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Zachary Young Zachary Young

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DEA-C01최신버전덤프데모문제최신인기인증시험

Fast2test의Snowflake DEA-C01인증시험의 자료 메뉴에는Snowflake DEA-C01인증시험실기와Snowflake DEA-C01인증시험 문제집으로 나누어져 있습니다.우리 사이트에서 관련된 학습가이드를 만나보실 수 있습니다. 우리 Fast2test의Snowflake DEA-C01인증시험자료를 자세히 보시면 제일 알맞고 보장도가 높으며 또한 제일 전면적인 것을 느끼게 될 것입니다.

Snowflake DEA-C01 시험요강:

주제
소개

주제 1

Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.

주제 2

Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.

주제 3

Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.

주제 4

Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.

주제 5

Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.

>> DEA-C01최신버전 덤프데모문제 <<

DEA-C01최신버전 덤프데모문제 100% 유효한 최신버전 공부자료

Fast2test 에서 제공해드리는 Snowflake DEA-C01덤프는 아주 우수한 IT인증덤프자료 사이트입니다. IT업계엘리트한 강사들이 퍼펙트한 Snowflake DEA-C01 덤프문제집을 제작하여 디테일한 시험문제와 답으로 여러분이 아주 간단히Snowflake DEA-C01시험을 패스할 수 있도록 최선을 다하고 있습니다.

최신 SnowPro Advanced DEA-C01 무료샘플문제 (Q61-Q66):

질문 # 61
In efforts to recover the dropped child tables within schema named SCV_SCHEMA by Data Engi-neer, She found that DATA_RETENTION_TIME_IN_DAYS parameter set with value 45 days at Schema level &the data retention period for child tables explicitly set at 85 days. What will happen when she will try to run undrop table command on Child tables to recover them on the 50th day as-suming SCV_SCHEMA is already dropped on 45th day?

A. When a schema is already dropped, the data retention period for child tables, if explicit-ly set to be different from the retention of the schema, is not honoured. So UNDROP command will fail to run on
50th day for Child tables recovery.
B. To honor the data retention period for child tables, She will ab able to recover the child tables on 50th day as DATA_RETENTION_TIME_IN_DAYS is explicitly set with higher retention value.
C. Data Engineer needs to first recover the Schema & then Child tables will automatically be recovered irrespective of Retention Inheritance.
D. Child tables can be recovered using Fail-Safe SQL commands.

정답：A

설명：
Explanation
Dropped Containers and Object Retention Inheritance
Currently, when a database is dropped, the data retention period for child schemas or tables, if ex-plicitly set to be different from the retention of the database, is not honored. The child schemas or tables are retained for the same period of time as the database.
Similarly, when a schema is dropped, the data retention period for child tables, if explicitly set to be different from the retention of the schema, is not honored. The child tables are retained for the same period of time as the schema.
To honor the data retention period for these child objects (schemas or tables), drop them explicitly before you drop the database or schema.

질문 # 62
Data engineer designed the data pipelines using Snowpipe to load data files into Snowflake tables, what will happen in case few files with same name but modified data are queued for reloading?

A. eTAG is changed for Files even they are having same name, so data will be duplicated in SnowFlake tables.
B. Snowpipe uses file loading metadata associated with each table object, so no metadata available to prevent duplication.
C. Snowpipe uses file loading metadata associated with each pipe object to prevent reload-ing the same files (and duplicating data) in a table.
D. Data will be reloaded as files are modified & its associated metadata also changed. But Snowflake handle implicitly deduplication.

정답：C

설명：
Explanation
Snowflake uses file loading metadata to prevent reloading the same files (and duplicating data) in a table.
Snowpipe prevents loading files with the same name even if they were later modified (i.e. have a different eTag).
The file loading metadata is associated with the pipe object rather than the table. As a result:
Staged files with the same name as files that were already loaded are ignored, even if they have been modified, e.g. if new rows were added or errors in the file were corrected.
Truncating the table using the TRUNCATE TABLE command does not delete the Snowpipe file loading metadata.

