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Databricks-Certified-Professional-Data-Engineer Valid Exam Discount & New Databricks-Certified-Professional-Data-Engineer Mock Test

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Databricks Certified Professional Data Engineer certification exam is a rigorous and challenging exam that requires a deep understanding of data engineering concepts and the Databricks platform. Candidates must have a strong foundation in computer science and data engineering, as well as practical experience using the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and hands-on exercises that test a candidate's ability to design, build, and maintain data pipelines using the Databricks platform.

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Databricks Certified Professional Data Engineer certification exam is a hands-on exam that requires candidates to demonstrate their skills in building data pipelines and workflows using Databricks. Databricks-Certified-Professional-Data-Engineer Exam consists of a set of performance-based tasks that require candidates to design, implement, and manage data solutions in a Databricks environment. Candidates are given a set of data engineering scenarios and must use Databricks to build solutions that meet the requirements of each scenario.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q198-Q203):

NEW QUESTION # 198
Why does AUTO LOADER require schema location?

A. Schema location is used to identify the schema of target table and source table
B. Schema location is used to store schema inferred by AUTO LOADER
C. Schema location is used to identify the schema of target table
D. Schema location is used to store user provided schema
E. AUTO LOADER does not require schema location, because its supports Schema evolution

Answer: B

Explanation:
Explanation
The answer is, Schema location is used to store schema inferred by AUTO LOADER, so the next time AUTO LOADER runs faster as does not need to infer the schema every single time by trying to use the last known schema.
Auto Loader samples the first 50 GB or 1000 files that it discovers, whichever limit is crossed first. To avoid incurring this inference cost at every stream start up, and to be able to provide a stable schema across stream restarts, you must set the option cloudFiles.schemaLocation. Auto Loader creates a hidden directory _schemas at this location to track schema changes to the input data over time.
The below link contains detailed documentation on different options
Auto Loader options | Databricks on AWS

NEW QUESTION # 199
Which of the following data workloads will utilize a gold table as its source?

A. A job that aggregates cleaned data to create standard summary statistics
B. A job that cleans data by removing malformatted records
C. A job that queries aggregated data that already feeds into a dashboard
D. A job that enriches data by parsing its timestamps into a human-readable format
E. A job that ingests raw data from a streaming source into the Lakehouse

Answer: C

Explanation:
Explanation
The answer is, A job that queries aggregated data that already feeds into a dashboard The gold layer is used to store aggregated data, which are typically used for dashboards and reporting.
Review the below link for more info,
Medallion Architecture - Databricks
Gold Layer:
1. Powers Ml applications, reporting, dashboards, ad hoc analytics
2. Refined views of data, typically with aggregations
3. Reduces strain on production systems
4. Optimizes query performance for business-critical data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
Purpose of each layer in medallion architecture

NEW QUESTION # 200
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:

The table daily_store_sales contains all the information needed to update store_sales_summary. The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

A. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
B. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
C. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
D. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
E. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.

Answer: E

Explanation:
The daily_store_sales table contains all the information needed to update store_sales_summary. The schema of the table is:
store_id INT, sales_date DATE, total_sales FLOAT
The daily_store_sales table is implemented as a Type 1 table, which means that old values are overwritten by new values and no history is maintained. The total_sales column might be adjusted after manual data auditing, which means that the data in the table may change over time.
The safest approach to generate accurate reports in the store_sales_summary table is to use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update. Structured Streaming is a scalable and fault-tolerant stream processing engine built on Spark SQL. Structured Streaming allows processing data streams as if they were tables or DataFrames, using familiar operations such as select, filter, groupBy, or join. Structured Streaming also supports output modes that specify how to write the results of a streaming query to a sink, such as append, update, or complete. Structured Streaming can handle both streaming and batch data sources in a unified manner.
The change data feed is a feature of Delta Lake that provides structured streaming sources that can subscribe to changes made to a Delta Lake table. The change data feed captures both data changes and schema changes as ordered events that can be processed by downstream applications or services. The change data feed can be configured with different options, such as starting from a specific version or timestamp, filtering by operation type or partition values, or excluding no-op changes.
By using Structured Streaming to subscribe to the change data feed for daily_store_sales, one can capture and process any changes made to the total_sales column due to manual data auditing. By applying these changes to the aggregates in the store_sales_summary table with each update, one can ensure that the reports are always consistent and accurate with the latest data. Verified Reference: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Structured Streaming" section; Databricks Documentation, under "Delta Change Data Feed" section.

NEW QUESTION # 201
A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB.
How should the data engineer meet this requirement?

A. Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
B. Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.
C. Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.
D. Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The Databricks documentation describes Liquid Clustering as the recommended data layout optimization for evolving workloads. Unlike traditional partitioning or Z-ordering, Liquid Clustering dynamically maintains data organization without rewriting existing files when clustering keys change. It handles data skew automatically and supports flexible re-clustering based on query patterns. Partitioning and Z-ordering require full data rewrites whenever key structures change, making them expensive for tables with frequently evolving access patterns. For tables under a few terabytes, Liquid Clustering offers the best balance between scalability, adaptability, and maintenance efficiency. Thus, option C aligns with Databricks' best practice for modern adaptive layout optimization.

NEW QUESTION # 202
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 fieldpk_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. 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.
D. Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
E. 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.

Answer: B

Explanation:
This is the correct answer because it meets the requirements of maintaining a full record of all values that have ever been valid in the source system and recreating the current table state with only the most recent value for each record. The code ingests all log information into a bronzetable, which preserves the raw CDC data as it is. Then, it uses merge into to perform an upsert operation on a silver table, which means it will insert new records or update or delete existing records based on the change type and the pk_id columns. This way, the silver table will always reflect the current state of the source table, while the bronze table will keep the history of all changes. Verified References: [Databricks Certified Data Engineer Professional], under
"Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.

NEW QUESTION # 203
......

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