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Databricks Certified Data Engineer Professional Sample Questions:

1. A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A) Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
B) Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
C) The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
D) Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
E) Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.

2. A data engineer manages a production Lakeflow Declarative Pipeline that processes customer transaction data. The pipeline includes several data quality expectations such as transaction_amount > 0 and customer_id IS NOT NULL. These expectations are defined using the EXPECT clause in SQL.
The engineer aims to monitor the pipeline's data quality by analyzing the number of records that passed or failed each expectation during the latest pipeline update. The Lakeflow Declarative Pipelines event logs are stored in a Delta table named event_log_table.
For the most recent pipeline update, determine a programmatically appropriate approach to extract information like the name of each expectation, associated dataset, count of records that passed the expectation, and count of records that failed the expectation.
Which method retrieves the desired data quality metrics from the Lakeflow Declarative Pipelines event log?

A) Use the Lakeflow Declarative Pipelines UI to navigate to the specific pipeline, select the dataset, and view the Data Quality tab to manually retrieve the expectation metrics.
B) Access the event_log_table, filter for events where event_type = 'flow_progress', and parse details.flow_progress.data_quality.expectations field to extract the required metrics.
C) Access the event_log_table, filter for events where event_type = 'expectation_result', and extract the expectation metrics from the details field.
D) Query the event_log_table for events with event_type = 'data_quality' and directly select the passed_records and failed_records fields.

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

4. While reviewing a query's execution in the Databricks Query Profiler, a data engineer observes that the Top Operators panel shows a Sort operator with high Time Spent and Memory Peak metrics. The Spark UI also reports frequent data spilling. How should the data engineer address this issue?

A) Switch to a broadcast join to reduce memory usage.
B) Repartition the DataFrame to a single partition before sorting.
C) Convert the sort operation to a filter operation.
D) Increase the number of shuffle partitions to better distribute data.

5. A platform team lead is responsible for automating the individual teams attribution towards SQL Warehouse usage. The requirement is to identify the SQL warehouse usage at the individual user's level and generate a daily report to be shared with an executive team that includes leaders from all business units. How should the platform lead generate an automated report that can be shared daily?

A) Restrict users from running any SQL query unless they provide all the query details so that the attribution can be calculated and shared with the executive team.
B) Let the users run the SQL query and then directly report the usage to the executives. The ownership of the SQL warehouse usage will be with the individual teams.
C) Use the system tables to capture the audit and billing usage data and share the queries with the executive team. This enables the executives to execute the query and see the latest results any time.
D) Use the system tables to capture the audit and billing usage data and create a dashboard with daily refresh schedules and shared with the executive team.

Solutions: