Databricks Databricks-Certified-Professional-Data-Engineer Latest Exam Cost, Databricks-Certified-Professional-Data-Engineer Reliable Dumps Pdf

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

Section	Weight	Objectives
Topic 1: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Task dependencies and orchestration 2. Monitoring and alerting 3. Jobs and job scheduling
Topic 2: Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. DataFrame operations and transformations 2. Spark SQL queries and functions 3. Window functions - Python and SQL for data engineering 1. Built-in and user-defined functions 2. Spark APIs in Python 3. Performance optimization techniques
Topic 3: Delta Lake	20-25%	- Delta Lake fundamentals 1. ACID transactions 2. Optimize and Z-order 3. Time travel and data versioning - Delta Lake operations 1. Schema evolution and enforcement 2. Delta Live Tables 3. Merge, update, delete operations
Topic 4: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Data governance fundamentals 2. Differences between data lake, data warehouse, and lakehouse 3. Bronze, silver, gold data layers
Topic 5: Data Ingestion	15-20%	- Batch ingestion methods 1. Integration with external systems 2. Spark APIs for ingestion 3. DBR autoloader - Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q201-Q206):

NEW QUESTION # 201
What statement is true regarding the retention of job run history?

A. It is retained for 90 days or until the run-id is re-used through custom run configuration
B. It is retained for 60 days, after which logs are archived
C. It is retained until you export or delete job run logs
D. t is retained for 60 days, during which you can export notebook run results to HTML
E. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

Answer: D

NEW QUESTION # 202
A data engineer in a workspace enabled for Unity Catalog is designing a daily batch job that runs for about 10 minutes, has no strict start-time requirement, needs no custom init scripts or cluster-level Spark configuration, and must minimize operational overhead for the team.
Which compute option should the data engineer choose for running the job?

A. Attach the job to a classic all-purpose cluster with a 10-minute auto-termination timeout, so that the cluster shuts down shortly after each daily run.
B. Run the job on classic job compute that is created for each run and terminated afterwards, with instance types and Databricks Runtime versions selected by the team.
C. Run the job on an always-on classic all-purpose cluster, so that the job starts each day immediately without waiting for compute to start.
D. Run the job task on serverless compute, so that Databricks provisions, scales, and upgrades the compute, and the team does not manage clusters.

Answer: D

Explanation:
Databricks recommends serverless compute for most supported automated workloads because it minimizes infrastructure management and provides automatic resource provisioning and scaling.
The scenario describes a short daily batch job without specialized cluster requirements, making serverless compute an appropriate choice.
Databricks manages compute provisioning, runtime updates, and scaling, eliminating the need to manually select instance types or configure cluster lifecycle settings.
Because there is no strict start-time requirement, the engineer can prioritize reduced administrative overhead rather than maintaining idle resources for immediate execution.
Option B requires managing classic job compute configurations. Option C uses all-purpose compute, which is generally less suitable for automated production jobs. Option D introduces persistent compute costs by keeping a cluster running continuously.
Serverless compute offers the best operational fit for the stated requirements.

NEW QUESTION # 203
An organization processes customer data from web and mobile applications. Data includes names, emails, phone numbers, and location history. Data arrives both as batch files (from SFTP daily) and streaming JSON events (from Kafka in real-time).
To comply with data privacy policies, the following requirements must be met:
Personally Identifiable Information (PII) such as email, phone number, and IP address must be masked or anonymized before storage.
Both batch and streaming pipelines must apply consistent PII handling.
Masking logic must be auditable and reproducible.
The masked data must remain usable for downstream analytics.
How should the data engineer design a compliant data pipeline on Databricks that supports both batch and streaming modes, applies data masking to PII, and maintains traceability for audits?

A. Ingest both batch and streaming data using Lakeflow Declarative Pipelines, and apply masking via Unity Catalog column masks at read time to avoid modifying the data during ingestion.
B. Load batch data with notebooks and ingest streaming data with SQL Warehouses; use Unity Catalog column masks on Silver tables to redact fields after storage.
C. Use Lakeflow Declarative Pipelines for batch and streaming ingestion, define a PII masking function, and apply it during Bronze ingestion before writing to Delta Lake.
D. Allow PII to be stored unmasked in Bronze for lineage tracking, then apply masking logic in Gold tables used for reporting.

Answer: C

Explanation:
Databricks recommends applying data masking or anonymization before persisting PII to ensure compliance with privacy regulations such as GDPR and HIPAA. In a Lakeflow Declarative Pipeline, developers can define custom Python or SQL-based masking functions to standardize PII handling across both batch and streaming inputs.
This approach ensures that data entering the Delta Lake is already anonymized, guaranteeing consistent and auditable behavior. By applying masking during ingestion (in the Bronze layer), audit trails are preserved through pipeline event logs.
While Unity Catalog column masks (option C) can enforce dynamic masking at query time, they do not prevent PII storage. Thus, option D aligns with the best practice of securing PII before storage, while still supporting reproducibility and analytics usability.

NEW QUESTION # 204
Which of the following operations are not supported on a streaming dataset view?
spark.readStream.format("delta").table("sales").createOrReplaceTempView("streaming_view")

A. SELECT id, sum(unitssold) FROM streaming_view GROUP BY id ORDER BY id
B. SELECT sum(unitssold) FROM streaming_view
C. SELECT * FROM streadming_view ORDER BY id
D. SELECT max(unitssold) FROM streaming_view
E. SELECT id, count(*) FROM streaming_view GROUP BY id

Answer: C

Explanation:
Explanation
The answer isSELECT * FROM streadming_view order by id Please Note: Sorting with Group by will work without any issues see below explanation for each option of the options, Graphical user interface, text, application Description automatically generated

Certain operations are not allowed on streaming data, please see highlighted in bold.
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#unsupported-operations
* Multiple streaming aggregations (i.e. a chain of aggregations on a streaming DF) are not yet supported on streaming Datasets.
* Limit and take the first N rows are not supported on streaming Datasets.
* Distinct operations on streaming Datasets are not supported.
* Deduplication operation is not supported after aggregation on a streaming Datasets.
* Sorting operations are supported on streaming Datasets only after an aggregation and in Complete Output Mode.
Note: Sorting without aggregation function is not supported.
Here is the sample code to prove this,
Setup test stream
Graphical user interface, text, application, email Description automatically generated

Sum aggregation function has no issues on stream
Graphical user interface, application Description automatically generated

Max aggregation function has no issues on stream
Graphical user interface, application Description automatically generated

Group by with Order by has no issues on stream

Group by has no issues on stream

Table Description automatically generated
Order by without group by fails.
Graphical user interface, text, application Description automatically generated

NEW QUESTION # 205
A Delta Lake table with Change Data Feed (CDF) enabled in the Lakehouse named customer_churn_params 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. The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours. Which approach would simplify the identification of these changed records?

A. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
B. Modify the overwrite logic to include a field populated by calling current_timestamp() as data are being written; use this field to identify records written on a particular date.
C. Replace the current overwrite logic with a MERGE statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the Change Data Feed.
D. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "Change data feed (CDF) provides row-level change information for Delta tables." Exact extract: "Use table_changes to query the set of rows that were inserted, updated, or deleted between two versions (or timestamps)." Exact extract: "MERGE INTO updates and inserts only the rows that changed."

NEW QUESTION # 206
......

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