New Databricks-Certified-Data-Engineer-Professional Test Materials & Valid Databricks-Certified-Data-Engineer-Professional Test Engine [Q60-Q75]

4/5 - (1 vote)

New Databricks-Certified-Data-Engineer-Professional Test Materials & Valid Databricks-Certified-Data-Engineer-Professional Test Engine

Databricks-Certified-Data-Engineer-Professional Updated Exam Dumps [2026] Practice Valid Exam Dumps Question

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

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

Q60. The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.

The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.

An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.

An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.

No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.

Q61. A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.

Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.

Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake’s upsert functionality.

Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.

Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.

Q62. A data engineer needs to install the PyYAML Python package within an air-gapped Databricks environment. The workspace has no direct internet access to PyPI. The engineer has downloaded the .whl file locally and wants it available automatically on all new clusters. Which approach should the data engineer use?

Upload the PyYAML .whl file to the user home directory and create a cluster-scoped init script to install it.

Upload the PyYAML .whl file to a Unity Catalog Volume, ensure it’s allow-listed, and create a cluster-scoped init script that installs it from that path.

Set up a private PyPI repository and install via pip index URL.

Add the .whl file to Databricks Git Repos and assume automatic installation.

Q63. A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?

Wrap the for loop inside another table definition, using generalized names and properties to replace with those from the inner table definition.

Convert the list of configuration values to a dictionary of table settings, using table names as keys.

Move the table definition into a separate function, and make calls to this function using different input parameters inside the for loop.

Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.

Q64. Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

– Total VMs: 8
– 50 GB per Executor
– 20 Cores / Executor

– Total VMs: 16
– 25 GB per Executor
Get Latest & Actual Certified-Data-Engineer-Professional Exam’s Question and Answers from
– 10 Cores / Executor

– Total VMs: 1
– 400 GB per Executor
– 160 Cores/Executor

– Total VMs: 4
– 100 GB per Executor
– 40 Cores / Executor

– Total VMs: 2
– 200 GB per Executor
– 80 Cores / Executor

Q65. A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.

Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.

Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.

Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.

Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.

Q66. 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?

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.

Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.

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.

Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.

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.

Q67. The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer’s suggestion is correct?

Delta Lake statistics are not optimized for free text fields with high cardinality.

Text data cannot be stored with Delta Lake.

ZORDER ON review will need to be run to see performance gains.

The Delta log creates a term matrix for free text fields to support selective filtering.

Delta Lake statistics are only collected on the first 4 columns in a table.

Q68. A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction

Databricks has autotuned to a smaller target file size based on the overall size of data in the table

Databricks has autotuned to a smaller target file size based on the amount of data in each partition

Q69. 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?

The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.

Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.

The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command

Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.

Tables created with SHALLOW CLONE are automatically deleted after their default retention threshold of 7 days.

Q70. A data engineer is designing a Lakeflow Declarative Pipeline to process streaming order data.
The pipeline uses Auto Loader to ingest data and must enforce data quality by ensuring customer_id and amount are greater than zero. Invalid records should be dropped. Which Lakeflow Declarative Pipelines configurations implement this requirement using Python?

@dlt.table
def silver_orders():
return (
dlt.read_stream(“bronze_orders”)
.expect_or_drop(“valid_customer”, “customer_id IS NOT NULL”)
.expect_or_drop(“valid_amount”, “amount > 0”)
)

@dlt.table
@dlt.expect(“valid_customer”, “customer_id IS NOT NULL”)
@dlt.expect(“valid_amount”, “amount > 0”)
def silver_orders():
return dlt.read_stream(“bronze_orders”)

@dlt.table
def silver_orders():
return (
dlt.read_stream(“bronze_orders”)
.expect(“valid_customer”, “customer_id IS NOT NULL”)
.expect(“valid_amount”, “amount > 0”)
)

@dlt.table
@dlt.expect_or_drop(“valid_customer”, “customer_id IS NOT NULL”)
@dlt.expect_or_drop(“valid_amount”, “amount > 0”)
def silver_orders():
return dlt.read_stream(“bronze_orders”)

Q71. When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM’s resources?

The five Minute Load Average remains consistent/flat

Bytes Received never exceeds 80 million bytes per second

Network I/O never spikes

Total Disk Space remains constant

CPU Utilization is around 75% Get Latest & Actual Certified-Data-Engineer-Professional Exam’s Question and Answers from

Q72. 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?
Get Latest & Actual Certified-Data-Engineer-Professional Exam’s Question and Answers from

Parse the Delta Lake transaction log to identify all newly written data files.

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.

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.

Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.

Q73. A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization’s data retention policy. Which code snippet correctly sets this retention period for deleted files?

spark.sql(“ALTER TABLE my_table SET TBLPROPERTIES
(‘delta.deletedFileRetentionDuration’ = ‘interval 15 days’)”)

from delta.tables import *
deltaTable = DeltaTable.forPath(spark, “/mnt/data/my_table”)
deltaTable.deletedFileRetentionDuration = “interval 15 days”

spark.sql(“VACUUM my_table RETAIN 15 HOURS”)

spark.conf.set(“spark.databricks.delta.deletedFileRetentionDuration”, “15 days”)

Q74. A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

Integration

Unit

Manual

functional

End-to-end

Q75. An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.

An external table will be created in the storage container mounted to /mnt/finance eda bucket.

A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.

An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket.

A managed table will be created in the DBFS root storage container.

Databricks-Certified-Data-Engineer-Professional Sample with Accurate & Updated Questions: https://www.testsimulate.com/Databricks-Certified-Data-Engineer-Professional-study-materials.html

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

Leave a Reply Cancel reply