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 |
|---|---|---|
| Data Processing | 28% | - Structured Streaming - Data Transformation - ETL Pipelines - Spark SQL |
| Databricks Lakehouse Platform | 24% | - Delta Lake - Data Management - Unity Catalog - Lakehouse Architecture |
| Data Modeling and Storage | 20% | - Storage Optimization - Data Modeling - File Formats |
| Monitoring and Troubleshooting | 16% | - Performance Optimization - Monitoring - Troubleshooting |
| Data Quality and Governance | 12% | - Data Lineage - Data Quality - Governance |
Databricks Certified Data Engineer Professional Sample Questions:
Which Python variable contains a list of directories to be searched when trying to locate required modules?
- A. os.path
- B. pypi.path
- C. pylib.source
- D. sys.path
- E. importlib.resource path
Correct Answer: D 🗳️
Explanation: Only visible for NewPassLeader members. You can sign-up / login (it's free).
A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context.
Assuming the job is already deployed, they need to trigger its execution while ensuring the target- specific configuration is respected. Which command will trigger the job execution?
- A. databricks execute my_project_job -e prod
- B. databricks run my_project_job -t prod
- C. databricks bundle run my_project_job -t prod
- D. databricks job run my_project_job --env prod
Correct Answer: C 🗳️
Explanation: Only visible for NewPassLeader members. You can sign-up / login (it's free).
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:
Which statement describes the execution and results of running the above query multiple times?
- A. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
- B. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
- C. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
- D. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
- E. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
Correct Answer: B 🗳️
Explanation: Only visible for NewPassLeader members. You can sign-up / login (it's free).
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".
The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?
- A.
- B.
- C.
- D. preds.write.mode("append").saveAsTable("churn_preds")
- E. preds.write.format("delta").save("/preds/churn_preds")
Correct Answer: D 🗳️
The data science team has created and logged a production model using MLflow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model.
Which code block will output a DataFrame with the schema "customer_id LONG, predictions DOUBLE"?
- A. df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
- B. df.apply(model, columns).select("customer_id, predictions")
- C. df.map(lambda x:model(x[columns])).select("customer_id, predictions")
- D. model.predict(df, columns)
- E. df.select("customer_id", model(*columns).alias("predictions"))
Correct Answer: E 🗳️
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