expertise in using the Databricks platform, data professionals can earn the Databricks-Certified-Professional-Data-Engineer (Databricks Certified Professional Data Engineer) certification. Databricks Certified Professional Data Engineer Exam certification is designed to validate the skills and knowledge required to design, build, and maintain data solutions on the Databricks platform.

Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) exam is designed for professionals who want to showcase their skills in building data pipelines and managing data workflows on Databricks. Databricks is a cloud-based data platform that provides powerful tools for data engineering, data science, and machine learning. Databricks Certified Professional Data Engineer Exam certification exam is intended to validate the candidates' ability to design, build, and maintain data processing systems using Databricks.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
B. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
C. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
D. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
E. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.

Answer: C

Explanation:
Explanation
This is the correct answer because it addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed. The situation is that an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added, due to new requirements from a customer-facing application. By configuring a new table with all the requisite fields and new names and using this as the source for the customer-facing application, the data engineering team can meet the new requirements without affecting other teams that rely on the existing table schema and name. By creating a view that maintains the original data schema and table name by aliasing select fields from the new table, the data engineering team can also avoid duplicating data or creating additional tables that need to be managed. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under
"CREATE VIEW" section.

NEW QUESTION # 98
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A.
B.
C. preds.write.format("delta").save("/preds/churn_preds")
D.
E. preds.write.mode("append").saveAsTable("churn_preds")

Answer: E

NEW QUESTION # 99
You are still noticing slowness in query after performing optimize which helped you to resolve the small files problem, the column(transactionId) you are using to filter the data has high cardinality and auto incrementing number. Which delta optimization can you enable to filter data effectively based on this column?

A. Create BLOOM FLTER index on the transactionId
B. Increase the driver size and enable delta optimization
C. transactionId has high cardinality, you cannot enable any optimization.
D. Increase the cluster size and enable delta optimization
E. Perform Optimize with Zorder on transactionId
(Correct)

Answer: E

Explanation:
Explanation
The answer is, perform Optimize with Z-order by transactionid
Here is a simple explanation of how Z-order works, once the data is naturally ordered, when a flle is scanned it only brings the data it needs into spark's memory Based on the column min and max it knows which data files needs to be scanned.
Table Description automatically generated

Graphical user interface, diagram, application Description automatically generated

NEW QUESTION # 100
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

A. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
B. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
C. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
D. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
E. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.

Answer: B

Explanation:
When a Databricks job runs multiple tasks with dependencies, the tasks are executed in a dependency graph. If a task fails, the downstream tasks that depend on it are skipped and marked as Upstream failed. However, the failed task may have already committed some changes to the Lakehouse before the failure occurred, and those changes are not rolled back automatically. Therefore, the job run may result in a partial update of the Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake to ensure that the changes are only committed when the entire job run succeeds. Alternatively, you can use the Run if condition to configure tasks to run even when some or all of their dependencies have failed, allowing your job to recover from failures and continue running. Reference:
transactional writes: https://docs.databricks.com/delta/delta-intro.html#transactional-writes Run if: https://docs.databricks.com/en/workflows/jobs/conditional-tasks.html

NEW QUESTION # 101
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. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
C. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
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

Answer: C

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. Z-order 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]

NEW QUESTION # 102
......

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