Exam Name: Databricks Certified Data Engineer Professional Exam
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality and Governance | 12% | - Governance - Data Lineage - Data Quality |
| Topic 2: Databricks Lakehouse Platform | 24% | - Lakehouse Architecture - Unity Catalog - Data Management - Delta Lake |
| Topic 3: Data Processing | 28% | - Data Transformation - Structured Streaming - Spark SQL - ETL Pipelines |
| Topic 4: Data Modeling and Storage | 20% | - File Formats - Data Modeling - Storage Optimization |
| Topic 5: Monitoring and Troubleshooting | 16% | - Troubleshooting - Monitoring - Performance Optimization |
Databricks Certified Data Engineer Professional Sample Questions:
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. 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.
- 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. 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.
- D. 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.
- E. Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.
Correct Answer: C 🗳️
The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?
- A. On Heap Memory Usage is within 75% of off Heap Memory usage
- B. Size on Disk is> 0
- C. Size on Disk is < Size in Memory
- D. The number of Cached Partitions> the number of Spark Partitions
- E. The RDD Block Name included the '' annotation signaling failure to cache
Correct Answer: B 🗳️
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A data engineer is building a customer data pipeline in Lakeflow Spark Declarative Pipelines. The source is a cloud-based event stream with limited retention containing inserts, updates, and deletes for customer records. These changes are being applied using the AUTO CDC INTO syntax to maintain an SCD Type 1 table as the target table, customer_dim. How should the data engineer build a downstream job that streams from the customer_dim table to only act on updates and delete events, processing data incrementally?
- A. Streaming from customer_dim table would only be possible in the case of SCD 2 retention.
- B. Use ignoreChanges flag while streaming from customer_dim to avoid breaking the pipeline during updates and deletes.
- C. When stored as SCD 1, the target of AUTO CDC INTO includes updates and deletes. Streaming from customer_dim can fail due to these operations. Instead, build another stream from the original source.
- D. Read change data feed from customer_dim table and apply filters to incrementally act on the change events.
Correct Answer: D 🗳️
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A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:
An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?
- A. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
- B. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
- C. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
- D. All records from all columns will be displayed with the values in user_ltv.
- E. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
Correct Answer: B 🗳️
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A data engineering team is setting up a Git project to automate integration tests using Databricks Asset Bundles and the Git provider's CI/CD functionalities. When a pull containing changes to their pipleline is sent, they need to run a Job to test their data pipeline. What is the correct databricks bundle command sequence to be executed from the Git provider's CI/CD automation for this task?
- A. deploy, run, validate
- B. validate, deploy, run
- C. init, validate, deploy, run
- D. init, deploy, run, validate
Correct Answer: B 🗳️
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