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For DP-750, the key is knowing what each Delta Lake operation changes: INSERT or append adds rows, UPDATE and DELETE change the current logical table, MERGE applies matched and unmatched changes, and VACUUM removes eligible old files. History and time travel help inspect earlier table versions, while schema rules and maintenance settings determine what writes and reads are possible.
What should you know about Delta Lake tables first?
A Delta table is read and changed through a table interface, with operations recorded as successive table versions. A modifying operation creates a new version; that version history supports reviewing operations and, while the necessary data files remain available, querying an earlier snapshot.
Platform details matter. Azure Databricks documentation describes Delta Lake as the storage layer underlying Databricks tables unless otherwise specified. That platform-specific default should not be assumed for every engine or storage setup.
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| Operation | What it does | When it fits |
|---|---|---|
| INSERT or append | Adds rows to the table. | Use when incoming records should be added and do not need match-based updates. |
| UPDATE | Changes rows that match a condition. | Use when existing rows need changes and the target rows can be identified. |
| DELETE | Removes matching rows from the latest logical table state. | Use when rows should no longer appear in the current snapshot. |
| MERGE | Applies actions based on whether source rows match target rows; it can update matched rows and insert unmatched rows. | Use for upserts or change workflows where incoming records may be new or may replace existing values. |
INSERT or append: add without matching
Append is appropriate when each incoming row is meant to be added as a new row. It does not, by itself, check whether a key already exists in the target. If the batch can contain records that are already present, use logic that accounts for that possibility rather than treating append as an upsert.
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UPDATE and DELETE: target existing rows
UPDATE changes rows selected by a condition; DELETE removes selected rows from the current logical snapshot. DELETE is not the same as immediately erasing every underlying data file. Those files can remain until eligible for later cleanup.
MERGE: apply matched updates and unmatched inserts
MERGE compares a source with a target using a matching condition. For example, a customer batch with a suitable customer key can update target rows for keys already present and insert rows for keys not yet present. This is the usual decision when one load contains both changed records and new records.
Before merging, ensure the source is deduplicated or otherwise constrained so that multiple source rows do not try to make conflicting changes to the same target row. The matching key and source quality are part of the correctness of the operation, not details MERGE can guess.
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Schema enforcement checks writes against the table’s schema. A write that does not fit the expected schema can be rejected rather than silently changing the table.
Schema evolution is a controlled way for supported writes to accommodate schema changes, such as incoming columns. It is not synonymous with enforcement and should not be assumed to happen for every write. You can make schema intent explicit with an operation such as ALTER TABLE, or use automatic evolution where the operation and version support it.
| Choice | What it emphasizes | Decision point |
|---|---|---|
| Explicit schema change | Declares the intended table change directly. | Prefer when a schema change should be deliberate and visible before the data write. |
| Automatic schema evolution | Allows supported operations to adapt the schema as part of writing. | Use only after confirming the operation-specific syntax and behavior for the Delta Lake or runtime version in use. |
Merge behavior for columns missing on one side, as well as the syntax for enabling evolution, depends on the operation and software version. For a DP-750 scenario, identify whether the question asks for a schema change or a row-level change, then check the applicable version-specific behavior instead of treating one evolution option as universal.
What do history and time travel let you do?
History lets you review table-version and operation metadata. Time travel lets you query a prior retained snapshot. They answer different questions: history helps establish what happened, while time travel reads what the table looked like at an earlier version or point in time.
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In Microsoft’s Azure Databricks documentation, Databricks Runtime 18.0 and later blocks time-travel queries that request a version older than the table’s deletedFileRetentionDuration. That documentation gives seven days as the default for this property. Treat this as a version- and platform-scoped behavior, not a universal Delta Lake retention guarantee; check the table’s settings and the runtime documentation relevant to your environment.
When should you use OPTIMIZE, clustering, or VACUUM?
OPTIMIZE: compact small files
Many small files can make reads less efficient. On Databricks, OPTIMIZE compacts files to improve read performance. Databricks also documents liquid clustering as a table-layout option, along with OPTIMIZE FULL for applying clustering to existing data. The right layout depends on workload, table characteristics, and platform support.
For eligible Unity Catalog managed tables with predictive optimization enabled, Databricks says predictive optimization can manage maintenance, so manual optimization may not be necessary. This qualification does not apply to every table or configuration.
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VACUUM: remove eligible obsolete files
VACUUM performs physical cleanup of eligible data files that are no longer needed for the retained table state. It is distinct from DELETE: DELETE changes which rows belong to the current logical snapshot; VACUUM removes obsolete files from storage according to retention rules.
That distinction creates a practical trade-off. Retaining old files supports historical access but uses storage; cleaning them up can reduce that access. Confirm historical-retention needs and the applicable safeguards before scheduling cleanup. Do not casually shorten retention or bypass safeguards to force removal.
| Need | Operation to consider | Important distinction |
|---|---|---|
| Improve file layout or reduce small-file overhead | OPTIMIZE; consider clustering where supported and suitable. | Layout maintenance is different from changing which rows are in the table. |
| Free space held by eligible obsolete data files | VACUUM, subject to retention rules. | Physical cleanup can affect the ability to query older snapshots. |
Which DP-750 topics should you practice?
Microsoft’s DP-750 study guide includes loading with merge, insert, and append; schema enforcement and schema drift; history tables; clustering strategy; and optimization with OPTIMIZE and VACUUM. The guide is the place to verify current exam scope. A listed skill is a study topic, not a promise about how often it appears on an exam.
Practice question 1: updates and new keys in one batch
Question: A batch contains changed customer records and previously unseen customer IDs. Which operation can update matched target rows and insert unmatched rows?
Answer: MERGE, using an appropriate matching key. The source should not contain conflicting duplicate rows for the same target key.
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Practice question 2: old snapshot unavailable after cleanup
Question: A table’s operation history is visible, but a query for an older snapshot no longer works after file cleanup. Why can these facts coexist?
Answer: History metadata and the old snapshot’s data files have different retention needs. A history record does not ensure that the data files required for time travel are still retained.
Practice question 3: incoming column does not fit the table
Question: A write includes a column not present in the target schema. Should the writer assume the table will add it automatically?
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