100%합격보장가능한Databricks-Certified-Professional-Data-Engineer최신덤프샘플문제다운인증시험덤프자료
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>> Databricks-Certified-Professional-Data-Engineer최신 덤프샘플문제 다운 <<
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최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q165-Q170):
질문 # 165
You were asked to setup a new all-purpose cluster, but the cluster is unable to start which of the following steps do you need to take to identify the root cause of the issue and the reason why the cluster was unable to start?
- A. Data plane
- B. Workspace logs
- C. Check the cluster driver logs
- D. Check the cluster event logs
(Correct) - E. Storage account
정답:D
설명:
Explanation
Cluster event logs are very useful, to identify issues pertaining to cluster availability. Cluster may not start due to resource limitations or issues with the cloud providers.
Some of the common issues include a subnet for compute VM reaching its limits or exceeding the subscription or cloud account CPU quota limit.
Here is an example where the cluster did not start due to subscription reaching the quota limit on a certain type of cpu cores for a VM type.
Graphical user interface, text, application, email Description automatically generated
Click on event logs
Graphical user interface, text, application, email Description automatically generated
Click on the message to see the detailed error message on why the cluster did not start.
Graphical user interface, text, application, email Description automatically generated
질문 # 166
A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.
all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high-cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time.
Which command should the data engineer implement?
- A. Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.
- B. Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.
- C. Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.
- D. Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.
정답:C
설명:
Databricks recommends Liquid Clustering for optimizing data layout in large Delta tables where query filters involve high-cardinality columns. Liquid Clustering automatically manages file organization and supports incremental maintenance without the need to rewrite data when clustering keys evolve. This is a key advantage over static partitioning or Z-ordering, which require costly file rewrites whenever optimization keys change. By combining Liquid Clustering with a periodic OPTIMIZE command, Databricks automatically compacts small files and maintains efficient data skipping performance. As stated in the Delta Lake optimization guide, Liquid Clustering is designed for scalability, minimal maintenance, and adaptability for analytical workloads with evolving query patterns-making B the correct answer.
질문 # 167
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.
Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?
- A. Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
- B. No: the change data feed only tracks inserts and updates not deleted records.
- C. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
- D. No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
정답:C
설명:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.
질문 # 168
A table named user_ltv is being used to create a view that will be used by data analysts on various teams.
Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:
An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?
- A. Only the email and itv columns will be returned; the email column will contain all null values.
- B. The email and ltv columns will be returned with the values in user itv.
- C. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row. - D. Three columns will be returned, but one column will be named "redacted" and contain only null values.
- E. The email, age. and ltv columns will be returned with the values in user ltv.
정답:C
설명:
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.
Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CASE expression" section.
질문 # 169
A data engineer has ingested a JSON file into a table raw_table with the following schema:
1.transaction_id STRING,
2.payload ARRAY<customer_id:STRING, date:TIMESTAMP, store_id:STRING>
The data engineer wants to efficiently extract the date of each transaction into a table with the fol-lowing
schema:
1.transaction_id STRING,
2.date TIMESTAMP
Which of the following commands should the data engineer run to complete this task?
- A. 1.SELECT transaction_id, payload[date]
2.FROM raw_table; - B. 1.SELECT transaction_id, explode(payload)
2.FROM raw_table; - C. 1.SELECT transaction_id, date from payload
2.FROM raw_table; - D. 1.SELECT transaction_id, payload.date
2.FROM raw_table; - E. 1.SELECT transaction_id, date
2.FROM raw_table;
정답:D
질문 # 170
......
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