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Databricks Databricks-Certified-Data-Analyst-Associate최고품질덤프샘플문제다운 & Databricks-Certified-Data-Analyst-Associate시험패스덤프공부자료
만약 여러분은Databricks Databricks-Certified-Data-Analyst-Associate인증시험취득으로 이 치열한 IT업계경쟁 속에서 자기만의 자리를 잡고, 스펙을 쌓고, 전문적인 지식을 높이고 싶으십니까? 하지만Databricks Databricks-Certified-Data-Analyst-Associate패스는 쉬운 일은 아닙니다.Databricks Databricks-Certified-Data-Analyst-Associate패스는 여러분이 IT업계에 한발작 더 가까워졌다는 뜻이죠. 하지만 이렇게 중요한 시험이라고 많은 시간과 정력을 낭비할필요는 없습니다. Fast2test의 완벽한 자료만으로도 가능합니다. Fast2test의 덤프들은 모두 전문적으로 IT관련인증시험에 대하여 연구하여 만들어진것이기 때문입니다.
네트웨크시대인 지금 인터넷에 검색하면 수많은Databricks인증 Databricks-Certified-Data-Analyst-Associate시험공부자료가 검색되는데 그중에서도Fast2test에서 출시한 Databricks인증 Databricks-Certified-Data-Analyst-Associate덤프가 가장 높은 인지도를 지니고 있습니다. Databricks인증 Databricks-Certified-Data-Analyst-Associate덤프에는Databricks인증 Databricks-Certified-Data-Analyst-Associate시험문제의 기출문제와 예상문제가 수록되어있어 덤프에 있는 문제만 잘 공부하시면 시험은 가볍게 패스가능합니다. Databricks인증 Databricks-Certified-Data-Analyst-Associate시험을 통과하여 자겨증취득하는 꿈에 더욱 가까이 다가가세요.
>> Databricks Databricks-Certified-Data-Analyst-Associate최고품질 덤프샘플문제 다운 <<
Databricks-Certified-Data-Analyst-Associate시험패스 덤프공부자료, Databricks-Certified-Data-Analyst-Associate시험패스 가능한 공부문제
Fast2test의 Databricks Databricks-Certified-Data-Analyst-Associate덤프를 구매하기전 우선 pdf버전 덤프샘플을 다운받아 덤프문제를 공부해보시면Fast2test덤프품질에 신뢰가 느껴질것입니다. Fast2test의 Databricks Databricks-Certified-Data-Analyst-Associate덤프가 고객님의 시험패스테 조금이나마 도움이 되신다면 행복으로 느끼겠습니다.
최신 Data Analyst Databricks-Certified-Data-Analyst-Associate 무료샘플문제 (Q54-Q59):
질문 # 54
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every 10 minutes.
A data analyst has created a dashboard based on this gold level dat
a. The project stakeholders want to see the results in the dashboard updated within 10 minutes or less of new data becoming available within the gold-level tables.
What is the ability to ensure the streamed data is included in the dashboard at the standard requested by the project stakeholders?
- A. A refresh schedule with a Structured Streaming cluster
- B. A refresh schedule with stakeholders included as subscribers
- C. A refresh schedule with an interval of 10 minutes or less
- D. A refresh schedule with an always-on SQL Warehouse (formerly known as SQL Endpoint
정답:C
설명:
In this scenario, the data engineering team has configured a Structured Streaming pipeline that updates the gold-level tables every 10 minutes. To ensure that the dashboard reflects the most recent data, it is essential to set the dashboard's refresh schedule to an interval of 10 minutes or less. This synchronization ensures that stakeholders view the latest information shortly after it becomes available in the gold-level tables. Options B, C, and D do not directly address the requirement of aligning the dashboard refresh frequency with the data update interval.
질문 # 55
A data analyst has created a Query in Databricks SQL, and now they want to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which of the following steps will they need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?
- A. They will need to create two separate dashboards.
- B. They will need to add two separate visualizations to the dashboard based on the same Query.
- C. They will need to copy the Query and create one data visualization per query.
- D. They will need to decide on a single data visualization to add to the dashboard.
- E. They will need to alter the Query to return two separate sets of results.
정답:B
설명:
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. Reference: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL
질문 # 56
Which location can be used to determine the owner of a managed table?
- A. Review the Owner field in the table page using Catalog Explorer
- B. Review the Owner field in the database page using Data Explorer
- C. Review the Owner field in the table page using the SQL Editor
- D. Review the Owner field in the schema page using Data Explorer
정답:A
설명:
In Databricks, to determine the owner of a managed table, you can utilize the Catalog Explorer feature. The steps are as follows:
Access Catalog Explorer:
In your Databricks workspace, click on the Catalog icon in the sidebar to open Catalog Explorer.
Navigate to the Table:
Within Catalog Explorer, browse through the catalog and schema to locate the specific managed table whose ownership you wish to verify.
View Table Details:
Click on the table name to open its details page.
Identify the Owner:
On the table's details page, review the Owner field, which displays the principal (user, service principal, or group) that owns the table.
This method provides a straightforward way to ascertain the ownership of managed tables within the Databricks environment. Understanding table ownership is essential for managing permissions and ensuring proper access control.
질문 # 57
A data analyst is processing a complex aggregation on a table with zero null values and their query returns the following result:
Which of the following queries did the analyst run to obtain the above result?
- A.

