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Databricks-Certified-Data-Engineer-Associate덤프최신문제완벽한시험최신덤프
Databricks인증 Databricks-Certified-Data-Engineer-Associate시험을 패스하여 자격증을 취득하여 승진이나 이직을 꿈구고 있는 분이신가요? 이 글을 읽게 된다면Databricks인증 Databricks-Certified-Data-Engineer-Associate시험패스를 위해 공부자료를 마련하고 싶은 마음이 크다는것을 알고 있어 시장에서 가장 저렴하고 가장 최신버전의 Databricks인증 Databricks-Certified-Data-Engineer-Associate덤프자료를 강추해드립니다. 높은 시험패스율을 자랑하고 있는Databricks인증 Databricks-Certified-Data-Engineer-Associate덤프는 여러분이 승진으로 향해 달리는 길에 날개를 펼쳐드립니다.자격증을 하루 빨리 취득하여 승진꿈을 이루세요.
GAQM Databricks 인증 데이터 엔지니어 관련 시험은 Databricks를 사용한 데이터 엔지니어링과 관련된 다양한 주제를 다루는 객관식 질문으로 구성됩니다. 시험은 데이터 엔지니어링 개념, 빅 데이터 처리 및 분석 및 데이터 사업을 사용한 클라우드 컴퓨팅에 대한 후보자의 지식을 테스트합니다. 응시자는 Databricks Certified Data Engineer Associate 인증을 받기 위해 시험에 합격해야합니다.
GAQM Databricks-Certified-Data-Engineer-Associate 시험은 Databricks를 사용하여 데이터 파이프라인을 구축하고 유지하는 능력을 검증하는 인증입니다. Databricks는 Apache Spark를 사용하여 대용량 데이터를 쉽게 처리할 수 있는 클라우드 기반 데이터 플랫폼입니다. 이 인증은 확장 가능하고 신뢰성 있으며 성능이 뛰어난 데이터 파이프라인을 구축하는 데 전문성을 갖춘 데이터 엔지니어를 대상으로 디자인되었습니다.
>> Databricks-Certified-Data-Engineer-Associate덤프최신문제 <<
Databricks-Certified-Data-Engineer-Associate인기시험덤프, Databricks-Certified-Data-Engineer-Associate인증시험덤프
Databricks Databricks-Certified-Data-Engineer-Associate인증시험패스는 아주 어렵습니다. 자기에맞는 현명한 학습자료선택은 성공을 내딛는 첫발입니다. 퍼펙트한 자료만의 시험에 성공할수 있습니다. Pass4Tes시험문제와 답이야 말로 퍼펙트한 자료이죠. 우리Databricks Databricks-Certified-Data-Engineer-Associate인증시험자료는 100%보장을 드립니다. 또한 구매 후 일년무료 업데이트버전을 받을 수 있는 기회를 얻을 수 있습니다.
GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) 시험은 Databricks를 사용하여 대용량 데이터 솔루션을 설계하고 구현하는 전문가들의 전문성을 증명하기 위한 자격증 프로그램입니다. 이 시험은 Databricks를 사용하여 대규모 데이터 처리 시스템을 구축하고 관리하는 데이터 엔지니어, 데이터 분석가 및 빅데이터 아키텍트를 대상으로 합니다.
최신 Databricks Certification Databricks-Certified-Data-Engineer-Associate 무료샘플문제 (Q10-Q15):
질문 # 10
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?
- A. There is no way to share data between PySpark and SQL.
- B. spark.delta.table
- C. SELECT * FROM sales
- D. spark.sql
- E. spark.table
정답：D
설명：
The spark.sql operation allows the data engineering team to run a SQL query and return the result as a PySpark DataFrame. This way, the data engineering team can use the same query that the data analyst has developed and operate with the results in PySpark. For example, the data engineering team can use spark.sql("SELECT * FROM sales") to get a DataFrame of all the records from the sales Delta table, and then apply various tests or transformations using PySpark APIs. The other options are either not valid operations (A, D), not suitable for running a SQL query (B, E), or not returning a DataFrame (A). Reference: Databricks Documentation - Run SQL queries, Databricks Documentation - Spark SQL and DataFrames.
질문 # 11
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?
- A. They can increase the maximum bound of the SQL endpoint's scaling range
- B. They can turn on the Serverless feature for the SQL endpoint.
- C. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized."
- D. They can increase the cluster size of the SQL endpoint.
- E. They can turn on the Auto Stop feature for the SQL endpoint.
정답：B
설명：
Explanation
Databricks SQL endpoints can run in two modes: Serverless and Dedicated. Serverless mode allows you to run queries without managing clusters, while Dedicated mode allows you to run queries on a specific cluster.
Serverless mode is faster and more cost-effective for ad-hoc queries, especially when the SQL endpoint is not running. Dedicated mode is more suitable for predictable and consistent performance, especially for long-running queries. By turning on the Serverless feature for the SQL endpoint, the data engineering team can reduce the time it takes to start the SQL endpoint and return results. The other options are not relevant or effective for this scenario. References: Databricks SQL endpoints, New Performance Improvements in Databricks SQL, Slowness when fetching results in Databricks SQL
질문 # 12
Which of the following statements regarding the relationship between Silver tables and Bronze tables is always true?
- A. Silver tables contain a more refined and cleaner view of data than Bronze tables.
- B. Silver tables contain more data than Bronze tables.
- C. Silver tables contain less data than Bronze tables.
- D. Silver tables contain aggregates while Bronze data is unaggregated.
- E. Silver tables contain a less refined, less clean view of data than Bronze data.
정답：A
설명：
In a medallion architecture, a common data design pattern for lakehouses, data flows from Bronze to Silver to Gold layer tables, with each layer progressively improving the structure and quality of data. Bronze tables store raw data ingested from various sources, while Silver tables apply minimal transformations and cleansing to create an enterprise view of the data. Silver tables can also join and enrich data from different Bronze tables to provide a more complete and consistent view of the data. Therefore, option D is the correct answer, as Silver tables contain a more refined and cleaner view of data than Bronze tables. Option A is incorrect, as it is the opposite of the correct answer. Option B is incorrect, as Silver tables do not necessarily contain aggregates, but can also store detailed records. Option C is incorrect, as Silver tables may contain less data than Bronze tables, depending on the transformations and cleansing applied. Option E is incorrect, as Silver tables may contain more data than Bronze tables, depending on the joins and enrichments applied. References: What is a Medallion Architecture?, Transforming Bronze Tables in Silver Tables, What is the medallion lakehouse architecture?
질문 # 13
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day.
They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?
- A. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
- B. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
- C. They could redesign the data model to separate the data used in the final query into a new table.
- D. They could submit a feature request with Databricks to add this functionality.
- E. They could only run the entire program on Sundays.
정답：B
설명：
This approach would allow the data engineering team to use the existing SQL program and add some logic to control the execution of the final query based on the day of the week. They could use the datetime module in Python to get the current date and check if it is a Sunday. If so, they could run the final query, otherwise they could skip it. This way, they could schedule the program to run every day without changing the data model or the source table. References: PySpark SQL Module, Python datetime Module, Databricks Jobs
질문 # 14
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when it is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?
- A. They can reduce the cluster size of the SQL endpoint.
- B. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
- C. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
- D. They can turn on the Auto Stop feature for the SQL endpoint.
- E. They can set up the dashboard's SQL endpoint to be serverless.
정답：D
질문 # 15
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