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| Section | Objectives |
|---|---|
| Topic 1: Databricks Lakehouse Platform Architecture | - Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics) |
| Topic 2: Data Modeling and Transformation | - Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts |
| Topic 3: Delta Lake and Data Management | - Delta Lake transactions and ACID properties - Time travel and versioning - Schema evolution and enforcement |
| Topic 4: Production Pipelines and Orchestration | - Job scheduling and monitoring - Databricks Workflows - Error handling and recovery strategies |
| Topic 5: Data Ingestion and Processing | - ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals |
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NEW QUESTION # 188
A data engineer is designing a pipeline in Databricks that processes records from a Kafka stream where late-arriving data is common. Which approach should the data engineer use?
Answer: C
Explanation:
In Structured Streaming, event-time watermarks control how long the engine waits for late- arriving data before finalizing aggregations. By setting an appropriate watermark, Databricks can handle late data gracefully -- incorporating records that arrive within the defined window while discarding excessively delayed events.
This approach ensures accurate aggregations, minimizes state size, and prevents memory leaks.
Manual reprocessing (A) or overwriting entire datasets (B) is inefficient and costly, while Auto CDC (C) is used for change tracking in Delta tables, not for streaming event lateness.
Thus, using watermarking is the recommended and official approach for managing late data in streaming pipelines.
NEW QUESTION # 189
A data engineer has a Delta table orders with deletion vectors enabled. The engineer executes the following command:
DELETE FROM orders WHERE status = 'cancelled';
What should be the behavior of deletion vectors when the command is executed?
Answer: D
Explanation:
Deletion vectors (DVs) in Delta Lake optimize delete operations by marking deleted rows logically in metadata rather than rewriting Parquet files. When a DELETE statement is executed, affected rows are tracked by DVs in the transaction log. The data remains in the underlying files but is filtered out during query reads. This improves performance for frequent deletes and updates since file rewrites are deferred. Physical data removal only occurs when a VACUUM command is later executed. The Databricks documentation confirms: "With deletion vectors, deleted rows are marked in metadata and skipped at read time, avoiding file rewrites." Thus, rows are marked as deleted in metadata--not in files.
NEW QUESTION # 190
A data engineer is analyzing transactional data in a PySpark DataFrame df containing customer_id, transaction_timestamp (precise to milliseconds), and amount_spent. The objective is to compute a cumulative sum of amount_spent per customer, strictly ordered by transaction_timestamp. The cumulative sum must include all transactions from the earliest timestamp up to and including the current row, respecting temporal ordering within each customer partition. Which PySpark code snippet most accurately constructs the appropriate window specification and applies the aggregation to yield the correct cumulative expenditure per customer?
Answer: B
Explanation:
This window specification partitions the data by customer_id, orders transactions by transaction_timestamp, and defines the frame from the first transaction through the current one.
This guarantees that the cumulative sum is computed independently per customer and strictly follows the temporal order, including all prior transactions up to the current row.
NEW QUESTION # 191
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?
Answer: E
Explanation:
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum
NEW QUESTION # 192
A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.
Original query:
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Proposed query:
Proposed query:
.start("/item_agg")
Which step must also be completed to put the proposed query into production?
Answer: C
Explanation:
When introducing a new aggregation or a change in the logic of a Structured Streaming query, it is generally necessary to specify a new checkpoint location. This is because the checkpoint directory contains metadata about the offsets and the state of the aggregations of a streaming query. If the logic of the query changes, such as including a new aggregation field, the state information saved in the current checkpoint would not be compatible with the new logic, potentially leading to incorrect results or failures. Therefore, to accommodate the new field and ensure the streaming job has the correct starting point and state information for aggregations, a new checkpoint location should be specified.
NEW QUESTION # 193
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