[Q15-Q31] 100% Free Databricks-Certified-Data-Engineer-Professional Exam Dumps Use Real Databricks Certification Dumps With 250 Questions!

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100% Free Databricks-Certified-Data-Engineer-Professional Exam Dumps Use Real Databricks Certification Dumps With 250 Questions!

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

Section	Objectives
Databricks Lakehouse Platform Architecture	– Data governance concepts (Unity Catalog basics) – Medallion architecture (Bronze, Silver, Gold) – Workspace and cluster architecture
Delta Lake and Data Management	– Time travel and versioning – Schema evolution and enforcement – Delta Lake transactions and ACID properties
Data Ingestion and Processing	– Batch and streaming ingestion with Auto Loader – Structured Streaming fundamentals – ETL pipeline design patterns
Production Pipelines and Orchestration	– Job scheduling and monitoring – Databricks Workflows – Error handling and recovery strategies
Data Modeling and Transformation	– Dimensional modeling concepts – Performance optimization techniques – Spark SQL transformations

NEW QUESTION 15
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.

Which approach would allow using DLT expectations to validate all expected records are present in this table?

Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.

Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table

Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null

Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table

NEW QUESTION 16
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”)

NEW QUESTION 17
How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.

Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.

Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.

Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.

NEW QUESTION 18
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

NEW QUESTION 19
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property “contains_pii” = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

DESCRIBE EXTENDED dev.pii test

DESCRIBE DETAIL dev.pii test

SHOW TBLPROPERTIES dev.pii test

DESCRIBE HISTORY dev.pii test

SHOW TABLES dev

NEW QUESTION 20
A data engineer is building a customer data pipeline in Lakeflow Spark Declarative Pipelines. The source is a cloud-based event stream with limited retention containing inserts, updates, and deletes for customer records. These changes are being applied using the AUTO CDC INTO syntax to maintain an SCD Type 1 table as the target table, customer_dim. How should the data engineer build a downstream job that streams from the customer_dim table to only act on updates and delete events, processing data incrementally?

Use ignoreChanges flag while streaming from customer_dim to avoid breaking the pipeline during updates and deletes.

Read change data feed from customer_dim table and apply filters to incrementally act on the change events.

Streaming from customer_dim table would only be possible in the case of SCD 2 retention.

When stored as SCD 1, the target of AUTO CDC INTO includes updates and deletes. Streaming from customer_dim can fail due to these operations. Instead, build another stream from the original source.

NEW QUESTION 21
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.

NEW QUESTION 22
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.

NEW QUESTION 23
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:
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Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.

Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.

Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.

Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.

Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.

NEW QUESTION 24
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.

Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.

No; the change data feed only tracks inserts and updates not deleted records.

No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command

Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.

NEW QUESTION 25
In order to facilitate near real-time workloads, a data engineer is creating a helper function to Get Latest & Actual Certified-Data-Engineer-Professional Exam’s Question and Answers from leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically 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?

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NEW QUESTION 26
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?
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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.

NEW QUESTION 27
A senior data engineer is planning large-scale data workflows. The current task is to identify the considerations that form a foundation for creating scalable data models that are essential for effective management of large datasets. The data engineering team has identified the core capabilities as part of a scalable data model to build a modern data platform and provided their reasoning for considering Delta Lake for review. The senior data engineer is responsible for identifying the recommendations that are not valid. Which key features can be ignored while evaluating Delta Lake?

Delta Lake works with various data formats (e.g., Parquet, JSON, CSV) and integrates well with Spark and Databricks tools.

Delta Lake optimizes metadata handling, efficiently managing billions of files and facilitating scalability to petabyte-scale datasets.

Delta Lake’s capability to process data in both batch and streaming modes seamlessly, providing flexibility in data ingestion and processing.

Delta Lake provides limited support for monitoring and troubleshooting data pipelines, so relevant partner tools have to be identified and set up for enhanced operational efficiency.

NEW QUESTION 28
A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

Delete the failed run, disable the cluster policy, and re-execute all tasks.

Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.

Use “Repair run”, override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.

Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.

NEW QUESTION 29
A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin

Check the query’s execution time in the Jobs UI and correlate it with cluster resource utilization metrics.

Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.

Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.

NEW QUESTION 30
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

NEW QUESTION 31
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?

When a database is being created, make sure that the LOCATION keyword is used.

When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.

When data is saved to a table, make sure that a full file path is specified alongside the Delta format.

When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.

When the workspace is being configured, make sure that external cloud object storage has been mounted.

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[Q15-Q31] 100% Free Databricks-Certified-Data-Engineer-Professional Exam Dumps Use Real Databricks Certification Dumps With 250 Questions!

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

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