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

Databricks Certified Data Engineer Professional Sample Questions:

1. A large company seeks to implement a near real-time solution involving hundreds of pipelines with parallel updates of many tables with extremely high volume and high velocity data.
Which of the following solutions would you implement to achieve this requirement?

A) Use Databricks High Concurrency clusters, which leverage optimized cloud storage connections to maximize data throughput.
B) Isolate Delta Lake tables in their own storage containers to avoid API limits imposed by cloud vendors.
C) Partition ingestion tables by a small time duration to allow for many data files to be written in parallel.
D) Store all tables in a single database to ensure that the Databricks Catalyst Metastore can load balance overall throughput.
E) Configure Databricks to save all data to attached SSD volumes instead of object storage, increasing file I/O significantly.

2. An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For analytical purposes, only the most recent value for each record needs to be recorded in the target Delta Lake table in the Lakehouse. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A) Iterate through an ordered set of changes to the table, applying each in turn to create the current state of the table, (insert, update, delete), timestamp of change, and the values.
B) Deduplicate records in each batch by pk_id and overwrite the target table.
C) Use MERGE INTO to insert, update, or delete the most recent entry for each pk_id into a table, then propagate all changes throughout the system.
D) Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.

3. A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.

Which command should be removed from the notebook before scheduling it as a job?

A) Cmd 4
B) Cmd 5
C) Cmd 2
D) Cmd 6
E) Cmd 3

4. A data engineer is implementing Unity Catalog governance for a multi-team environment. Data scientists need interactive clusters for basic data exploration tasks, while automated ETL jobs require dedicated processing. How should the data engineer configure cluster isolation policies to enforce least privilege and ensure Unity Catalog compliance?

A) Configure all clusters with NO ISOLATION_SHARED access mode since Unity Catalog works with any cluster configuration.
B) Allow all users to create any cluster type and rely on manual configuration to enable Unity Catalog access modes.
C) Use only DEDICATED access mode for both interactive workloads and automated jobs to maximize security isolation.
D) Create compute policies with STANDARD access mode for interactive workloads and DEDICATED access mode for automated jobs.

5. A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A) Set the configuration delta.deduplicate = true.
B) Perform an insert-only merge with a matching condition on a unique key.
C) Perform a full outer join on a unique key and overwrite existing data.
D) Rely on Delta Lake schema enforcement to prevent duplicate records.
E) VACUUM the Delta table after each batch completes.

Solutions: