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| Section | Objectives |
|---|---|
| Delta Lake and Data Management | - Time travel and versioning - Delta Lake transactions and ACID properties - Schema evolution and enforcement |
| Data Modeling and Transformation | - Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts |
| Data Ingestion and Processing | - ETL pipeline design patterns - Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader |
| Databricks Lakehouse Platform Architecture | - Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture - Data governance concepts (Unity Catalog basics) |
| Production Pipelines and Orchestration | - Error handling and recovery strategies - Databricks Workflows - Job scheduling and monitoring |
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NEW QUESTION # 33
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?
Answer: B
Explanation:
https://docs.databricks.com/en/archive/dev-tools/cli/dbfs-cli.html
NEW QUESTION # 34
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?
Answer: D
Explanation:
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.
In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file:
- Total number of records
- Minimum value in each column of the first 32 columns of the table
- Maximum value in each column of the first 32 columns of the table
- Null value counts for in each column of the first 32 columns of the table When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter.
For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column.
NEW QUESTION # 35
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?
Answer: A
Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade- off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job.
NEW QUESTION # 36
A data engineer needs to productionize a new Spark application written by teammate. This application has numerous external dependencies, including libraries, and requires custom environment variables and Spark configuration parameters to be set. Which two methods will help the data engineer accomplish the task? (Choose two.)
Answer: D,E
Explanation:
Compute policies allow centrally defining and enforcing Spark configuration parameters, system properties, and environment variables required by the application, ensuring consistent production settings. Init scripts enable installing external dependencies and performing custom environment setup at cluster startup, making them essential for productionizing Spark applications with complex dependency and configuration requirements.
NEW QUESTION # 37
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device. Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot. How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?
Answer: B
Explanation:
In Databricks Lakehouse, to retain manually deleted or updated records in the raw_iot table while recomputing downstream tables when a pipeline update is run, the property pipelines.reset.allowed should be set to false. This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to the raw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.
NEW QUESTION # 38
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