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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. 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) 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.
B) 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.
C) 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.
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.

2. A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

A) Yes; Delta Lake supports infinite concurrent writers.
B) Yes; both of the streams can share a single checkpoint directory.
C) No; each of the streams needs to have its own checkpoint directory.
D) No; Delta Lake manages streaming checkpoints in the transaction log.
E) No; only one stream can write to a Delta Lake table.

3. The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

A) The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
B) The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
C) The source query failed to update properly for three consecutive minutes and then restarted
D) The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
E) The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query

4. When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A) Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
B) Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
C) Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
D) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
E) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1

5. A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?

A) Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
B) Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
C) Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
D) Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
E) Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.

Solutions: