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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is running a groupBy aggregation on a massive user activity log grouped by user_id. A few users have millions of records, causing task skew and long runtimes. Which technique will fix the skew in this aggregation?

A) Use reduceByKey instead of groupBy to avoid shuffles.
B) Increase the Spark driver memory and retry.
C) Filter out the skewed users before the aggregation.
D) Use salting by adding a random prefix to skewed keys before aggregation, then aggregate again after removing the prefix.

2. A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A) Perform a full outer join on a unique key and overwrite existing data.
B) Rely on Delta Lake schema enforcement to prevent duplicate records.
C) Set the configuration delta.deduplicate = true.
D) Perform an insert-only merge with a matching condition on a unique key.
E) VACUUM the Delta table after each batch completes.

3. A company processes semi-structured JSON files from an external source using Auto Loader in a classic Databricks job. Occasionally, records arrive with null critical fields, invalid types, or unexpected nested schema variations. The engineer must ensure that malformed or non- conforming records are not dropped silently and are captured in a separate quarantine table. The pipeline should continue processing good records into the Bronze layer without failing the job, and the approach must support both batch and streaming ingestion.
The data engineer needs to build a robust ingestion pattern that automatically routes bad records to a quarantine Delta table, while still ingesting good records into the Bronze layer for further processing.
Which approach fulfills the quarantine mechanism in this ingestion architecture?

A) Use Auto Loader with LDP and implement an EXPECT () constraint with a record audit logic to route bad records.
B) Use Lakeflow Spark Declarative Pipelines with a SQL pipeline; configure it to drop rows with nulls using where critical_fields is not null, and rely on audit logs for malformed data.
C) Use Auto Loader with failFast mode to set to false, and enable schema evolution; invalid records will be silently ignored during ingestion.
D) Create a notebook job with inferSchema=True, write a streaming query with .foreachBatch() and catch exceptions using try/except to redirect failed batches to quarantine.

4. A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A) Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
B) Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
C) Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
D) Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
E) Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.

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) The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
D) Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
E) The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.

Solutions: