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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Monitoring and Alerting | - Alerting
|
| Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Cost & Performance Optimization | - Optimize cost and performance
|
| Data Transformation, Cleansing, and Quality | - Transform and validate data
|
| Data Modeling | - Design and optimize data models
|
| Data Sharing and Federation | - Share and federate data
|
| Debugging and Deploying | - Deploying CI/CD
|
| Ensuring Data Security and Compliance | - Applying Data Security Mechanisms
|
| Data Governance | - Govern enterprise data
|
| Developing Code for Data Processing using Python and SQL | - Using Python and Tools for Development
|
Databricks Certified Data Engineer Professional Sample Questions:
1. A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team. The analytics team wants to use the table with another tool that requires Apache Iceberg format. What should the data engineer do?
A) Require the analytics team to use a tool that supports Delta table.
B) Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table.
C) Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table.
D) Create an Iceberg copy of the transactions Delta table which can be used by the analytics team.
2. 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 Query Profiler, under Query Source
B) In the Spart UI, under the Jobs tab
C) In the Query Profiler, under the Stages tab
D) In the Spark UI, under the SQL/DataFrame tab
3. A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill. The compute instance being used has a core-to-memory ratio of
1:2. What are the two steps the data engineer should take to minimize spillage? (Choose two.)
A) Increase spark.sql.files.maxPartitionBytes.
B) Choose a compute instance with a higher core-to-memory ratio.
C) Reduce spark.sql.files.maxPartitionBytes.
D) Choose a compute instance with more network bandwidth.
E) Choose a compute instance with more disk space.
4. A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers, one driver of type i3.xlarge, and should use the '14.3.x- scala2.12' runtime. Which command should the data engineer use?
A) databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name Data Engineer_cluster
B) databricks clusters create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name DataEngineer_cluster
C) databricks clusters add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
D) databricks compute add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
5. A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.
Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?
A) Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.
B) Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
C) Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
D) Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to maximum allowable threshold should minimize this cost.
E) Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: B,C | Question # 4 Answer: B | Question # 5 Answer: D |
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