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

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is configuring a Lakeflow Declarative Pipeline to process CDC (Change Data Capture) data from a source. The source events sometimes arrive out of order, and multiple updates may occur with the same update_timestamp but with different update_sequence_id.
What should the data engineer do to ensure events are sequenced correctly?

A) Use SEQUENCE BY STRUCT(event_timestamp, update_sequence_id) in AUTO CDC APIs.
B) Use a window function to sort update_sequence_id within the same partition, i.e., update_timestamp in the LDP pipeline.
C) Set track_history_column_list to [event_timestamp, event_id] in AUTO CDC APIs.
D) Use dropDuplicates() to remove out-of-order and duplicate records in LDP.

2. The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A) Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
B) Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
C) Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.
D) Define a view against the products_per_order table and define the dashboard against this view.
E) Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.

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

4. A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities. Which type of Databricks cluster should the data engineer configure?

A) A high-concurrency cluster designed for interactive SQL workloads.
B) A job cluster configured to autoscale across multiple workers during the pipeline run.
C) An all-purpose cluster always kept running to ensure low-latency job startup times.
D) A lightweight single-node cluster with low worker node count to reduce costs.

5. A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high- cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time. Which command should the data engineer implement?

A) Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.
B) Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.
C) Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.
D) Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.

Solutions: