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Databricks Certified Professional Data Engineer certification exam is intended for data engineers, data architects, and other IT professionals who work with big data technologies. Databricks-Certified-Professional-Data-Engineer Exam covers a wide range of topics, including data ingestion, data transformation, data storage, and data analysis. It also covers the use of Databricks tools and technologies such as Databricks Delta, Databricks Runtime, and Apache Spark.
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Databricks, a unified analytics platform provider that helps organizations process and analyze large data sets, offers a certification exam for data engineers called the Databricks Certified Professional Data Engineer. Databricks-Certified-Professional-Data-Engineer exam is designed to test the skills and knowledge of data engineers in building and managing data pipelines, ETL processes, and data architectures within the Databricks platform. Databricks Certified Professional Data Engineer Exam certification is intended to validate the expertise of data engineers in implementing and managing data projects, and to demonstrate their competency in using the Databricks platform.
Databricks-Certified-Professional-Data-Engineer exam is a comprehensive assessment that evaluates a candidate's ability to design, implement, and manage data pipelines, as well as leverage advanced analytics and machine learning techniques on the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and requires candidates to complete a hands-on project that demonstrates their ability to build a data solution on the Databricks platform.
NEW QUESTION # 207
A table 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: E
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.
NEW QUESTION # 208
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: E
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 was omitted 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 Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delta Lake core features" section.
NEW QUESTION # 209
A junior data engineer on your team has implemented the following code block.
The viewnew_eventscontains a batch of records with the same schema as theeventsDelta table.
Theevent_idfield serves as a unique key for this table.
When this query is executed, what will happen with new records that have the sameevent_idas an existing record?
Answer: A
Explanation:
Explanation
This is the correct answer because it describes what will happen with new records that have the same event_id as an existing record when the query is executed. The query uses the INSERT INTO command to append new records from the view new_events to the table events. However, the INSERT INTO command does not check for duplicate values in the primary key column (event_id) and does not perform any update or delete operations on existing records. Therefore, if there are new records that have the same event_id as an existing record, they will be ignored and not inserted into the table events. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Append data using INSERT INTO" section.
NEW QUESTION # 210
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings.
The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?
Answer: A
Explanation:
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.
Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations/index.html).
NEW QUESTION # 211
A data engineer has created a new cluster using shared access mode with default configurations. The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?
Answer: D
Explanation:
Databricks provides different permission levels to control access to clusters. The correct minimal permission required for viewing driver logs is CAN VIEW.
Databricks Cluster Permission Levels:
* CAN ATTACH TO:
* Allows users to attach notebooks to a cluster but does not allow them to view logs.
* Not sufficient for viewing driver logs.
* CAN MANAGE:
* Grants full control over the cluster, including starting, stopping, and editing configurations.
* Too broad for this requirement.
* CAN VIEW (Correct Answer):
* Allows users to view cluster details, logs, and status but not modify any configurations.
* Minimal required permission for viewing logs.
* CAN RESTART:
* Grants permission to restart the cluster, but does not include log access.
* Not sufficient for viewing logs.
Conclusion:
The minimal permission needed to allow the development team to view driver logs is CAN VIEW.
References:
Databricks Cluster Permissions Documentation
NEW QUESTION # 212
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