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Snowflake ARA-C01최신덤프데모다운로드 - ARA-C01시험대비덤프데모다운
2025 PassTIP 최신 ARA-C01 PDF 버전 시험 문제집과 ARA-C01 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1Q38_fR6BWrKm1flEAVa_sXst216P51kE
ARA-C01는Snowflake의 인증시험입니다.ARA-C01인증시험을 패스하면Snowflake인증과 한 발작 더 내디딘 것입니다. 때문에ARA-C01시험의 인기는 날마다 더해갑니다.ARA-C01시험에 응시하는 분들도 날마다 더 많아지고 있습니다. 하지만ARA-C01시험의 통과 율은 아주 낮습니다.ARA-C01인증시험준비중인 여러분은 어떤 자료를 준비하였나요?
PassTIP에는 IT인증시험의 최신Snowflake ARA-C01학습가이드가 있습니다. PassTIP 는 여러분들이Snowflake ARA-C01시험에서 패스하도록 도와드립니다. Snowflake ARA-C01시험준비시간이 충분하지 않은 분은 덤프로 철저한 시험대비해보세요. 문제도 많지 않고 깔끔하게 문제와 답만으로 되어있어 가장 빠른 시간내에Snowflake ARA-C01시험합격할수 있습니다.
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SnowPro Advanced Architect 인증 시험은 데이터 모델링, 보안, 성능 최적화 및 데이터 통합을 포함한 다양한 주제를 다루는 포괄적 인 테스트입니다. 이 시험은 복잡한 비즈니스 요구 사항을 충족하는 고급 눈송이 솔루션을 설계, 구현 및 최적화하는 개인의 능력을 평가하도록 설계되었습니다.
최신 SnowPro Advanced Certification ARA-C01 무료샘플문제 (Q128-Q133):
질문 # 128
When using the Snowflake Connector for Kafka, what data formats are supported for the messages? (Choose two.)
- A. Avro
- B. Parquet
- C. CSV
- D. JSON
- E. XML
정답：A,D
설명：
The data formats that are supported for the messages when using the Snowflake Connector for Kafka are Avro and JSON. These are the two formats that the connector can parse and convert into Snowflake table rows. The connector supports both schemaless and schematized JSON, as well as Avro with or without a schema registry1. The other options are incorrect because they are not supported data formats for the messages. CSV, XML, and Parquet are not formats that the connector can parse and convert into Snowflake table rows. If the messages are in these formats, the connector will load them as VARIANT data type and store them as raw strings in the table2. References: Snowflake Connector for Kafka | Snowflake Documentation, Loading Protobuf Data using the Snowflake Connector for Kafka | Snowflake Documentation
질문 # 129
A company has a table with that has corrupted data, named Data. The company wants to recover the data as it was 5 minutes ago using cloning and Time Travel.
What command will accomplish this?
- A. CREATE CLONE TABLE Recover_Data FROM Data AT(OFFSET => -60*5);
- B. CREATE CLONE Recover_Data FROM Data AT(OFFSET => -60*5);
- C. CREATE TABLE Recover Data CLONE Data AT(TIME => -60*5);
- D. CREATE TABLE Recover_Data CLONE Data AT(OFFSET => -60*5);
정답：D
설명：
This is the correct command to create a clone of the table Data as it was 5 minutes ago using cloning and Time Travel. Cloning is a feature that allows creating a copy of a database, schema, table, or view without duplicating the data or metadata. Time Travel is a feature that enables accessing historical data (i.e. data that has been changed or deleted) at any point within a defined period. To create a clone of a table at a point in time in the past, the syntax is:
CREATE TABLE <clone_name> CLONE <source_table> AT (OFFSET => <offset_in_seconds>); The OFFSET parameter specifies the time difference in seconds from the present time. A negative value indicates a point in the past. For example, -60*5 means 5 minutes ago. Alternatively, the TIMESTAMP parameter can be used to specify an exact timestamp in the past. The clone will contain the data as it existed in the source table at the specified point in time12.
Snowflake Documentation: Cloning Objects
Snowflake Documentation: Cloning Objects at a Point in Time in the Past
질문 # 130
Search optimization does not support Materialized views and External Tables
정답：A
질문 # 131
A company has a source system that provides JSON records for various loT operations. The JSON Is loading directly into a persistent table with a variant field. The data Is quickly growing to 100s of millions of records and performance to becoming an issue. There is a generic access pattern that Is used to filter on the create_date key within the variant field.
What can be done to improve performance?
- A. Alter the target table to include additional fields pulled from the JSON records. This would include a create_date field with a datatype of varchar. When this field is used in the filter, partition pruning will occur.
- B. Incorporate the use of multiple tables partitioned by date ranges. When a user or process needs to query a particular date range, ensure the appropriate base table Is used.
- C. Alter the target table to Include additional fields pulled from the JSON records. This would Include a create_date field with a datatype of time stamp. When this field Is used in the filter, partition pruning will occur.
- D. Validate the size of the warehouse being used. If the record count is approaching 100s of millions, size XL will be the minimum size required to process this amount of data.
정답：C
설명：
* The correct answer is A because it improves the performance of queries by reducing the amount of data scanned and processed. By adding a create_date field with a timestamp data type, Snowflake can automatically cluster the table based on this field and prune the micro-partitions that do not match the filter condition. This avoids the need to parse the JSON data and access the variant field for every record.
* Option B is incorrect because it does not improve the performance of queries. By adding a create_date field with a varchar data type, Snowflake cannot automatically cluster the table based on this field and prune the micro-partitions that do not match the filter condition. This still requires parsing the JSON data and accessing the variant field for every record.
