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Databricks Certified Machine Learning Professional · Databricks
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,B,C - Correct answer s : A, B, C — Choosing a checkpoint location so the streaming query can recover its progress and avoid reprocessing or dropping events after a restart.; Applying a watermark on the event-time column to bound how long the job retains state for late-arriving events.; Picking a trigger interval that matches how fresh the feature values need to be, such as a fixed processing time or `availableNow`. A . A checkpoint location lets Structured Streaming persist offsets and state, so the job can resume correctly after a restart instead of reprocessing or losing events. B . A watermark on event time bounds how long windowed aggregation state is retained, which is necessary to keep the job's memory and state store from growing without limit as late events trickle in. C . The trigger interval controls how often micro-batches run and therefore how fresh the written feature values are, so it should be chosen to match the freshness the downstream use case needs. D . `"merge"` is the only mode `write_table` supports for both batch and streaming input; there is no requirement or need to switch to `"overwrite"` for streaming sources. E . Change Data Feed is unrelated to whether the write side is streaming; it is a source-table property used for downstream incremental consumption and is not something streaming writes require disabling. F . Merge writes do not require single-partition output; forcing one partition would only hurt throughput and is not a documented requirement of `write_table`.