[Oct 05, 2026] Get New Databricks-Certified-Data-Engineer-Professional Certification Practice Test Questions Exam Dumps [Q141-Q163]

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[Oct 05, 2026] Get New Databricks-Certified-Data-Engineer-Professional Certification Practice Test Questions Exam Dumps

Real Databricks-Certified-Data-Engineer-Professional Exam Dumps Questions Valid Databricks-Certified-Data-Engineer-Professional Dumps PDF

NEW QUESTION 141
An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

Create a new table for the analytics team using a CTAS statement.

Deep clone the table for the analytics team.

Give the analytics team direct access to the production table.

Shallow clone the table for the analytics team.

A shallow clone of the production Delta table creates an instantaneous snapshot that references the same data files, so it introduces virtually no downtime or storage overhead and avoids interfering with the ongoing ETL. A deep clone would copy all data (very expensive and slow for
20B rows). CTAS rewrites data and is unnecessary; direct access to prod risks contention and accidental changes.

NEW QUESTION 142
A data engineer, while designing a Pandas UDF to process financial time-series data with complex calculations that require maintaining state across rows within each stock symbol group, must ensure the function is efficient and scalable. Which approach will solve the problem with minimum overhead while preserving data integrity?

Use a SCALAR_ITER Pandas UDF with iterator-based processing, implementing state management through persistent storage (Delta tables) that gets updated after each batch to maintain continuity across iterator chunks.

Use a SCALAR Pandas UDF that processes the entire dataset at once, implementing custom partitioning logic within the UDF to group by stock symbol and maintain state using global variables shared across all executor processes.

Use applyInPandas() on a Spark DataFrame that receives all rows for each stock symbol as a Pandas DataFrame, allowing processing within each group while maintaining state variables local to each group’s processing function.

Use a grouped_agg Pandas UDF that processes each stock symbol group independently, maintaining state through intermediate aggregation results that get passed between successive UDF calls via broadcast variables.

The Databricks documentation recommends applyInPandas() for complex per-group operations where maintaining internal state within each group is necessary. When using applyInPandas(), Spark provides all records for each grouping key as a Pandas DataFrame to the function, allowing efficient vectorized operations with local state management. This approach ensures high performance and scalability while maintaining logical isolation between groups. In contrast, SCALAR and SCALAR_ITER UDFs operate on individual rows or batches and cannot maintain inter-row state effectively. grouped_agg UDFs are limited to computing aggregates and do not support complex multi-row transformations. Therefore, applyInPandas() is the correct and Databricks-recommended solution for stateful per-group time-series computations.

NEW QUESTION 143
A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?

Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.

Use Delta Lake’s DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.

Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.

Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.

The Catalog Explorer provides an AI-powered “AI Generate” capability that automatically creates intelligent column descriptions by analyzing column names, data types, sample values, and observed data patterns. This approach enables rapid, scalable documentation of existing tables, significantly improving data discoverability and compliance without manual effort.

NEW QUESTION 144
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.

This code is using the pyspark.sql.functions library to group the silver_customer_sales table by customer_id and then aggregate the data using the minimum sale date, maximum sale total, and sum of distinct order ids. The resulting aggregated data is then written to the gold_customer_lifetime_sales_summary table, overwriting any existing data in that table. This is a batch job that does not use any incremental or streaming logic, and does not perform any merge or update operations. Therefore, the code will overwrite the gold table with the aggregated values from the silver table every time it is executed.

NEW QUESTION 145
A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

The table reference in the metastore is updated and no data is changed.

The table name change is recorded in the Delta transaction log.

All related files and metadata are dropped and recreated in a single ACID transaction.

The table reference in the metastore is updated and all data files are moved.

A new Delta transaction log Is created for the renamed table.

The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly.

NEW QUESTION 146
Which statement describes a key benefit of an end-to-end test?

It closely simulates real world usage of your application.

It pinpoint errors in the building blocks of your application.

It provides testing coverage for all code paths and branches.

It makes it easier to automate your test suite

End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real- world, user behavior, ensuring that the system as a whole operates correctly.
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NEW QUESTION 147
The data science team has created and logged a production model using MLflow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model.

