Exam Name: Databricks Certified Data Engineer Professional
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Modelling | - Scalable Data Models
|
| Data Transformation, Cleansing, and Quality | - Data Quality
|
| Debugging and Deploying | - Deploying CI/CD
|
| Developing Code for Data Processing using Python and SQL | - Using Python and Tools for Development
|
| Data Sharing and Federation | - Lakehouse Federation
|
| Ensuring Data Security and Compliance | - Compliance
|
| Cost & Performance Optimisation | - Cost Optimization
|
| Monitoring and Alerting | - Alerting
|
| Data Governance | - Metadata and Discoverability
|
| Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
Databricks Certified Data Engineer Professional Sample Questions:
A data engineer is creating a daily reporting job. There are two reporting notebooks--one for weekdays and one for weekends. An "if/else condition" task is configured as
{{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?
- A. Merge, as the job configuration looks good.
- B. Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.
- C. Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.
- D. Reject, as they should use {{job.trigger_time.is_weekday}} instead.
Correct Answer: C 🗳️
Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).
A data engineer is analyzing a large, partitioned retail dataset in Databricks, where each row represents a sale made by a salesperson. The dataset contains millions of records with the following schema:
sales_df: [salesperson_id: string, region: string, sale_amount: double, sale_date: date] The data engineer needs to generate a DataFrame that ranks salespeople within each region based on their total cumulative sales, with the highest seller ranked as 1. If multiple salespeople have the same total sales, they should share the same rank.
The data engineer wants to implement this logic using a PySpark window function and the dense_rank () function.
Which code snippet will perform this ranking?
- A.
- B.
- C.
- D.
Correct Answer: C 🗳️
Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).
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 Delta table lacks optimization that enables dynamic file pruning.
- C. The event_date column is outside the table's partitioning and Z-ordering scheme.
- D. The filter condition involves a data type excluded from data skipping support.
Correct Answer: C 🗳️
Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).
Which of the following is true of Delta Lake and the Lakehouse?
- A. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
- B. Z-order can only be applied to numeric values stored in Delta Lake tables
- C. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
- D. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
- E. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
Correct Answer: E 🗳️
Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).
Which statement describes Delta Lake Auto Compaction?
- A. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
- B. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
- C. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
- D. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
- E. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
Correct Answer: B 🗳️
Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).
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