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Databricks Certified Professional Data Engineer certification is highly valued by organizations that use the Databricks platform for their data processing and analytics needs. By earning this certification, data engineers can demonstrate their expertise and proficiency in using the Databricks platform to design and implement complex data projects. Databricks Certified Professional Data Engineer Exam certification can also help data engineers advance their careers and increase their earning potential, as it is recognized and respected by employers in the data engineering field.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q92-Q97):

NEW QUESTION # 92
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A. awaitArrival("event_time", "10 minutes")
B. delayWrite("event_time", "10 minutes")
C. await("event_time + '10 minutes'")
D. withWatermark("event_time", "10 minutes")
E. slidingWindow("event_time", "10 minutes")

Answer: D

Explanation:
Explanation
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The otheroptions are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html Windowed aggregations:
https://docs.databricks.com/spark/latest/structured-streaming/window-operations.html

NEW QUESTION # 93
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
B. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
C. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
D. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
E. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.

Answer: B

Explanation:
Explanation
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. References:
Constraints: https://docs.delta.io/latest/delta-constraints.html
ACID Transactions: https://docs.delta.io/latest/delta-intro.html#acid-transactions

NEW QUESTION # 94
The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalidlatitudeandlongitudevalues in theactivity_detailstable have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to addCHECKconstraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

A. The activity details table already exists; CHECK constraints can only be added during initial table creation.
B. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
C. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
D. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
E. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.

Answer: B

Explanation:
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and 180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Add a CHECK constraint to an existing table" section.
https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-alter-table.html#add-constraint

NEW QUESTION # 95
A dataset has been defined using Delta Live Tables and includes an expectations clause: CON-STRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
B. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
C. Records that violate the expectation cause the job to fail.
D. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
E. Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset.

Answer: B

Explanation:
Explanation
The answer is, Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
Delta live tables support three types of expectations to fix bad data in DLT pipelines Review below example code to examine these expectations, Diagram Description automatically generated with medium confidence

NEW QUESTION # 96
The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables.
Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

A. Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
B. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
C. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
D. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
E. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.

Answer: D

Explanation:
This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "Database object privileges" section.

NEW QUESTION # 97
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Free PDF Quiz Databricks-Certified-Professional-Data-Engineer - Authoritative Databricks Certified Professional Data Engineer Exam Reliable Dumps Ebook

Databricks-Certified-Professional-Data-Engineer Test Pass4sure | Exam Databricks-Certified-Professional-Data-Engineer PDF

Databricks Certified Professional Data Engineer Exam Sample Questions (Q92-Q97):

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