Databricks-Certified-Data-Engineer-Associate Exam Training Practice Tests (Databricks Certified Data Engineer Associate)]

Question 1

A data engineer is standardizing repository layouts for multiple teams adopting Databricks Asset Bundles. The engineer wants to ensure every project has a single authoritative configuration file at the repository root that defines the bundle name, targets, workspace settings, permissions, and resource mappings (for jobs and pipelines).
Which strategy should the data engineer use to meet this goal?

A. Place multiple databricks.yml files under each subfolder (for example, jobs/, pipelines/, workspace/) and merge them at deploy time using the include mapping. B. Place a databricks.yml at the repository root and optional databricks.yml in subfolders; the CLI prefers .yaml over .yml when both exist. C. Place exactly one databricks.yml at the repository root; it is the main configuration file and may reference additional configuration files via the include mapping. D. Place a databricks.yml in a .databricks/ hidden folder at the repository root; only hidden locations are valid for bundle configs.

Correct Answer: C

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Question 2

Which of the following is stored in the Databricks customer's cloud account?

A. Cluster management metadata B. Databricks web application C. Notebooks D. Repos E. Data

Correct Answer: E

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Question 3

A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database. They run the following command:
CREATE TABLE jdbc_customer360
USING
OPTIONS (
url "jdbc:sqlite:/customers.db", dbtable "customer360"
)
Which line of code fills in the above blank to successfully complete the task?

A. sqlite B. autoloader C. org.apache.spark.sql.jdbc D. org.apache.spark.sql.sqlite

Correct Answer: C

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Question 4

An organization plans to share a large dataset stored in a Databricks workspace on AWS with a partner organization whose Databricks workspace is hosted on Azure. The data engineer wants to minimize data transfer costs while ensuring secure and efficient data sharing.
Which strategy will reduce data egress costs associated with cross-cloud data sharing?

A. Using Delta Sharing without any additional configurations B. Sharing data via pre-signed URLs without monitoring egress costs C. Configure VPN connection between AWS and Azure for faster data sharing D. Migrating the dataset to Cloudflare R2 object storage before sharing

Correct Answer: A

Question 5

A data engineer has been provided a PySpark DataFrame named df with columns product and revenue. The data engineer needs to compute complex aggregations to determine each product's total revenue, average revenue, and transaction count.
Which code snippet should the data engineer use?

A.

B.

C.

D.

Correct Answer: D

Question 6

A data engineering team ingests customer transaction data from three enterprise sources: a SQL database, Amazon S3, and an Apache Kafka stream. The data must maintain lineage and support compliance audits that require access to historical snapshots.
Which Lakeflow Connect configuration satisfies both the governance and audit requirements?

A. Use separate connectors with full-load synchronization only, archive historical data separately, and create external tables for audit queries. B. Use separate connectors for each source, source-appropriate incremental processing, individual Unity Catalog schemas, and retained Delta table versions for audit compliance. C. Use a single connector with batch scheduling, a shared Unity Catalog schema, and incremental snapshots stored externally. D. Use a single Lakeflow connector for all three sources, write directly to Delta tables, and disable table versioning to reduce storage costs.

Correct Answer: B

Question 7

A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing. B. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated. C. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused. D. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped. E. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.

Correct Answer: D

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Question 8

A data engineer is cleaning a Bronze table. The requirement is to eliminate rows where either the customer_email field or the customer_phone field is NULL. The cleaning must be performed in one operation using a single method call.
Which PySpark approach supports filtering multiple columns for NULL values in one call?

A. df.dropna(subset=["customer_email", "customer_phone"]) B. df.filter(col("customer_email").isNotNull() & col("customer_phone").isNotNull()) C. df.na.drop(how="all") D. df.where("customer_email IS NOT NULL").where("customer_phone IS NOT NULL")

Correct Answer: A

Question 9

A data engineering team needs to ingest historical CSV files from a cloud-storage location that already contains 50,000 existing files. The team also expects new files to arrive continuously. The team wants to use Auto Loader to incrementally process both the existing files and new arrivals efficiently.
Which Auto Loader mode should the team configure for this use case?