Which code block will output a DataFrame with the schema “customer_id LONG, predictions DOUBLE”?

model.predict(df, columns)

df.map(lambda x:model(x[columns])).select(“customer_id, predictions”)

df.select(“customer_id”, model(*columns).alias(“predictions”))

df.apply(model, columns).select(“customer_id, predictions”)

df.select(“customer_id”, pandas_udf(model, columns).alias(“predictions”))

This code block applies the Spark UDF created from the MLflow model to the DataFrame df by selecting the existing customer_id column and the new column produced by the model, which is aliased to predictions. The model(*columns) part is where the UDF is applied to the columns specified in the columns list, and alias(“predictions”) is used to name the output column of the model’s predictions. This will result in a DataFrame with the desired schema: “customer_id LONG, predictions DOUBLE”.

NEW QUESTION 148
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.

https://docs.databricks.com/en/data-governance/unity-catalog/create-schemas.html#language- SQL

NEW QUESTION 149
A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)

Apply liquid clustering using the merge join keys.

Enable deletion vectors on the table if not already enabled.

Partition the table by date.

Use ZORDER on high-cardinality columns.

Overwrite the table instead of Merge.

Liquid clustering on the merge join keys improves data locality and reduces the amount of data scanned during MERGE operations, which is especially effective for large, frequently updated tables. Enabling deletion vectors avoids rewriting entire Parquet files for updates and deletes, significantly reducing I/O and improving MERGE performance on Unity Catalog-managed tables.

NEW QUESTION 150
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?

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.

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.

NEW QUESTION 151
What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?

PII masking is only required for direct identifiers.

Dynamic data masking is applied only at rest, so it does not affect query performance.

Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.

Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.

Consistent PII masking in distributed batch and streaming pipelines requires centrally defined and governed masking logic. Standardizing masking functions and enforcing them through Unity Catalog ensures that the same rules are applied uniformly across all datasets and execution paths, preventing inconsistencies across nodes and workloads.

NEW QUESTION 152
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

Maintain data quality rules in a Delta table outside of this pipeline’s target schema, providing the schema name as a pipeline parameter.

Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.

Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.

Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.

Maintaining data quality rules in a centralized Delta table allows for the reuse of these rules across multiple DLT (Delta Live Tables) pipelines. By storing these rules outside the pipeline’s target schema and referencing the schema name as a pipeline parameter, the team can apply the same set of data quality checks to different tables within the pipeline. This approach ensures consistency in data quality validations and reduces redundancy in code by not having to replicate the same rules in each DLT notebook or file.

NEW QUESTION 153
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

return spark.readStream.table(“bronze”)

return spark.readStream.load(“bronze”)

return spark.read.option(“readChangeFeed”, “true”).table (“bronze”)

https://docs.databricks.com/en/delta/delta-change-data-feed.html

NEW QUESTION 154
A faulty IoT sensor in a factory reports a temperature of -500, causing the LDP pipeline to fail the expectation, which only allows values between -100 and 200 degrees Celsius. The data engineer would like to further analyze the faulty data to better understand the reason behind this. How should the data engineer resolve the faulty data while ensuring data quality standards are maintained?

Remove all expectations form the pipeline to prevent any future failures, regardless of data quality.

Ignore the error and simply re-run the pipeline, as Databricks will automatically skip the problematic record on the next run.

Fix the pipeline code and implement a quarantine logic to isolate the faulty data before re-running the pipeline.

Change the expectation action from fail to warn so that invalid records are included in the output and the pipeline does not fail.

Implementing quarantine logic allows invalid records to be isolated for further analysis while keeping them out of trusted datasets. Fixing the pipeline and re-running it ensures the expectation continues to enforce data quality standards, prevents future failures, and enables root-cause analysis of the faulty sensor data without compromising downstream consumers.

NEW QUESTION 155
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 a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

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

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

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

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

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https://docs.databricks.com/en/clusters/cluster-config-best-practices.html

NEW QUESTION 156
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

In the context of cluster performance and resource utilization, a CPU utilization rate of around
75% is generally considered a good indicator of efficient resource usage. This level of CPU utilization suggests that the cluster is being effectively used without being overburdened or underutilized. A consistent 75% CPU utilization indicates that the cluster’s processing power is being effectively employed while leaving some headroom to handle spikes in workload or additional tasks without maxing out the CPU, which could lead to performance degradation. A five Minute Load Average that remains consistent/flat (Option A) might indicate underutilization or a bottleneck elsewhere.
Monitoring network I/O (Options B and C) is important, but these metrics alone don’t provide a complete picture of resource utilization efficiency.
Total Disk Space (Option D) remaining constant is not necessarily an indicator of proper resource utilization, as it’s more related to storage rather than computational efficiency.

