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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q187-Q192):

NEW QUESTION # 187
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
B. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
C. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
D. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
E. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.

Answer: D

Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

NEW QUESTION # 188
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
B. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
C. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
D. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
E. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1

Answer: E

Explanation:
Explanation
This is the best configuration for scheduling Structured Streaming jobs for production, as it automatically recovers from query failures and keeps costs low. A new job cluster is created for each run of the job and terminated when the job completes, which saves costs and avoids resource contention. Retries are not needed for Structured Streaming jobs, as they can automatically recover from failures using checkpointing and write-ahead logs. Maximum concurrent runs should be set to 1 to avoid duplicate output or data loss. Verified References: Databricks Certified Data Engineer Professional, under "Monitoring & Logging" section; Databricks Documentation, under "Schedule streaming jobs" section.

NEW QUESTION # 189
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
B. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
C. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
D. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
E. The write will fail completely because of the constraint violation and no records will be inserted into the target table.

Answer: E

Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. References:
* Constraints : https://docs.delta.io/latest/delta-constraints.html
* ACID Transactions : https://docs.delta.io/latest/delta-intro.html#acid-transactions

NEW QUESTION # 190
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?

A. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
B. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
C. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
D. Databricks has autotuned to a smaller target file size based on the overall size of data in the table

Answer: C

Explanation:
Explanation
This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade-off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section.

NEW QUESTION # 191
Newly joined data analyst requested read-only access to tables, assuming you are owner/admin which section of Databricks platform is going to facilitate granting select access to the user

A. Azure RBAC
B. User settings
C. Azure Databricks control pane IAM
D. Data explorer
E. Admin console

Answer: D

Explanation:
Explanation
Anser is Data Explorer
https://docs.databricks.com/sql/user/data/index.html
Data explorer lets you easily explore and manage permissions on databases and tables. Users can view schema details, preview sample data, and see table details and properties. Administrators can view and change owners, and admins and data object owners can grant and revoke permissions.
To open data explorer, click Data in the sidebar.

NEW QUESTION # 192
......

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