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

Section	Weight	Objectives
Topic 1: Data Modeling and Storage	20%	- Data Modeling - File Formats - Storage Optimization
Topic 2: Data Quality and Governance	12%	- Data Quality - Governance - Data Lineage
Topic 3: Databricks Lakehouse Platform	24%	- Unity Catalog - Lakehouse Architecture - Data Management - Delta Lake
Topic 4: Monitoring and Troubleshooting	16%	- Performance Optimization - Troubleshooting - Monitoring
Topic 5: Data Processing	28%	- ETL Pipelines - Spark SQL - Data Transformation - Structured Streaming

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q153-Q158):

NEW QUESTION # 153
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. 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.
B. 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.
C. 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.
D. Allow PII to be stored unmasked in Bronze for lineage tracking, then apply masking logic in Gold tables used for reporting.

Answer: A

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 # 154
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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A. 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.
B. 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.
C. Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
D. Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
E. Reprocess all records in reviews_raw and overwrite the next table in the pipeline.

Answer: D

Explanation:
https://www.databricks.com/blog/2017/05/22/running-streaming-jobs-day-10x-cost-savings.html

NEW QUESTION # 155
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
B. Whenever a table is being created, make sure that the location keyword is used.
C. Whenever a database is being created, make sure that the location keyword is used
D. When the workspace is being configured, make sure that external cloud object storage has been mounted.
E. When tables are created, make sure that the external keyword is used in the create table statement.

Answer: B

Explanation:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table 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 by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it.

NEW QUESTION # 156
Which statement describes Delta Lake optimized writes?

A. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
B. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
C. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
D. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.

Answer: B

Explanation:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table.
The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.

NEW QUESTION # 157
A data engineer is designing a secure data sharing strategy for their organization. The company needs to share sensitive customer analytics data with two different partners. Partner A uses Databricks with Unity Catalog enabled, while Partner B uses Apache Spark on AWS without Databricks. How should the company implement secure data sharing for these scenarios?

A. Databricks-to-Databricks sharing (D2D) can only be used within the same cloud provider, so you must use open sharing (D2O) for any cross-cloud scenarios. Unit Catalog governance is not available when sharing with external platforms.
B. For Partner A, implement Databricks-to-Databricks sharing (D2D) with Unit Catalog integration and no-token exchange system. For Partner B, use open sharing protocol (D2O) with either bearer tokens or OIDC federation for authentication, ensuring both approaches maintain robust security and governance.
C. Both partners should use the same Delta Sharing approach since security requirements are identical. You should create bearer tokens for both partners and use the open sharing protocol (D2O) for maximum compatibility.
D. Open sharing protocol (D2O) should be used for both partners because it provides better security than D2D sharing. The bearer token approach is always more secure than Unity Catalog's native authentication.

Answer: B

Explanation:
Databricks-to-Databricks sharing with Unity Catalog provides the most seamless and secure option for Partner A by enabling native governance, fine-grained access controls, and a no-token exchange model. For Partner B, which does not use Databricks, the open sharing protocol enables secure access from external Spark environments using standard authentication mechanisms such as bearer tokens or OIDC federation, while still enforcing sharing policies and protecting sensitive data.

NEW QUESTION # 158
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