Latest Databricks-Certified-Data-Engineer-Professional Exam Notes, Databricks-Certified-Data-Engineer-Professional Valid Practice Materials

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

Section	Objectives
Topic 1: Data Ingestion and Processing	- Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals - ETL pipeline design patterns
Topic 2: Delta Lake and Data Management	- Time travel and versioning - Delta Lake transactions and ACID properties - Schema evolution and enforcement
Topic 3: Production Pipelines and Orchestration	- Error handling and recovery strategies - Databricks Workflows - Job scheduling and monitoring
Topic 4: Databricks Lakehouse Platform Architecture	- Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture - Data governance concepts (Unity Catalog basics)
Topic 5: Data Modeling and Transformation	- Spark SQL transformations - Dimensional modeling concepts - Performance optimization techniques

>> Latest Databricks-Certified-Data-Engineer-Professional Exam Notes <<

Databricks-Certified-Data-Engineer-Professional Valid Practice Materials - Latest Databricks-Certified-Data-Engineer-Professional Practice Questions

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q10-Q15):

NEW QUESTION # 10
A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?

A. Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
B. Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.
C. Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
D. Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.

Answer: D

Explanation:
Materialized views in Databricks are optimized for precomputing and caching results of complex SQL queries, joins, and aggregations. They store query outputs physically and automatically refresh on a schedule or incremental change basis, drastically improving BI dashboard performance and reducing compute costs.
Conversely, streaming tables are designed for real-time data ingestion and processing, enabling event-driven analytics and low-latency use cases.
Databricks documentation explicitly recommends materialized views for analytical workloads with periodic updates and streaming tables for continuously updating sources. Therefore, the correct choice is C, where complex aggregations from large tables benefit most from materialized precomputation for fast reporting.

NEW QUESTION # 11
Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
B. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
C. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
D. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
E. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.

Answer: E

Explanation:
The key idea in Structured Streaming is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model. You will express your streaming computation as standard batch-like query as on a static table, and Spark runs it as an incremental query on the unbounded input table. Let's understand this model in more detail.
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html

NEW QUESTION # 12
A data engineer is using Structured Streaming to read in transaction data from a bronze Delta table. It was discovered that the data has quality issues where sometimes the transaction value is negative, and when that occurs, the rows need to be routed to a separate quarantine table. They have low latency requirements for the good data since it is used by downstream systems, but the bad data will only be analyzed periodically and has no production dependencies. The quarantine job needs to be implemented so that it cannot affect the production processes that depend on the good data, and the cost of the job needs to be minimized. How should the quarantine process be implemented in order to satisfy these requirements?

A. The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. A new boolean column called "quarantine" should be added to the dataframe, and its value should be set to true if the transaction value is less than 0 and false if the transaction value is greater than or equal to 0. Processing and storing all the data together will save costs.
B. The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing, and should not share compute with other processes. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing, and should be implemented on a separate small cluster and only run once a day to minimize cost.
C. The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing. Both should run as separate streams on the same cluster to minimize cost.
D. The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. Inside a foreachBatch function, the dataframe should be filtered so that records with a transaction value greater than or equal to 0 are written to the good data table and records with a transaction value less than 0 are written to a quarantine table. Try/Catch can be added around the writes in the foreachBatch function so that the stream can't fail.

Answer: B

Explanation:
Separating the quarantine processing from the low-latency production stream ensures the good- data pipeline cannot be slowed down or destabilized by quarantine writes or failures. Filtering out negative values in the production stream preserves latency and reliability for downstream systems. Running the quarantine stream on a small, independent cluster on a daily schedule minimizes cost while still capturing the bad records for periodic analysis, and it fully isolates quarantine operations from production dependencies.

NEW QUESTION # 13
A facilities-monitoring team is building a near-real-time PowerBI dashboard off the Delta table device_readings:
Columns:
device_id (STRING, unique sensor ID)
event_ts (TIMESTAMP, ingestion timestamp UTC)
temperature_c (DOUBLE, temperature in °C)
Requirement:
For each sensor, generate one row per non-overlapping 5-minute
interval, offset by 2 minutes (e.g., 00:02-00:07, 00:07-00:12, ...).
Each row must include interval start, interval end, and average
temperature in that slice.
Downstream BI tools (e.g., Power BI) must use the interval timestamps
to plot time-series bars.

A. SELECT device_id,
date_trunc('minute', event_ts - INTERVAL 2 MINUTES) + INTERVAL 2 MINUTES AS bucket_start, date_trunc('minute', event_ts - INTERVAL 2 MINUTES) + INTERVAL 7 MINUTES AS bucket_end, AVG(temperature_c) AS avg_temp_5m FROM device_readings GROUP BY device_id, date_trunc('minute', event_ts - INTERVAL 2 MINUTES) ORDER BY device_id, bucket_start;
B. SELECT device_id,
event_ts,
AVG(temperature_c) OVER (
PARTITION BY device_id
ORDER BY event_ts
RANGE BETWEEN INTERVAL 5 MINUTES PRECEDING AND CURRENT ROW
) AS avg_temp_5m
FROM device_readings
WINDOW w AS (window(event_ts, '5 minutes', '2 minutes'));
C. WITH buckets AS (
SELECT device_id,
window(event_ts, '5 minutes', '2 minutes', '5 minutes') AS win,
temperature_c
FROM device_readings
)
SELECT device_id,
win.start AS bucket_start,
win.end AS bucket_end,
AVG(temperature_c) AS avg_temp_5m
FROM buckets
GROUP BY device_id, win
ORDER BY device_id, bucket_start;
D. SELECT device_id,
window.start AS bucket_start,
window.end AS bucket_end,
AVG(temperature_c) AS avg_temp_5m
FROM device_readings
GROUP BY device_id, window(event_ts, '5 minutes', '5 minutes', '2 minutes') ORDER BY device_id, bucket_start;

Answer: C

Explanation:
The correct way to satisfy non-overlapping windows with an offset in Databricks SQL is to use the window function with three parameters: window duration, slide duration, and start offset.
In option A, the function call:
window(event_ts, '5 minutes', '2 minutes', '5 minutes')
creates 5-minute windows that slide every 5 minutes, with a 2-minute offset, which exactly matches the requirement (intervals like 00:02?0:07, 00:07?0:12, ...).

NEW QUESTION # 14
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. Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
B. Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
C. 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.
D. 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.
E. Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.

Answer: A

Explanation:
https://www.databricks.com/blog/2017/05/22/running-streaming-jobs-day-10x-cost-savings.html

NEW QUESTION # 15
......

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