질문 # 63
An online retail company has an application that runs on Amazon EC2 instances that are in a VPC. The company wants to collect flow logs for the VPC and analyze network traffic.
Which solution will meet these requirements MOST cost-effectively?

A. Publish flow logs to Amazon CloudWatch Logs. Use Amazon Athena for analytics.
B. Publish flow logs to Amazon S3 in text format. Use Amazon Athena for analytics.
C. Publish flow logs to Amazon CloudWatch Logs. Use an Amazon OpenSearch Service cluster for analytics.
D. Publish flow logs to Amazon S3 in Apache Parquet format. Use Amazon Athena for analytics.

정답：D

설명：
Publishing flow logs to Amazon S3 in Apache Parquet format and using Amazon Athena for analytics (D) is the most cost-effective solution. This approach minimizes storage costs due to the efficient compression of Parquet, and optimizes query performance and cost in Athena due to the reduced data size and optimized columnar storage.

질문 # 64
When would a Data engineer use table with the flatten function instead of the lateral flatten combination?

A. When table withFLATTENis acting like a sub-query executed for each returned row
B. Whenthe LATERALFLATTENcombination requires no other source m the from clause to refer to
C. When TABLE with FLATTENrequires another source in the from clause to refer to
D. WhenTABLE with FLATTENrequires no additional source m the from clause to refer to

정답：C

설명：
Explanation
The TABLE function with the FLATTEN function is used to flatten semi-structured data, such as JSON or XML, into a relational format. The TABLE function returns a table expression that can be used in the FROM clause of a query. The TABLE function with the FLATTEN function requires another source in the FROM clause to refer to, such as a table, view, or subquery that contains the semi-structured data. For example:
SELECT t.value:city::string AS city, f.value AS population FROM cities t, TABLE(FLATTEN(input => t.value:population)) f; In this example, the TABLE function with the FLATTEN function refers to the cities table in the FROM clause, which contains JSON data in a variant column named value. The FLATTEN function flattens the population array within each JSON object and returns a table expression with two columns: key and value.
The query then selects the city and population values from the table expression.

질문 # 65
Let us say you have List of 50 Source files, which needs to be loaded into Snowflake internal stage. All these Source system files are already Brotli-compressed files. Which statement is correct with respect to Compression of Staged Files?

A. When staging 50 compressed files in a Snowflake stage, the files are automatically com-pressed using gzip.
B. Auto-detection is not yet supported for Brotli-compressed files; when staging or loading Brotli-compressed files, you must explicitly specify the compression method that was used.
C. Snowflake automatically detect Brotli Compression, will skip further compression of all 50 files.
D. Even though Source files are already compressed, Snowflake do apply default gzip2 Compression to optimize the storage cost.

정답：B

설명：
Explanation
Auto-detection is not yet supported for Brotli-compressed files; when staging or loading Brotli-compressed files, you must explicitly specify the compression method that was used.
To Know more about Compression of Staged Files, please refer the link:
https://docs.snowflake.com/en/user-guide/intro-summary-loading.html#compression-of-staged-files

질문 # 66
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Fast2test는IT업계전문가들이 그들의 노하우와 몇 년간의 경험 등으로 자료의 정확도를 높여 응시자들의 요구를 만족시켜 드립니다. 우리는 꼭 한번에Snowflake DEA-C01시험을 패스할 수 있도록 도와드릴 것입니다. 여러분은Snowflake DEA-C01시험자료 구매로 제일 정확하고 또 최신시험버전의 문제와 답을 사용할 수 있습니다. Pass4Tes의 인증시험적중 율은 아주 높습니다. 때문에 많은 IT인증시험준비중인분들에세 많은 편리를 드릴수 있습니다.100%정확도 100%신뢰.여러분은 마음편히 응시하시면 됩니다.

DEA-C01시험내용: https://kr.fast2test.com/DEA-C01-premium-file.html