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 | Objectives |
|---|---|
| Databricks Lakehouse Platform Architecture | - Workspace and cluster architecture - Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold) |
| Delta Lake and Data Management | - Time travel and versioning - Delta Lake transactions and ACID properties - Schema evolution and enforcement |
| Data Ingestion and Processing | - ETL pipeline design patterns - Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader |
| Data Modeling and Transformation | - Dimensional modeling concepts - Spark SQL transformations - Performance optimization techniques |
| Production Pipelines and Orchestration | - Error handling and recovery strategies - Job scheduling and monitoring - Databricks Workflows |
Databricks Certified Data Engineer Professional Sample Questions:
A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?
- A. Network I/O never spikes
- B. The five Minute Load Average remains consistent/flat
- C. Bytes Received never exceeds 80 million bytes per second
- D. Total Disk Space remains constant
- E. Overall cluster CPU utilization is around 25%
Correct Answer: E 🗳️
Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).
A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team shares large Delta tables internally via Databricks-to-Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types. What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?
- A. With Open Sharing, recipients cannot access Volumes, Models, or notebooks -- only static Delta tables are supported.
- B. Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.
- C. Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.
- D. With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.
Correct Answer: A 🗳️
Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).
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.apply(model, columns).select("customer_id, predictions")
- B. df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
- C. df.map(lambda x:model(x[columns])).select("customer_id, predictions")
- D. df.select("customer_id", model(*columns).alias("predictions"))
- E. model.predict(df, columns)
Correct Answer: D 🗳️
Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?
- A. The Delta log is scanned for min and max statistics for the latitude column
- B. The Hive metastore is scanned for min and max statistics for the latitude column
- C. All records are cached to an operational database and then the filter is applied
- D. All records are cached to attached storage and then the filter is applied
- E. The Parquet file footers are scanned for min and max statistics for the latitude column
Correct Answer: A 🗳️
Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).
How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?
- A. Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.
- B. Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.
- C. Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.
- D. Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.
Correct Answer: B 🗳️
Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).
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