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Databricks Certified Data Engineer Professional 認定 Databricks-Certified-Data-Engineer-Professional 試験問題:

1. A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A) delayWrite("event_time", "10 minutes")
B) withWatermark("event_time", "10 minutes")
C) slidingWindow("event_time", "10 minutes")
D) await("event_time + `10 minutes'")
E) awaitArrival("event_time", "10 minutes")

2. The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A) No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
B) No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
C) Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
D) Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
E) No; the Delta cache may return records from previous versions of the table until the cluster is restarted.

3. A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

A) New fields cannot be added to a production Delta table.
B) Updating the table schema will invalidate the Delta transaction log metadata.
C) New fields not be computed for historic records.
D) Updating the table schema requires a default value provided for each file added.
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E) Spark cannot capture the topic partition fields from the kafka source.

4. In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone. A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?

A) The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.
B) The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command
C) Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.
D) Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
E) Tables created with SHALLOW CLONE are automatically deleted after their default retention threshold of 7 days.

5. A data engineer wants to reflector the following DLT code, which includes multiple definition with very similar code:

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for tables.
How can the data engineer fix this?

A) Convert the list of configuration values to a dictionary of table settings, using table names as keys.
B) Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
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C) Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
D) Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table

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