- B.

- C.

- D.

- E.

정답:E
설명:
The result set provided shows a combination of grouping by two columns (group_1 and group_2) with subtotals for each level of grouping and a grand total. This pattern is typical of a GROUP BY ... WITH ROLLUP operation in SQL, which provides subtotal rows and a grand total row in the result set.
Considering the query options:
A) Option A: GROUP BY group_1, group_2 INCLUDING NULL - This is not a standard SQL clause and would not result in subtotals and a grand total.
B) Option B: GROUP BY group_1, group_2 WITH ROLLUP - This would create subtotals for each unique group_1, each combination of group_1 and group_2, and a grand total, which matches the result set provided.
C) Option C: GROUP BY group_1, group 2 - This is a simple GROUP BY and would not include subtotals or a grand total.
D) Option D: GROUP BY group_1, group_2, (group_1, group_2) - This syntax is not standard and would likely result in an error or be interpreted as a simple GROUP BY, not providing the subtotals and grand total.
E) Option E: GROUP BY group_1, group_2 WITH CUBE - The WITH CUBE operation produces subtotals for all combinations of the selected columns and a grand total, which is more than what is shown in the result set.
The correct answer is Option B, which uses WITH ROLLUP to generate the subtotals for each level of grouping as well as a grand total. This matches the result set where we have subtotals for each group_1, each combination of group_1 and group_2, and the grand total where both group_1 and group_2 are NULL.
질문 # 58
Which of the following is an advantage of using a Delta Lake-based data lakehouse over common data lake solutions?
- A. ACID transactions
- B. Flexible schemas
- C. Open-source formats
- D. Scalable storage
- E. Data deletion
정답:A
설명:
A Delta Lake-based data lakehouse is a data platform architecture that combines the scalability and flexibility of a data lake with the reliability and performance of a data warehouse. One of the key advantages of using a Delta Lake-based data lakehouse over common data lake solutions is that it supports ACID transactions, which ensure data integrity and consistency. ACID transactions enable concurrent reads and writes, schema enforcement and evolution, data versioning and rollback, and data quality checks. These features are not available in traditional data lakes, which rely on file-based storage systems that do not support transactions. Reference:
Delta Lake: Lakehouse, warehouse, advantages | Definition
Synapse - Data Lake vs. Delta Lake vs. Data Lakehouse
Data Lake vs. Delta Lake - A Detailed Comparison
Building a Data Lakehouse with Delta Lake Architecture: A Comprehensive Guide
질문 # 59
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