Databricks-Certified-Data-Engineer-Professional Valid Test Syllabus - Valid Dumps Databricks-Certified-Data-Engineer-Professional Free

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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Databricks Lakehouse Platform	24%	- Delta Lake - Lakehouse Architecture - Unity Catalog - Data Management
Monitoring and Troubleshooting	16%	- Troubleshooting - Monitoring - Performance Optimization
Data Modeling and Storage	20%	- Data Modeling - File Formats - Storage Optimization
Data Quality and Governance	12%	- Data Quality - Governance - Data Lineage
Data Processing	28%	- Data Transformation - ETL Pipelines - Spark SQL - Structured Streaming

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
B. Define a view against the products_per_order table and define the dashboard against this view.
C. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
D. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
E. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.

Answer: D

NEW QUESTION # 33
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.

Answer: D

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.

NEW QUESTION # 34
A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats. After converting a subset of data to Delta Lake, they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join, whereas queries on legacy formats used Sort Merge Joins. The queries reading Delta Lake data also scanned less data. Which reason could be attributed to the difference in query performance?

A. Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.
B. Shuffle Hash Joins are always more efficient than Sort Merge Joins.
C. The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.
D. The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.

Answer: A

Explanation:
Delta Lake outperforms legacy Hadoop formats because it leverages Parquet-based storage, data skipping, and file pruning. According to Databricks documentation, Delta Lake automatically stores detailed statistics (min/max values and file-level metadata) in the transaction log. During query planning, the engine uses these statistics to skip entire files that do not match query filters, a process called data skipping and file pruning. Additionally, Delta uses a vectorized Parquet reader, which reduces I/O and CPU overhead. Together, these optimizations allow Delta to scan significantly less data and produce more efficient physical query plans (e.g., Shuffle Hash Join instead of Sort Merge Join). The performance gain is due to efficient data skipping, not the inherent superiority of join type.

NEW QUESTION # 35
A platform team lead is responsible for automating the individual teams attribution towards SQL Warehouse usage. The requirement is to identify the SQL warehouse usage at the individual user's level and generate a daily report to be shared with an executive team that includes leaders from all business units. How should the platform lead generate an automated report that can be shared daily?

A. Let the users run the SQL query and then directly report the usage to the executives. The ownership of the SQL warehouse usage will be with the individual teams.
B. Use the system tables to capture the audit and billing usage data and share the queries with the executive team. This enables the executives to execute the query and see the latest results any time.
C. Use the system tables to capture the audit and billing usage data and create a dashboard with daily refresh schedules and shared with the executive team.
D. Restrict users from running any SQL query unless they provide all the query details so that the attribution can be calculated and shared with the executive team.

Answer: C

Explanation:
System tables provide authoritative audit and billing data needed for per-user SQL Warehouse attribution. Creating a dashboard with a scheduled daily refresh automates report generation and ensures executives receive consistent, up-to-date insights without needing to run queries themselves.

NEW QUESTION # 36
In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone. A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?

A. Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.
B. Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
C. The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command
D. Tables created with SHALLOW CLONE are automatically deleted after their default retention threshold of 7 days.
E. The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.

Answer: C

Explanation:
In Delta Lake, a shallow clone creates a new table by copying the metadata of the source table without duplicating the data files. When the vacuum command is run on the source table, it removes old data files that are no longer needed to maintain the transactional log's integrity, potentially including files referenced by the shallow clone's metadata. If these files are purged, the shallow cloned tables will reference non-existent data files, causing them to stop working properly. This highlights the dependency of shallow clones on the source table's data files and the impact of data management operations like vacuum on these clones.

NEW QUESTION # 37
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