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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineering team is implementing an append-only data pipeline using Delta Lake, and wants to ensure that data is never modified or deleted once written. Which Delta Lake feature should the data engineer enable to prevent modifications to existing data?

A) Delta OPTIMIZE
B) Delta VACUUM
C) Delta APPEND_ONLY
D) Delta Time Travel

2. A data engineer wants to refactor the following DLT code, which includes multiple table definitions 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 these tables.
How can the data engineer fix this?

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

3. Which method can be used to determine the total wall-clock time it took to execute a query?

A) In the Spark UI, take the job duration of the longest-running job associated with that query.
B) Open the Query Profiler associated with that query and use the Aggregated task time metric.
C) In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
D) Open the Query Profiler associated with that query and use the Total wall-clock duration metric.

4. A data engineer is building a customer data pipeline in Lakeflow Spark Declarative Pipelines. The source is a cloud-based event stream with limited retention containing inserts, updates, and deletes for customer records. These changes are being applied using the AUTO CDC INTO syntax to maintain an SCD Type 1 table as the target table, customer_dim. How should the data engineer build a downstream job that streams from the customer_dim table to only act on updates and delete events, processing data incrementally?

A) Use ignoreChanges flag while streaming from customer_dim to avoid breaking the pipeline during updates and deletes.
B) Read change data feed from customer_dim table and apply filters to incrementally act on the change events.
C) When stored as SCD 1, the target of AUTO CDC INTO includes updates and deletes. Streaming from customer_dim can fail due to these operations. Instead, build another stream from the original source.
D) Streaming from customer_dim table would only be possible in the case of SCD 2 retention.

5. A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT,
latitude FLOAT, post_time TIMESTAMP, date DATE
This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A) No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
B) Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
C) Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
D) The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
E) The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.

Solutions: