ExamPassdump의 도움을 받겠다고 하면 우리는 무조건 최선을 다하여 한번에 패스하도록 도와드릴 것입니다. 또한 일년무료 업뎃서비스를 제공합니다. 중요한 건 덤프가 갱신이 되면 또 갱신버전도 여러분 메일로 보내드립니다. 망설이지 마십시오. 우리를 선택하는 동시에 여러분은DP-750시험고민을 하시지 않으셔도 됩니다.빨리 우리덤프를 장바구니에 넣으시죠.
| Section | Weight | Objectives |
|---|---|---|
| Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Deploy and manage data pipelines and workloads | 30-35% | - Lakehouse architecture operations
|
| Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
| Prepare and process data | 30-35% | - Data ingestion
|
지난 몇년동안 IT산업의 지속적인 발전과 성장을 통해Microsoft 인증DP-750시험은 IT인증시험중의 이정표로 되어 많은 인기를 누리고 있습니다. IT인증시험을ExamPassdump덤프로 준비해야만 하는 이유는ExamPassdump덤프는 IT업계전문가들이 실제시험문제를 연구하여 시험문제에 대비하여 예상문제를 제작했다는 점에 있습니다.
질문 # 18
You have an Azure Databricks workspace.
You have an Apache Spark Structured Streaming job named Job! that processes data continuously and fails periodically due to transient errors You need to ensure that Job! meets the following requirements
* Resumes processing from the point that Job1 failed
* Minimizes how long it takes to restart Job!
* Minimizes the costs to restart Job!
What should you do?
정답：C
설명：
The correct answer is B - implement checkpointing.
A checkpoint is a durable record of the streaming job's progress written to ADLS Gen2 or DBFS after each successfully committed micro-batch. When the job restarts after a transient failure, it reads the checkpoint to find the last committed offset and resumes from that exact point - no data is reprocessed, no data is lost.
This satisfies all three requirements directly: checkpointing enables resumption from the failure point (not from the beginning), restart is fast because there's no replay overhead, and costs are minimised because no compute is wasted reprocessing records already handled.
Option A (decrease retry interval) makes the job retry sooner but doesn't control where it resumes from.
Option C (alert and manual restart) adds human latency and doesn't prevent reprocessing without a checkpoint. Option D (increase minimum nodes) reduces the likelihood of resource-related failures but increases cost and doesn't address the recovery behaviour itself.
Reference: https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/query-recovery
질문 # 19
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a data lifecycle and expiration solution that meets the following requirements
* Transaction logs and deleted data files that are older than 90 days must be removed from Delta tables to reclaim storage.
* All the tables must remain available for querying during the cleanup process.
* Administrative effort must be minimized
What should you do for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
Two actions are needed to reclaim storage while keeping tables queryable:
Set delta.deletedFileRetentionDuration and delta.logRetentionDuration to 90 days on each table. These properties define the retention floor - VACUUM will not touch anything newer than this threshold, so no data needed for time travel within 90 days can be accidentally removed.
Run VACUUM on each table. VACUUM is the Delta Lake command that physically removes data files and transaction log entries older than the retention duration. Importantly, VACUUM runs as a background operation - it uses Delta Lake's MVCC (multi-version concurrency control) to ensure that concurrent reads against the table continue uninterrupted while cleanup happens. Tables are fully available throughout.
OPTIMIZE compacts small files for query performance but doesn't delete anything. Manually deleting files outside the Delta protocol would corrupt the table.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-vacuum
질문 # 20
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
You need to implement a data retention solution that meets the following requirements:
- Deleted data must be retained for 30 days to support audits.
- Deleted data that is older than 30 days must be removed permanently.
- The solution must minimize administrative effort
Which two properties should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
정답：D,E
설명：
To configure an Azure Databricks managed Delta table to retain deleted data for 30 days and minimize administrative overhead, you must set the following table properties:
delta.logRetentionDuration: Set this to interval 30 days. This property controls how long the transaction log history is kept, which is essential for audit trails and time travel.delta.
deletedFileRetentionDuration: Set this to interval 30 days. This property determines the threshold for when deleted data files become eligible for permanent removal by the VACUUM command.
Reference:
https://docs.databricks.com/aws/en/delta/history
질문 # 21
You need to configure the telemetry pipeline to support the planned changes for pipeline orchestration and address the resiliency issues.
What should you do?
정답：B
설명：
Lakeflow Jobs provides native orchestration for multi-task Databricks workflows. Separate ingestion, cleansing, and curation tasks can be connected through explicit dependencies, ensuring that each stage starts only after its required upstream work succeeds. Each task can also have independent retry, notification, timeout, and compute settings, directly addressing the pipeline's resiliency requirements. Azure Data Factory could orchestrate notebooks, but it introduces another service when Lakeflow Jobs already provides the required functionality. A single notebook makes failures harder to isolate and can force successful stages to be rerun. Independently scheduled jobs rely on timing assumptions rather than actual task completion and can fail when an upstream stage runs longer than expected. Explicit Lakeflow Jobs dependencies provide reliable execution order and centralized monitoring. Microsoft Learn
질문 # 22
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two Delta tables named Table1 and Table2 of the same data type.
Table1 contains a column named Column1. Table2 contains a column named Column2.
You run the following query.
SELECT Colum1
FROM Table1
GROUP BY Column1
HAVING COUNT(*) > 1
INTERSECT
SELECT Column2
FROM Table2
GROUP BY Column2
HAVING COUNT(*) > 1;
What occurs when you run the query?
정답：B
설명：
The query will output a single column containing only the values that are duplicated within Table1 and also duplicated within Table2.
Duplicates Isolated First: The GROUP BY and HAVING COUNT(*) > 1 clauses filter each table independently to find only their duplicate rows.
Matching Values Found: The INTERSECT operator compares the filtered results of both tables.
Shared Values Returned: Only values that exist in both filtered datasets are included in the final output.
Automatic Distinct Filtering: INTERSECT automatically removes any duplicate values from the final result set, ensuring each matching value appears exactly once.
Column Naming: The output column will inherit its name from the first SELECT statement (Column1).
Reference:
https://quizlet.com/study-guides/inf3707-chapter-4-data-retrieval-from-2-or-more-tables-
6ec17b26-76a8-4c33-8fa7-18d64154512d
질문 # 23
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우리ExamPassdump 사이트에Microsoft DP-750관련자료의 일부 문제와 답 등 문제들을 제공함으로 여러분은 무료로 다운받아 체험해보실 수 있습니다. 여러분은 이것이야 말로 알맞춤이고, 전면적인 여러분이 지금까지 갖고 싶었던 문제집이라는 것을 느끼게 됩니다.
DP-750최신 시험 최신 덤프자료: https://www.exampassdump.com/DP-750_valid-braindumps.html