Databricks-Certified-Data-Engineer-Professional Dumps PDF New [2026] Ultimate Study Guide [Q65-Q84]

질문 65
A data architect has heard about lake’s built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of Get Latest & Actual Certified-Data-Engineer-Professional Exam’s Question and Answers from information is critical to this decision?

Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.

Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.

Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.

Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.

Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.

질문 66
A streaming video analytics team ingests billions of events daily into a Unity Catalog-managed Delta table video_events. Analysts run ad-hoc point-lookup queries on columns like user_id, campaign_id, and region. The team manually runs OPTIMIZE video_events ZORDER BY (user_id, campaign_id, region), but still sees poor performance on recent data and dislikes the operational overhead. The team wants a hands-off way to keep hot columns co-located as query patterns evolve. Which Delta capability should the team leverage on video_events?

Schedule OPTIMIZE/ZORDER to run after each job to improve recent file performance.

Enable Delta caching.

Utilize Liquid Clustering (CLUSTER BY AUTO) and Predictive Optimization.

Enable auto-compaction (optimizeWrite and autoCompact).

질문 67
The data engineer is using Spark’s MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI’s Storage tab to signal that a cached table is not performing optimally?

Size on Disk is> 0

The number of Cached Partitions> the number of Spark Partitions

The RDD Block Name included the ” annotation signaling failure to cache

On Heap Memory Usage is within 75% of off Heap Memory usage

Size on Disk is < Size in Memory

질문 68
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 Get Latest & Actual Certified-Data-Engineer-Professional Exam’s Question and Answers from events and the job run events.

질문 69
A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?

All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.

All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.

All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.

All records from all columns will be displayed with the values in user_ltv.

All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.

질문 70
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.

Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.

Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.

Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.

Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

질문 71
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.

Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.

Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.

Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.

Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.

질문 72
A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?

Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.

Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.

Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.

Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.

Reprocess all records in reviews_raw and overwrite the next table in the pipeline.

질문 73
A junior data engineer seeks to leverage Delta Lake’s Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.

Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.

Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.

Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.

Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

질문 74
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device. Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot. How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

Set the skipChangeCommits flag to true on bpm_stats

Set the SkipChangeCommits flag to true raw_lot

Set the pipelines, reset, allowed property to false on bpm_stats

Set the pipelines, reset, allowed property to false on raw_iot

질문 75
Which method can be used to determine the total wall-clock time it took to execute a query?

In the Spark UI, take the job duration of the longest-running job associated with that query.

In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.

Open the Query Profiler associated with that query and use the Total wall-clock duration metric.

Open the Query Profiler associated with that query and use the Aggregated task time metric.

질문 76
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.

질문 77
To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control.
The team decided to use Databricks system tables, available under the System catalog in Unity Catalog, to gain detailed visibility into workspace activity. Which SQL query should the team run from the System catalog to achieve this?

SELECT sku_name,
identity_metadata.created_by AS user_email,
COUNT(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10

SELECT identity_metadata.run_as AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email
ORDER BY total_dbus DESC
LIMIT 10

SELECT sku_name,
identity_metadata.created_by AS user_email,
SUM(usage_quantity * usage_unit) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10

SELECT sku_name,
usage_metadata.run_name AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10

질문 78
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.

질문 79
Which statement characterizes the general programming model used by Spark Structured Streaming?

Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.

Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.

Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.

Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.

Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.

질문 80
A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a “dev” target to be the default and override specific paths. How should the team use databricks.yml to satisfy these requirements?

Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.

Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.

Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.

Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default:true and override workspace paths or artifacts under that target.

질문 81
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bpm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

Set the skipChangeCommits flag to true on bpm_stats

Set the SkipChangeCommits flag to true raw_lot

Set the pipelines, reset, allowed property to false on bpm_stats

Set the pipelines, reset, allowed property to false on raw_iot

질문 82
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”)

질문 83
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active Get Latest & Actual Certified-Data-Engineer-Professional Exam’s Question and Answers from user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

The connection to the external table will fail; the string “redacted” will be printed.

An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.

An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.

The connection to the external table will succeed; the string value of password will be printed in plain text.

The connection to the external table will succeed; the string “redacted” will be printed.

질문 84
A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context.
Assuming the job is already deployed, they need to trigger its execution while ensuring the target- specific configuration is respected. Which command will trigger the job execution?

databricks execute my_project_job -e prod

databricks job run my_project_job –env prod

databricks run my_project_job -t prod

databricks bundle run my_project_job -t prod

Databricks-Certified-Data-Engineer-Professional Dumps PDF New [2026] Ultimate Study Guide [Q65-Q84]

Databricks-Certified-Data-Engineer-Professional Dumps PDF New [2026] Ultimate Study Guide [Q65-Q84]

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