[Sep-2026] Verified Databricks-Certified-Professional-Data-Engineer dumps Q&As – Databricks-Certified-Professional-Data-Engineer dumps with Correct Answers [Q41-Q58]

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[Sep-2026] Verified Databricks-Certified-Professional-Data-Engineer dumps Q&As – Databricks-Certified-Professional-Data-Engineer dumps with Correct Answers

The Best Databricks Certification Study Guide for the Databricks-Certified-Professional-Data-Engineer Exam

Databricks is a leading company in the field of data engineering, providing a cloud-based platform for collaborative data analysis and processing. The company’s platform is used by a wide range of companies and organizations, including Fortune 500 companies, government agencies, and academic institutions. Databricks offers a range of certifications to help professionals demonstrate their proficiency in using the platform, including the Databricks Certified Professional Data Engineer certification.

Databricks Certified Professional Data Engineer exam is a comprehensive assessment of a candidate’s ability to design, implement, and manage data pipelines on the Databricks platform. Databricks Certified Professional Data Engineer Exam certification exam covers a wide range of topics, including data ingestion, data processing, data transformation, and data storage. Databricks-Certified-Professional-Data-Engineer exam is designed to test the candidate’s knowledge of best practices for building efficient and scalable data pipelines that can handle large volumes of data.

Q41. A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.
all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high-cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time.
Which command should the data engineer implement?

Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.

Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.

Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.

Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.

Q42. The data engineering team maintains the following code:

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

The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.

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

The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.

An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.

An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.

Q43. The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalidlatitudeandlongitudevalues in theactivity_detailstable have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to addCHECKconstraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.

The activity details table already exists; CHECK constraints can only be added during initial table creation.

The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.

The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.

The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.

Q44. 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?

Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.

The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.

Shuffle Hash Joins are always more efficient than Sort Merge Joins.

The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.

Q45. The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.

How would the following update be handled?

The update is moved to separate ”restored” column because it is missing a column expected in the target schema.

The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.

The update throws an error because changes to existing columns in the target schema are not supported.

The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.

Q46. 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”)

Q47. You are asked to debug a databricks job that is taking too long to run on Sunday’s, what are the steps you are going to take to identify the step that is taking longer to run?

A notebook activity of job run is only visible when using all-purpose cluster.

Under Workflow UI and jobs select job you want to monitor and select the run, notebook activity can be viewed.

Enable debug mode in the Jobs to see the output activity of a job, output should be available to view.

Once a job is launched, you cannot access the job’s notebook activity.

Use the compute’s spark UI to monitor the job activity.

Q48. 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.

Q49. 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?

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.

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.

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.

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.

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.

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.

Q50. A junior data engineer is working to implement logic for a Lakehouse table namedsilver_device_recordings.
The source data contains 100 unique fields in a highly nested JSON structure.
Thesilver_device_recordingstable will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

The Tungsten encodingused by Databricksis optimized for storing stringdata; newly-added nativesupport for querying JSON strings means that stringtypes are always most efficient.

Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.

Human labor in writingcode is the largest cost associated with data engineering workloads; as such, automatingtable declaration logic should be a priority in all migration workloads.

Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.

Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

Q51. Which of the following is a true statement about the global temporary view?

A global temporary view is available only on the cluster it was created, when the cluster restarts global temporary view is automatically dropped.

A global temporary view is available on all clusters for a given workspace

A global temporary view persists even if the cluster is restarted

A global temporary view is stored in a user database

A global temporary view is automatically dropped after 7 days

Q52. You are working on a process to query the table based on batch date, and batch date is an input parameter and expected to change every time the program runs, what is the best way to we can parameterize the query to run without manually changing the batch date?

Create a notebook parameter for batch date and assign the value to a python variable and use a spark data frame to filter the data based on the python variable

Create a dynamic view that can calculate the batch date automatically and use the view to query the data

There is no way we can combine python variable and spark code

Manually edit code every time to change the batch date

Store the batch date in the spark configuration and use a spark data frame to filter the data based on the spark configuration.

Q53. A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

Three new jobs named “Ingest new data” will be defined in the workspace, and they will each run once daily.

The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.

Three new jobs named “Ingest new data” will be defined in the workspace, but no jobs will be executed.

One new job named “Ingest new data” will be defined in the workspace, but it will not be executed.

The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.

Q54. A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses.
Which design approach for the date dimension will support efficient time-based querying and aggregation?

Store the date as a string in the format YYYY-MM-DD for readability.

Create separate dimension tables for different calendar systems (fiscal, academic, etc.).

Store only the date value and calculate all time attributes dynamically in queries.

Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.

Q55. A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format(“cloudFiles”)
.option(“cloudFiles.format”, “json”)
.option(“badRecordsPath”, “/tmp/somewhere/badRecordsPath”)
.schema(“a int, b int”)
.load(“/Volumes/catalog/schema/raw_data/”))
What is the cause of the missing data?

At some point, the upstream data provider switched everything to multi-line JSON.

The badRecordsPath location is accumulating many small files.

The source data is valid JSON but does not conform to the defined schema in some way.

The engineer forgot to set the option “cloudFiles.quarantineMode” = “rescue”.

Q56. A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

Task queueing resulting from improper thread pool assignment.

Spill resulting from attached volume storage being too small.

Network latency due to some cluster nodes being in different regions from the source data

Skew caused by more data being assigned to a subset of spark-partitions.

Credential validation errors while pulling data from an external system.

Q57. In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function willautomatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

Option A

Option B

Option C

Option D

Option E

Q58. What is the output of the below function when executed with input parameters 1, 3 :
1.def check_input(x,y):
2. if x < y:
3. x= x+1
4. if x<y:
5. x= x+1
6. if x <y:
7. x = x+1
8. return x
check_input(1,3)

3
(Correct)

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[Sep-2026] Verified Databricks-Certified-Professional-Data-Engineer dumps Q&As – Databricks-Certified-Professional-Data-Engineer dumps with Correct Answers [Q41-Q58]

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