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Databricks Certified Professional Data Engineer Exam Sample Questions (Q104-Q109):
NEW QUESTION # 104
A data engineer has three notebooks in an ELT pipeline. The notebooks need to be executed in a specific order
for the pipeline to complete successfully. The data engineer would like to use Delta Live Tables to manage this
process.
Which of the following steps must the data engineer take as part of implementing this pipeline using Delta
Live Tables?
- A. They need to create a Delta Live Tables pipeline from the Data page
- B. They need to refactor their notebook to use Python and the dlt library
- C. They need to create a Delta Live Tables pipeline from the Jobs page
- D. They need to create a Delta Live tables pipeline from the Compute page
- E. They need to refactor their notebook to use SQL and CREATE LIVE TABLE keyword
Answer: C
NEW QUESTION # 105
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?
- A. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
- B. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly- parallel writes.
- C. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
- D. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
Answer: A
Explanation:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints.
Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.References:
* Databricks Documentation on Delta Lake: Delta Lake Guide
* Databricks Documentation on ACID Transactions in Delta Lake: ACID Transactions in Delta Lake
NEW QUESTION # 106
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
Which command allows manual confirmation that these three requirements have been met?
- A. DESCRIBE HISTORY dev.pii test
- B. SHOW TABLES dev
- C. SHOW TBLPROPERTIES dev.pii test
- D. DESCRIBE EXTENDED dev.pii test
- E. DESCRIBE DETAIL dev.pii test
Answer: D
Explanation:
This is the correct answer because it allows manual confirmation that these three requirements have been met.
The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property
"contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "DESCRIBE EXTENDED" section.
NEW QUESTION # 107
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 insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
- 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 fail completely because of the constraint violation and no records will be inserted into the target table.
Answer: E
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 # 108
The data engineering team noticed that one of the job normally finishes in 15 mins but gets stuck randomly when reading remote databases due to a network packet drop, which of the following steps can be used to improve the stability of the job?
- A. Use Databrick REST API to monitor long running jobs and issue a kill command
- B. Use Cluster timeout setting in the Job cluster UI
- C. Modify the task, to include a timeout to kill the job if it runs more than 15 mins.
- D. Use Jobs runs, active runs UI section to monitor and kill long running job
- E. Use Spark job time out setting in the Spark UI
Answer: C
Explanation:
Explanation
The answer is, Modify the task, to include time out to kill the job if it runs more than 15 mins.
https://docs.microsoft.com/en-us/azure/databricks/data-engineering/jobs/jobs#timeout
NEW QUESTION # 109
......
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