NEW QUESTION 157
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
“device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT”
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

withWatermark(“event_time”, “10 minutes”)

awaitArrival(“event_time”, “10 minutes”)

await(“event_time + `10 minutes'”)

slidingWindow(“event_time”, “10 minutes”)

delayWrite(“event_time”, “10 minutes”)

This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming.

NEW QUESTION 158
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.

Databricks jobs create will create a new job with the same name each time it is run.
In order to overwrite the extsting job you need to run databricks jobs reset

NEW QUESTION 159
A data engineer is evaluating tools to build a production-grade data pipeline. The team must process change data from cloud object storage, filter out or isolate invalid records, and ensure the timely delivery of clean data to downstream consumers. The team is small, under tight deadlines, and wants to minimize operational overhead while keeping pipelines auditable and maintainable.
Which approach should the data engineer implement?

Ingest data directly into Delta tables via Spark jobs, apply data quality filters using UDFs, and use LDP for creating Materialized Views.

Use a hybrid approach: Ingest with Auto Loader into Bronze tables, then process using SQL queries in Databricks Workflows to generate cleaned Silver and Gold tables on a schedule.

Implement ingestion using Auto Loader with Structured Streaming, and manage invalid data handling and table updates using checkpointing and merge logic.

Use LDP to build declarative pipelines with Streaming Tables and Materialized Views, leveraging built-in support for data expectations and incremental processing.

LDP provides a declarative framework for building production-grade pipelines with minimal operational overhead. Streaming Tables and Materialized Views handle incremental processing automatically, while built-in data expectations allow invalid records to be filtered or isolated in a consistent and auditable way. This approach is well suited for small teams under tight deadlines, as it simplifies maintenance, improves reliability, and ensures timely delivery of clean data to downstream consumers.

NEW QUESTION 160
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

Workspace audit logs

Driver’s log file

Ganglia

Cluster Event Log

Executor’s log file

The Cluster Event Log in Databricks will show the timeline for cluster resizing events, including details about when and why a cluster was resized (scaled up or down). This log would help the workspace administrator determine the causes of cluster scaling, whether due to many concurrent users submitting jobs or a few users running resource-intensive queries.

NEW QUESTION 161
A data engineer is creating a daily reporting job. There are two reporting notebooks–one for weekdays and one for weekends. An “if/else condition” task is configured as
{{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?

Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.

Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.

Merge, as the job configuration looks good.

Reject, as they should use {{job.trigger_time.is_weekday}} instead.

Databricks parameter templates like {{job.start_time.is_weekday}} evaluate in UTC time by default, not in local workspace or regional time zones. Therefore, when jobs are configured to run across different time zones, relying on is_weekday using UTC may cause scheduling and task routing mismatches (for example, triggering the weekday notebook in one region while it’s still the weekend locally).
Databricks recommends adjusting conditional logic or pipeline parameters explicitly to handle time zone conversions if business requirements depend on local times. Because the engineer’s configuration does not account for this behavior, a senior data engineer should reject the pull request and suggest time-zone-aware logic before merging.

NEW QUESTION 162
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store
If this statement is executed by a workspace admin, which result will occur?

Nothing will occur until a COMMIT command is executed.

The table will be removed from the catalog but the data will remain in storage.

The table will be removed from the catalog and the data will be deleted.

An error will occur because Delta Lake prevents the deletion of production data.

Data will be marked as deleted but still recoverable with Time Travel.

When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage.

NEW QUESTION 163
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the “Owner” for each job.
They attempt to transfer “Owner” privileges to the “DevOps” group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

Databricks jobs must have exactly one owner; “Owner” privileges cannot be assigned to a group.

The creator of a Databricks job will always have “Owner” privileges; this configuration cannot be changed.

Other than the default “admins” group, only individual users can be granted privileges on jobs.

A user can only transfer job ownership to a group if they are also a member of that group.

Only workspace administrators can grant “Owner” privileges to a group.

A job cannot have more than one owner. A job cannot have a group as an owner.

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