A. Use directory listing mode initially to process the 50,000 existing files, and then reconfigure the stream to use file notification mode for ongoing ingestion of new arrivals. B. Use file notification mode because it scales efficiently for both large numbers of existing files and continuous new arrivals by using cloud-storage events rather than repeatedly listing directories. C. Use directory listing mode because it provides better performance for the initial processing of 50,000 existing files through optimized incremental listing capabilities. D. Use file notification mode only after all existing files have been processed because it cannot efficiently handle large volumes of pre-existing files.

Correct Answer: B

Question 10

Which of the following data workloads will utilize a Gold table as its source?

A. A job that queries aggregated data designed to feed into a dashboard B. A job that cleans data by removing malformatted records C. A job that aggregates uncleaned data to create standard summary statistics D. A job that enriches data by parsing its timestamps into a human-readable format E. A job that ingests raw data from a streaming source into the Lakehouse

Correct Answer: A

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Question 11

Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

A. Parquet files can be partitioned B. CREATE TABLE AS SELECT statements cannot be used on files C. Parquet files have a well-defined schema D. Parquet files have the ability to be optimized E. Parquet files will become Delta tables

Correct Answer: C

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Question 12

A new data engineering team team. has been assigned to an ELT project. The new data engineering team will need full privileges on the database customers to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?

A. GRANT SELECT PRIVILEGES ON DATABASE customers TO teams; B. GRANT USAGE ON DATABASE customers TO team; C. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team; D. GRANT ALL PRIVILEGES ON DATABASE customers TO team; E. GRANT ALL PRIVILEGES ON DATABASE team TO customers;

Correct Answer: D

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Question 13

A data engineer needs to optimize the data layout and query performance for an e-commerce transactions Delta table. The table is partitioned by "purchase_date" a date column which helps with time-based queries but does not optimize searches on user statistics "customer_id", a high-cardinality column.
The table is usually queried with filters on "customer_i
d" within specific date ranges, but since this data is spread across multiple files in each partition, it results in full partition scans and increased runtime and costs.
How should the data engineer optimize the Data Layout for efficient reads?

A. Alter the table implementing liquid clustering by "customer_id" and "purchase_date". B. Enable delta caching on the cluster so that frequent reads are cached for performance. C. Alter the table to partition by "customer_id". D. Alter table implementing liquid clustering on "customerid" while keeping the existing partitioning.

Correct Answer: A

Question 14

A data engineer is setting up a new Databricks pipeline that ingests clickstream events from Kafka and daily product catalogs from cloud object storage. To ensure auditability and easy reprocessing, the engineer wants to land all source data first. Later stages will handle cleaning, deduplication, and business modeling before the data is used in dashboards.
Which approach aligns with Medallion Architecture principles?

A. Land both sources directly into the Silver layer with schema enforcement and deduplication to reduce downstream complexity B. Land both sources in Gold with denormalized star schemas to optimize BI while retaining full source fidelity C. Land both sources in the Bronze layer append-only with minimal validation, then build Silver/Gold downstream for quality and analytics D. Land streaming events from Kafka in Silver and the product catalog directly in Gold to minimize layers for batch data

Correct Answer: C

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Question 15

A governance team is evaluating whether to use Unity Catalog attribute-based access control policies or manually applied row filters and column masks to protect sensitive data across its catalog.
Why should the team use attribute-based access control policies instead of manually applied row filters and column masks?

A. Attribute-based access control policies allow row filters and column masks to be written directly in Python and Scala, whereas manually applied filters and masks can use only SQL user-defined functions. B. Attribute-based access control policies support applying multiple distinct row filters to the same table for one user at query time, whereas manually applied row filters are limited to one filter per table. C. Attribute-based access control policies use governed tags to dynamically match tables and columns, so one catalog-level policy can apply to matching current and future objects without per-table configuration. D. Attribute-based access control policies can be applied to views and materialized views, whereas manually applied row filters and column masks can only be applied to tables.

Correct Answer: C

Databricks Certified Data Engineer Associate - Databricks-Certified-Data-Engineer-Associate Exam Practice Test