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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Governance | - Metadata and Discoverability
|
| Topic 2: Developing Code for Data Processing using Python and SQL | - Using Python and Tools for Development
|
| Topic 3: Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Topic 4: Data Sharing and Federation | - Delta Sharing
|
| Topic 5: Ensuring Data Security and Compliance | - Compliance
|
| Topic 6: Monitoring and Alerting | - Monitoring
|
| Topic 7: Data Transformation, Cleansing, and Quality | - Advanced Data Transformation
|
| Topic 8: Data Modelling | - Scalable Data Models
|
| Topic 9: Cost & Performance Optimisation | - Delta Optimization
|
| Topic 10: Debugging and Deploying | - Debugging and Troubleshooting
|
Databricks Certified Data Engineer Professional Sample Questions:
Question 1
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?
A. Whenever a table is being created, make sure that the location keyword is used.
B. Whenever a database is being created, make sure that the location keyword is used
C. When the workspace is being configured, make sure that external cloud object storage has been mounted.
D. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
E. When tables are created, make sure that the external keyword is used in the create table statement.
Question 2
A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high- cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time. Which command should the data engineer implement?
A. Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.
B. Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.
C. Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.
D. Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.
Question 3
A platform engineer needs to report the resource consumption, categorized by SKU tier, across all workspaces. The engineer decides to use the system.billing.usage system table to create a query. Which SQL query will accurately return the daily usage by product?
A. 
B. 
C. 
D. 
Question 4
The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer's suggestion is correct?
A. Text data cannot be stored with Delta Lake.
B. Delta Lake statistics are only collected on the first 4 columns in a table.
C. Delta Lake statistics are not optimized for free text fields with high cardinality.
D. ZORDER ON review will need to be run to see performance gains.
E. The Delta log creates a term matrix for free text fields to support selective filtering.
Question 5
When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?
A. In the Spark UI, under the SQL/DataFrame tab
B. In the Spart UI, under the Jobs tab
C. In the Query Profiler, under Query Source
D. In the Query Profiler, under the Stages tab
Solutions:
| Question 1 Answer: A | Question 2 Answer: B | Question 3 Answer: A | Question 4 Answer: C | Question 5 Answer: A |
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