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NEW QUESTION # 87
Which of the following SQL command can be used to insert or update or delete rows based on a condition to check if a row(s) exists?
Answer: D
Explanation:
Explanation
here is the additional documentation for your review.
https://docs.databricks.com/spark/latest/spark-sql/language-manual/delta-merge-into.html
1.MERGE INTO targettablename [targetalias]
2. USING sourcetablereference [sourcealias]
3. ON mergecondition
4. [ WHEN MATCHED [ AND condition ] THEN matchedaction ] [...]
5. [ WHEN NOT MATCHED [ AND condition ] THEN notmatchedaction ] [...]
6.
7.matchedaction
8. { DELETE |
9. UPDATE SET * |
10. UPDATE SET { column1 = value1 } [, ...] }
11.
12.notmatchedaction
13. { INSERT * |
14. INSERT (column1 [, ...] ) VALUES (value1 [, ...])
NEW QUESTION # 88
A dataset has been defined using Delta Live Tables and includes an expectations clause: CON-STRAINT validtimestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL What is the expected behavior when a batch of data containing data that violates these constraints is processed?
Answer: E
Explanation:
Explanation
The answer is Records that violate the expectation cause the job to fail.
Delta live tables support three types of expectations to fix bad data in DLT pipelines Review below example code to examine these expectations, Diagram Description automatically generated with medium confidence
Invalid records:
Use the expect operator when you want to keep records that violate the expectation. Records that violate the expectation are added to the target dataset along with valid records:
SQL
CONSTRAINT validtimestamp EXPECT (timestamp > '2020-01-01')
Drop invalid records:
Use the expect or drop operator to prevent the processing of invalid records. Records that violate the expectation are dropped from the target dataset:
SQL
CONSTRAINT validtimestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION DROP ROW Fail on invalid records:
When invalid records are unacceptable, use the expect or fail operator to halt execution immediately when a record fails validation. If the operation is a table update, the system atomically rolls back the transaction:
SQL
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UP-DATE
NEW QUESTION # 89
Which of the following techniques structured streaming uses to create an end-to-end fault toler-ance?
Answer: B
Explanation:
Explanation
The answer is Checkpointing and idempotent sinks
How does structured streaming achieves end to end fault tolerance:
First, Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval.
Next, the streaming sinks are designed to be idempotent-that is, multiple writes of the same data (as identified by the offset) do not result in duplicates being written to the sink.
Taken together, replayable data sources and idempotent sinks allow Structured Streaming to en-sure end-to-end, exactly-once semantics under any failure condition.
NEW QUESTION # 90
When investigating a data issue you realized that a process accidentally updated the table, you want to query the same table with yesterday's version of the data so you can review what the prior version looks like, what is the best way to query historical data so you can do your analysis?
Answer: C
Explanation:
Explanation
The answer is SELECT * FROM tablename TIMESTAMP as of datesub(currentdate(), 1) FYI, Time travel supports two ways one is using timestamp and the second way is using version number, Timestamp:
1.SELECT count(*) FROM mytable TIMESTAMP AS OF "2019-01-01"
2.SELECT count() FROM mytable TIMESTAMP AS OF datesub(current_date(), 1)
3.SELECT count() FROM mytable TIMESTAMP AS OF "2019-01-01 01:30:00.000" Version Number:
1.SELECT count(*) FROM mytable VERSION AS OF 5238
2.SELECT count() FROM [email protected]
3.SELECT count() FROM delta./path/to/my/[[email protected]](/cdn-cgi/l/email-protection)
https://databricks.com/blog/2019/02/04/introducing-delta-time-travel-for-large-scale-data-lakes.html
NEW QUESTION # 91
Which of the following techniques structured streaming uses to ensure recovery of failures during stream processing?
Answer: B
Explanation:
Explanation
The answer is Checkpointing and write-ahead logging.
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval.
NEW QUESTION # 92
......
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