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To take the Databricks Databricks-Certified-Professional-Data-Engineer Exam, candidates must first complete the Databricks Certified Associate Data Analyst and Databricks Certified Associate Data Engineer exams. These exams provide a foundation of knowledge and skills that are necessary to pass the professional-level exam. Candidates must also have experience working with Databricks and be familiar with its various features and capabilities.

The Databricks Databricks-Certified-Professional-Data-Engineer exam covers a wide range of topics, including data architecture, data modeling, data integration, data processing, and data analytics. Databricks-Certified-Professional-Data-Engineer exam consists of both theoretical and practical components, which test the candidate's ability to apply their knowledge to real-world scenarios. The practical component requires candidates to complete a series of hands-on exercises using Databricks notebooks, which are used to build, test, and optimize data pipelines.

Databricks Certified Professional Data Engineer certification is a valuable credential for data engineers who work with Databricks. It demonstrates that the candidate has a deep understanding of Databricks and can use it effectively to solve complex data engineering problems. Databricks Certified Professional Data Engineer Exam certification can help data engineers advance their careers, increase their earning potential, and gain recognition as experts in the field of big data and machine learning.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A. Network latency due to some cluster nodes being in different regions from the source data
B. Task queueing resulting from improper thread pool assignment.
C. Skew caused by more data being assigned to a subset of spark-partitions.
D. Credential validation errors while pulling data from an external system.
E. Spill resulting from attached volume storage being too small.

Answer: C

Explanation:
Explanation
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified References:
[Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" section.

NEW QUESTION # 18
A table in the Lakehouse namedcustomer_churn_paramsis used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

A. Modify the overwrite logic to include a field populated by calling spark.sql.functions.
current_timestamp() as data are being written; use this field to identify records written on a particular date.
B. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
C. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.
D. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
E. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.

Answer: C

Explanation:
The approach that would simplify the identification of the changed records is to replace the current overwrite logic with a merge statement to modify only those records that have changed, and write logic to make predictions on the changed records identified by the change data feed. This approach leverages the Delta Lake features of merge and change data feed, which are designed to handle upserts and track row-level changes in a Delta table12. By using merge, the data engineering team can avoid overwriting the entire table every night, and only update or insert the records that have changed in the source data. By using change data feed, the ML team can easily access the change events that have occurred in the customer_churn_params table, and filter them by operation type (update or insert) and timestamp. This way, they can only make predictions on the records that have changed in the past 24 hours, and avoid re-processing the unchanged records.
The other options are not as simple or efficient as the proposed approach, because:
* Option A would require applying the churn model to all rows in the customer_churn_params table, which would be wasteful and redundant. It would also require implementing logic to perform an upsert into the predictions table, which would be more complex than using the merge statement.
* Option B would require converting the batch job to a Structured Streaming job, which would involve changing the data ingestion and processing logic. It would also require using the complete output mode, which would output the entire result table every time there is a change in the source data, which would be inefficient and costly.
* Option C would require calculating the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers, which would be computationally expensive and prone to errors. It would also require storing and accessing the previous predictions, which would add extra storage and I/O costs.
* Option D would require modifying the overwrite logic to include a field populated by calling spark.sql.
functions.current_timestamp() as data are being written, which would add extra complexity and overhead to the data engineering job. It would also require using this field to identify records written on a particular date, which would be less accurate and reliable than using the change data feed.
References: Merge, Change data feed

NEW QUESTION # 19
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, usingdisplay()calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
B. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
C. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
D. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
E. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.

Answer: D

Explanation:
In Databricks notebooks, using thedisplay()function triggers an action that forces Spark to execute the code and produce a result. However, Spark operations are generally divided into transformations and actions.
Transformations create a new dataset from an existing one and are lazy, meaning they are not computed immediately but added to a logical plan. Actions, likedisplay(), trigger the execution of this logical plan.
Repeatedly running the same code cell can lead to misleading performance measurements due to caching.
When a dataset is used multiple times, Spark's optimization mechanism caches it in memory, making subsequent executions faster. This behavior does not accurately represent the first-time execution performance in a production environment where data might not be cached yet.
To get a more realistic measure of performance, it is recommended to:
* Clear the cache or restart the cluster to avoid the effects of caching.
* Test the entire workflow end-to-end rather than cell-by-cell to understand the cumulative performance.
* Consider using a representative sample of the production data, ensuring it includes various cases the code will encounter in production.
References:
* Databricks Documentation on Performance Optimization: Databricks Performance Tuning
* Apache Spark Documentation: RDD Programming Guide - Understanding transformations and actions

NEW QUESTION # 20
Which of the following data workloads will utilize a Bronze table as its destination?

A. A job that develops a feature set for a machine learning application
B. A job that ingests raw data from a streaming source into the Lakehouse
C. A job that queries aggregated data to publish key insights into a dashboard
D. A job that enriches data by parsing its timestamps into a human-readable format
E. A job that aggregates cleaned data to create standard summary statistics

Answer: B

Explanation:
Explanation
The answer is A job that ingests raw data from a streaming source into the Lakehouse.
The ingested data from the raw streaming data source like Kafka is first stored in the Bronze layer as first destination before it is further optimized and stored in Silver.
Medallion Architecture - Databricks
Bronze Layer:
1. Raw copy of ingested data
2. Replaces traditional data lake
3. Provides efficient storage and querying of full, unprocessed history of data
4. No schema is applied at this layer
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
Purpose of each layer in medallion architecture

NEW QUESTION # 21
The data engineer is using Spark's MEMORY_ONLY storage level.
Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

A. The RDD Block Name included the '' annotation signaling failure to cache
B. On Heap Memory Usage is within 75% of off Heap Memory usage
C. Size on Disk is> 0
D. The number of Cached Partitions> the number of Spark Partitions

Answer: A

Explanation:
In the Spark UI's Storage tab, an indicator that a cached table is not performing optimally would be the presence of the _disk annotation in the RDD Block Name. This annotation indicates that some partitions of the cached data have been spilled to disk because there wasn't enough memory to hold them. This is suboptimal because accessing data from disk is much slower than from memory. The goal of caching is to keep data in memory for fast access, and a spill to disk means that this goal is not fully achieved.

NEW QUESTION # 22
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Pass Guaranteed Quiz Databricks Databricks-Certified-Professional-Data-Engineer - First-grade Exam Databricks Certified Professional Data Engineer Exam Preview

Free Download Exam Databricks-Certified-Professional-Data-Engineer Preview - How to Download for Latest Databricks-Certified-Professional-Data-Engineer Test Voucher Free of Charge

Databricks Certified Professional Data Engineer Exam Sample Questions (Q17-Q22):

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