PASS GUARANTEED QUIZ 2025 HIGH-QUALITY DATABRICKS-CERTIFIED-DATA-ENGINEER-PROFESSIONAL: DATABRICKS CERTIFIED DATA ENGINEER PROFESSIONAL EXAM DUMPS

Pass Guaranteed Quiz 2025 High-quality Databricks-Certified-Data-Engineer-Professional: Databricks Certified Data Engineer Professional Exam Dumps

Pass Guaranteed Quiz 2025 High-quality Databricks-Certified-Data-Engineer-Professional: Databricks Certified Data Engineer Professional Exam Dumps

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
Which distribution does Databricks support for installing custom Python code packages?

  • A. CRAN
  • B. Wheels
  • C. sbt
  • D. CRAM
  • E. jars
  • F. nom

Answer: F

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
https://learn.microsoft.com/en-us/azure/databricks/workflows/jobs/how-to/use-python-wheels-in- workflows


NEW QUESTION # 66
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

  • A. Ensures that all steps interact correctly to achieve the desired end result
  • B. Troubleshooting is easier since all steps are isolated and tested individually
  • C. Yields faster deployment and execution times
  • D. Improves the quality of your data
  • E. Validates a complete use case of your application

Answer: B

Explanation:
Unit tests are small, isolated tests that are used to check specific parts of the code, such as functions or classes.


NEW QUESTION # 67
Which statement describes the correct use of pyspark.sql.functions.broadcast?

  • A. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
  • B. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
  • C. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
  • D. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
  • E. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.

Answer: C

Explanation:
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html The broadcast function in PySpark is used in the context of joins. When you mark a DataFrame with broadcast, Spark tries to send this DataFrame to all worker nodes so that it can be joined with another DataFrame without shuffling the larger DataFrame across the nodes. This is particularly beneficial when the DataFrame is small enough to fit into the memory of each node. It helps to optimize the join process by reducing the amount of data that needs to be shuffled across the cluster, which can be a very expensive operation in terms of computation and time.
The pyspark.sql.functions.broadcast function in PySpark is used to hint to Spark that a DataFrame is small enough to be broadcast to all worker nodes in the cluster. When this hint is applied, Spark can perform a broadcast join, where the smaller DataFrame is sent to each executor only once and joined with the larger DataFrame on each executor. This can significantly reduce the amount of data shuffled across the network and can improve the performance of the join operation. In a broadcast join, the entire smaller DataFrame is sent to each executor, not just a specific column or a cached version on attached storage. This function is particularly useful when one of the DataFrames in a join operation is much smaller than the other, and can fit comfortably in the memory of each executor node.


NEW QUESTION # 68
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

  • A. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
  • B. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
  • C. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
  • D. Create a separate history table for each pk_id resolve the current state of the table by running a Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from union all filtering the history tables for the most recent state.
  • E. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.

Answer: B

Explanation:
CDF captures changes only from a Delta table and is only forward-looking once enabled. The CDC logs are writing to object storage. So you would need to ingestion those and merge into downstream tables.


NEW QUESTION # 69
The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

  • A. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
  • B. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
  • C. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
  • D. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.
  • E. Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.

Answer: B

Explanation:
This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability.


NEW QUESTION # 70
......

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