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By passing the DCPDE exam, data engineers can demonstrate their proficiency in using the Databricks platform to build scalable and reliable data pipelines. Databricks Certified Professional Data Engineer Exam certification can help data engineers advance their careers and increase their earning potential by showcasing their expertise in data engineering on Databricks.
Databricks Certified Professional Data Engineer certification exam is a highly sought-after certification in the data engineering industry. It is designed to test the skills and knowledge of data engineers who work with Databricks, a cloud-based platform that helps organizations manage large amounts of data and perform advanced analytics.
Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) exam is a certification program designed to validate the skills and expertise of data engineers in developing and managing big data pipelines using Databricks. Databricks-Certified-Professional-Data-Engineer exam is ideal for data engineers, ETL developers, and data architects who work with Databricks and want to showcase their skills and proficiency.
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Databricks Certified Professional Data Engineer Exam Sample Questions (Q95-Q100):NEW QUESTION # 95 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 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.
* B. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
* C. 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.
* D. 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.
* E. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
Answer: DExplanation:
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. Verified References: , under "Lakehouse" section; Databricks Documentation, under "Database object privileges" section.
NEW QUESTION # 96 A table is registered with the following code:
Bothusersandordersare Delta Lake tables. Which statement describes the results of queryingrecent_orders?
* A. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
* B. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
* C. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
* D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
* E. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
Answer: CNEW QUESTION # 97 A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?
* A. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
* B. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
* C. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
* D. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
* E. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
Answer: CExplanation:
This is the correct answer because it describes how data will be filtered when a query is run with the following filter: longitude < 20 & longitude > -20. The query is run on a Delta Lake table that has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE. This table is partitioned by the date column. When a query is run on a partitioned Delta Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might include records in the filtered range. The statistics include information such as min and max values for each column in each data file. By using these statistics, Delta Lake can skip reading data files that do not match the filter condition, which can improve query performance and reduce I/O costs. Verified References: , under "Delta Lake" section; Databricks Documentation, under "Data skipping" section.
NEW QUESTION # 98 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. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
* D. 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.
Answer: DExplanation:
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
NEW QUESTION # 99 A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:
Which statement describes the execution and results of running the above query multiple times?
* A. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
* B. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
* C. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
* D. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
* E. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
Answer: CExplanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.
NEW QUESTION # 100......
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