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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Professional Data Engineer Exam |
| Exam Number: | Databricks-Certified-Professional-Data-Engineer |
| Related Certifications: | Databricks Certified Associate Developer Databricks Certified Data Analyst Associate |
| Exam Duration: | 90 minutes |
| Exam Format: | Multiple Select, Multiple Choice |
| Exam Price: | $200 USD |
| Real Exam Qty: | 60 |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Passing Score: | 70% |
| Sample Questions: | Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) |
| Pre Condition: | Recommended: 6+ months of hands-on experience with Databricks and data engineering concepts; familiarity with Python or Scala and SQL is strongly recommended |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/professional-data-engineer |
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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.
NEW QUESTION # 143
A Delta Lake table with Change Data Feed (CDF) enabled in the Lakehouse named customer_churn_params is 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?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "Change data feed (CDF) provides row-level change information for Delta tables." Exact extract: "Use table_changes to query the set of rows that were inserted, updated, or deleted between two versions (or timestamps)." Exact extract: "MERGE INTO updates and inserts only the rows that changed." Overwriting the table nightly makes it difficult to isolate just the changed rows. With CDF enabled, if you update the table using MERGE so only changed rows are touched, you can read exactly those changed rows from CDF for the last 24 hours and score only them, which is simpler and more efficient.
Reference:
NEW QUESTION # 144
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?
Answer: B
Explanation:
This is the correct answer because it describes how Delta Lake can help to avoid data loss of this nature in the future. By ingesting all raw data and metadata from Kafka to a bronze Delta table, Delta Lake creates a permanent, replayable history of the data state that can be used for recovery or reprocessing in case of errors or omissions in downstream applications or pipelines. Delta Lake also supports schema evolution, which allows adding new columns to existing tables without affecting existing queries or pipelines. Therefore, if a critical field wasomitted from an application that writes its Kafka source to Delta Lake, it can be easily added later and the data can be reprocessed from the bronze table without losing any information. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delta Lake core features" section.
NEW QUESTION # 145
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?
Answer: E
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 # 146
A data engineer, while designing a Pandas UDF to process financial time-series data with complex calculations that require maintaining state across rows within each stock symbol group, must ensure the function is efficient and scalable.
Which approach will solve the problem with minimum overhead while preserving data integrity?
Answer: A
Explanation:
The Databricks documentation recommends applyInPandas() for complex per-group operations where maintaining internal state within each group is necessary. When using applyInPandas(), Spark provides all records for each grouping key as a Pandas DataFrame to the function, allowing efficient vectorized operations with local state management. This approach ensures high performance and scalability while maintaining logical isolation between groups. In contrast, SCALAR and SCALAR_ITER UDFs operate on individual rows or batches and cannot maintain inter-row state effectively. grouped_agg UDFs are limited to computing aggregates and do not support complex multi-row transformations. Therefore, applyInPandas() is the correct and Databricks-recommended solution for stateful per-group time-series computations.
NEW QUESTION # 147
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?
Answer: E
Explanation:
The libraries command group allows you to install, uninstall, and list libraries on Databricks clusters. You can use the libraries install command to install a custom Python Wheel on a cluster by specifying the --whl option and the path to the wheel file. For example, you can use the following command to install a custom Python Wheel named mylib-0.1-py3-none-any.whl on a cluster with the id 1234-567890-abcde123:
databricks libraries install --cluster-id 1234-567890-abcde123 --whl dbfs:/mnt/mylib/mylib-0.1-py3-none-any.whl This will upload the custom Python Wheel to the cluster and make it available for use with a production job. You can also use the libraries uninstall command to uninstall a library from a cluster, and the libraries list command to list the libraries installed on a cluster.
Reference:
Libraries CLI (legacy): https://docs.databricks.com/en/archive/dev-tools/cli/libraries-cli.html Library operations: https://docs.databricks.com/en/dev-tools/cli/commands.html#library-operations Install or update the Databricks CLI: https://docs.databricks.com/en/dev-tools/cli/install.html
NEW QUESTION # 148
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