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

Databricks Certified Data Engineer Professional Sample Questions:

1. Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A) - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor
B) - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor
C) - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
D) - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
E) - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor

2. When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

A) CPU Utilization is around 75%
B) Total Disk Space remains constant
C) Bytes Received never exceeds 80 million bytes per second
D) Network I/O never spikes
E) The five Minute Load Average remains consistent/flat

3. 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) Deduplicate records in each batch by pk_id and overwrite the target table.
B) 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.
C) 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.
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.

4. Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table")
\
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?

A) The filter is executed only after the full data scan, preventing data skipping.
B) The event_date column is outside the table's partitioning and Z-ordering scheme.
C) The filter condition involves a data type excluded from data skipping support.
D) The Delta table lacks optimization that enables dynamic file pruning.

5. A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?

A) Run the notebook on a single node cluster to keep driver from falling.
B) Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
C) Rewrite their code to avoid putting memory pressure on the driver node.
D) Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.

Solutions: