Online DP-750 Questions Video

The correct answer is B — configure a checkpoint location.

A checkpoint is the Structured Streaming mechanism for fault tolerance. Databricks writes the committed offset (i.e., how far through the source stream the job has successfully read and processed) to a durable path in ADLS Gen2 or DBFS after each micro-batch. When the cluster restarts, the engine reads that offset and resumes from the next unprocessed record — nothing is reprocessed, nothing is skipped.

Option A (increase trigger interval) affects how frequently micro-batches run but does nothing to record progress between runs. Option C (watermark) handles late-arriving events in event-time windows but doesn ' t control source offset tracking. Option D (enable CDF on the target table) tracks changes made to a Delta table for downstream consumers — it has no bearing on the streaming job ' s own fault tolerance or offset management.

Checkpointing is a required configuration for any production streaming job. Without it, every cluster restart triggers a full replay from the source.

The correct answer is A — Yes.

df.filter(df.order_amount.isNotNull()) is the correct PySpark pattern for excluding null rows. The isNotNull() method is a Column method that returns True for every row where order_amount has a value and False for rows where it is null. Spark ' s filter keeps only the rows where the condition evaluates to True, producing a DataFrame with all null order_amount rows removed.

This works correctly because isNotNull() is explicitly null-aware — unlike the != None comparison in Q52, it doesn ' t rely on Python equality semantics. Under the hood it maps to the SQL expression order_amount IS NOT NULL, which is unambiguous in both SQL and Spark.

Both df.filter(df.order_amount.isNotNull()) and df.dropna(subset=[ ' order_amount ' ]) produce identical results. The choice between them is stylistic — isNotNull() reads more explicitly as a filter condition, while dropna is more compact when handling multiple columns.

A file arrival trigger starts Job1 when new files are detected in the monitored Azure Storage location. Because the job is launched only after a qualifying arrival, compute does not remain active while the source is idle. This is well suited to unpredictable file-delivery patterns and avoids the unnecessary executions produced by a fixed schedule. A continuous trigger keeps the workload running and therefore consumes compute even when no files are available. A scheduled trigger starts the job at predetermined times whether or not new data exists. A manual trigger cannot provide automatic processing. File arrival triggers consequently provide the required event-driven behavior while improving resource utilization and controlling cost during inactive periods. Microsoft Learn

The correct answer is C. Column masks are the right tool when you need to partially expose sensitive data rather than hide it entirely. A column mask is a SQL function attached to a column that rewrites the returned value based on who is querying. For email_address, the mask returns only the substring after ' @ ' . For credit_card_number, it returns only the last four digits. Business analysts get useful data without seeing anything sensitive, and they can still query the table without errors.

Option A (row-level filters) controls which rows a user sees, not the values within a row — it can ' t partially redact a column. Option B (grant SELECT only on non-sensitive columns) removes the columns entirely, so analysts can ' t see even partial email or card values — that doesn ' t meet the ' can see only partial values ' requirement. Option D (column-level encryption) requires key management infrastructure and decryption at query time, which is significantly more complex than column masking for this use case.