Databricks Certified Data Engineer Associate Practice Questions: Incremental Data Processing Domain

Correct Answer: A

Explanation: Using a unique identifier for each record and performing upserts ensures idempotency, as it allows the system to update existing records without duplication. Reprocessing the entire dataset (B) is inefficient and not suitable for incremental processing. Deleting existing data (C) can lead to data loss and is not idempotent. Relying on the default behavior (D) does not guarantee idempotency as it may not handle duplicates correctly.

Correct Answer: A

Explanation: The 'foreachBatch' operation (A) allows you to apply custom logic to each micro-batch of data in a streaming query. It is not used for aggregation (B), schema definition (C), or filtering null values (D).

Correct Answer: C

Explanation: Option C is correct because Delta Lake's Change Data Feed (CDF) allows you to efficiently capture and process only the incremental changes to a dataset, eliminating the need for full table scans. Option A is incorrect because full table scans are inefficient for large datasets. Option B is incorrect because time travel is primarily used for accessing historical data, not for capturing new records. Option D is incorrect because manually tracking changes is error-prone and less efficient compared to using Delta Lake's built-in features.

Correct Answer: C

Explanation: The correct answer is C. Delta Lake's Change Data Feed (CDF) allows you to track and process changes to data over time, which is crucial for incremental processing. A (reducing storage costs) is not specific to CDF. B (schema enforcement) and D (automating data cleaning) are features of Delta Lake but not directly related to CDF.

Correct Answer: B

Explanation: Trigger-based processing in Databricks structured streaming is used when you want to process data in fixed-size batches at regular intervals, providing a balance between real-time processing and resource management. Processing data as soon as it arrives (A) is more typical of continuous processing. Processing data only once at the end of the day (C) is more akin to batch processing, not streaming. Micro-batch processing with low latency (D) is a feature of structured streaming, but trigger-based processing specifically refers to scheduled batch intervals.

Correct Answer: B

Explanation: Structured Streaming in Apache Spark is designed for low-latency, continuous data processing and provides built-in fault tolerance by leveraging Spark's underlying capabilities. This makes it suitable for real-time analytics and applications requiring near real-time insights. Option A is incorrect because Structured Streaming supports both batch and streaming data. Option C is incorrect as Structured Streaming handles late data with watermarking. Option D is incorrect because it supports stateful operations, such as maintaining state across streaming batches.

Correct Answer: B

Explanation: The 'Append' output mode (B) writes only the new rows since the last trigger, making it suitable for scenarios where only new data should be captured. 'Complete' (A) writes all rows, 'Update' (C) writes both new and changed rows, and 'Overwrite' (D) replaces the entire output.

Correct Answer: B

Explanation: Delta Lake provides ACID transactions, which are crucial for ensuring data reliability and consistency during incremental data processing. This is particularly important when dealing with concurrent write operations and updates. While Delta Lake supports real-time data processing (A), the primary benefit in the context of incremental processing is ACID compliance. Delta Lake does not automatically scale compute resources (C); this is managed by Databricks clusters. Schema evolution (D) is supported but not eliminated by Delta Lake.

Correct Answer: B

Explanation: Watermarking in Structured Streaming is used to manage state and control the retention of old data by specifying how late data can arrive and still be processed. This helps in managing the state size and ensuring timely processing. Option A is incorrect because watermarking does not directly improve query performance by skipping data; it manages data retention. Option C is incorrect because watermarking is not used for partitioning data. Option D is incorrect because watermarking does not handle resource scaling.

Correct Answer: B

Explanation: Option B is correct because using a watermark with an appropriate delay allows the system to wait for late-arriving data within a specified time window, ensuring it is processed correctly. Option A is incorrect as the trigger interval affects how often the query is processed, not how late data is handled. Option C is incorrect because increasing the cluster size improves performance but does not directly address late-arriving data. Option D is incorrect because using a non-Delta format would not leverage Delta Lake's capabilities for handling late-arriving data.