* Option C is incorrect because it does not address the root cause of the performance issue. By validating the size of the warehouse being used, Snowflake can adjust the compute resources to match the data volume and parallelize the query execution. However, this does not reduce the amount of data scanned and processed, which is the main bottleneck for queries on JSON data.
* Option D is incorrect because it adds unnecessary complexity and overhead to the data loading and querying process. By incorporating the use of multiple tables partitioned by date ranges, Snowflake can reduce the amount of data scanned and processed for queries that specify a date range. However, this requires creating and maintaining multiple tables, loading data into the appropriate table based on the date, and joining the tables for queries that span multiple date ranges. References:
* Snowflake Documentation: Loading Data Using Snowpipe: This document explains how to use Snowpipe to continuously load data from external sources into Snowflake tables. It also describes the syntax and usage of the COPY INTO command, which supports various options and parameters to control the loading behavior, such as ON_ERROR, PURGE, and SKIP_FILE.
* Snowflake Documentation: Date and Time Data Types and Functions: This document explains the different data types and functions for working with date and time values in Snowflake. It also describes how to set and change the session timezone and the system timezone.
* Snowflake Documentation: Querying Metadata: This document explains how to query the metadata of the objects and operations in Snowflake using various functions, views, and tables. It also describes how to access the copy history information using the COPY_HISTORY function or the COPY_HISTORY view.
* Snowflake Documentation: Loading JSON Data: This document explains how to load JSON data into Snowflake tables using various methods, such as the COPY INTO command, the INSERT command, or the PUT command. It also describes how to access and query JSON data using the dot notation, the FLATTEN function, or the LATERAL join.
* Snowflake Documentation: Optimizing Storage for Performance: This document explains how to
* optimize the storage of data in Snowflake tables to improve the performance of queries. It also describes the concepts and benefits of automatic clustering, search optimization service, and materialized views.
질문 # 132
An Architect has a VPN_ACCESS_LOGS table in the SECURITY_LOGS schema containing timestamps of the connection and disconnection, username of the user, and summary statistics.
What should the Architect do to enable the Snowflake search optimization service on this table?
- A. Assume role with ALL PRIVILEGES including ADD SEARCH OPTIMIZATION in the SECURITY LOGS schema.
- B. Assume role with OWNERSHIP on future tables and ADD SEARCH OPTIMIZATION on the SECURITY_LOGS schema.
- C. Assume role with ALL PRIVILEGES on VPN_ACCESS_LOGS and ADD SEARCH OPTIMIZATION in the SECURITY_LOGS schema.
- D. Assume role with OWNERSHIP on VPN_ACCESS_LOGS and ADD SEARCH OPTIMIZATION in the SECURITY_LOGS schema.
정답：D
질문 # 133
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Snowflake인증 ARA-C01시험을 패스하여 자격증을 취득하는게 꿈이라구요? PassTIP에서 고객님의Snowflake인증 ARA-C01시험패스꿈을 이루어지게 지켜드립니다. PassTIP의 Snowflake인증 ARA-C01덤프는 가장 최신시험에 대비하여 만들어진 공부자료로서 시험패스는 한방에 끝내줍니다.
ARA-C01시험대비 덤프데모 다운: https://www.passtip.net/ARA-C01-pass-exam.html
Snowflake ARA-C01최신 덤프데모 다운로드 그 답은 바로 Pass4Test에서 찾아볼수 있습니다, Snowflake ARA-C01최신 덤프데모 다운로드 시험에서 불합격성적표를 받으시면 덤프구매시 지불한 덤프비용은 환불해드립니다, IT업계 종사자라면 누구나 Snowflake ARA-C01시험대비 덤프데모 다운 시험을 패스하고 싶어하리라고 믿습니다, 뿐만아니라 ARA-C01덤프를 구매하시면 1년무료 업데이트서비스를 제공해드리는데 ARA-C01덤프구매후 업데이트될때마다 업데이트버전을 고객님 구매시 사용한 메일주소로 발송해드려 덤프유효기간을 연장해드립니다, PassTIP ARA-C01시험대비 덤프데모 다운 는 전문적으로 it전문인사들에게 도움을 드리는 사이트입니다.많은 분들의 반응과 리뷰를 보면 우리PassTIP ARA-C01시험대비 덤프데모 다운의 제품이 제일 안전하고 최신이라고 합니다.
유안은 잠시 가만한 눈으로 미경의 얼굴을 바라보았다, 아, 뭐야, 리움 씨였어, 그 답은 바로 Pass4Test ARA-C01시험패스 인증덤프자료에서 찾아볼수 있습니다, 시험에서 불합격성적표를 받으시면 덤프구매시 지불한 덤프비용은 환불해드립니다, IT업계 종사자라면 누구나 Snowflake 시험을 패스하고 싶어하리라고 믿습니다.
시험패스에 유효한 ARA-C01최신 덤프데모 다운로드 덤프문제보기
뿐만아니라 ARA-C01덤프를 구매하시면 1년무료 업데이트서비스를 제공해드리는데 ARA-C01덤프구매후 업데이트될때마다 업데이트버전을 고객님 구매시 사용한 메일주소로 발송해드려 덤프유효기간을 연장해드립니다.
PassTIP 는 전문적으로 it전문인사들에게 도움을ARA-C01드리는 사이트입니다.많은 분들의 반응과 리뷰를 보면 우리PassTIP의 제품이 제일 안전하고 최신이라고 합니다.
참고: PassTIP에서 Google Drive로 공유하는 무료, 최신 ARA-C01 시험 문제집이 있습니다: https://drive.google.com/open?id=1Q38_fR6BWrKm1flEAVa_sXst216P51kE
