DEA-C02유효한시험시험준비에가장좋은기출문제

Itcertkr는 믿을 수 있는 사이트입니다. IT업계에서는 이미 많이 알려져 있습니다. 그리고 여러분에 신뢰를 드리기 위하여 Snowflake 인증DEA-C02 관련자료의 일부분 문제와 답 등 샘플을 무료로 다운받아 체험해볼 수 있게 제공합니다. 아주 만족할 것이라고 믿습니다. Itcertkr제품에 대하여 아주 자신이 있습니다. Snowflake 인증DEA-C02 도 여러분의 무용지물이 아닌 아주 중요한 자료가 되리라 믿습니다. 여러분께서는 아주 순조로이 시험을 패스하실 수 있을 것입니다.

많은 사이트에서도 무료Snowflake DEA-C02덤프데모를 제공합니다. 우리도 마찬가지입니다. 여러분은 그러한Snowflake DEA-C02데모들을 보시고 다시 우리의 덤프와 비교하시면, 우리의 덤프는 다른 사이트덤프와 차원이 다른 덤프임을 아사될 것 입니다. 우리 Itcertkr사이트에서 제공되는Snowflake인증DEA-C02시험덤프의 일부분인 데모 즉 문제와 답을 다운받으셔서 체험해보면 우리Itcertkr에 믿음이 갈 것입니다. 왜냐면 우리 Itcertkr에는 베터랑의 전문가들로 이루어진 연구팀이 잇습니다, 그들은 it지식과 풍부한 경험으로 여러 가지 여러분이Snowflake인증DEA-C02시험을 패스할 수 있을 자료 등을 만들었습니다 여러분이Snowflake인증DEA-C02시험에 많은 도움이Snowflake DEA-C02될 것입니다. Itcertkr 가 제공하는DEA-C02테스트버전과 문제집은 모두Snowflake DEA-C02인증시험에 대하여 충분한 연구 끝에 만든 것이기에 무조건 한번에Snowflake DEA-C02시험을 패스하실 수 있습니다. 때문에Snowflake DEA-C02덤프의 인기는 당연히 짱 입니다.

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네트워크 전성기에 있는 지금 인터넷에서Snowflake 인증DEA-C02시험자료를 많이 검색할수 있습니다. 하지만 왜Itcertkr덤프자료만을 믿어야 할가요? Itcertkr덤프자료는 실제시험문제의 모든 유형에 근거하여 예상문제를 묶어둔 문제은행입니다.시험적중율이 거의 100%에 달하여Snowflake 인증DEA-C02시험을 한방에 통과하도록 도와드립니다.

최신 SnowPro Advanced DEA-C02 무료샘플문제 (Q249-Q254):

질문 # 249
A data engineering team is building a data pipeline in Snowflake. They are using tasks and streams to incrementally load data into a fact table. The team needs to monitor the pipeline's performance and ensure data lineage. What are the valid and most effective techniques to ensure that this pipeline adheres to compliance and governance rules?

A. Use Account Usage views like 'TASK HISTORY and 'STREAM_LAG' to track task execution and stream latency, create stored procedures to log metadata about each pipeline run to a separate metadata table, and rely on developers to manually document the pipeline's data flow and policy enforcement.
B. Implement Snowflake's Data Lineage and Object Dependencies features to track data flow automatically, create Alerts based on 'TASK HISTORY to monitor task failures, and enforce data masking and row-level security policies at the table level. Use Snowflake's tags to categorise and classify objects.
C. Use a third-party data catalog to track lineage, monitor task performance via 'TASK_HISTORY, and ignore data masking and row-level security policies for simplicity in the initial implementation.
D. Enable Snowflake Horizon features, which include Data Lineage, Object Dependencies and Discovery and integrate it with the data lake and also tag the data pipeline.
E. Leverage Snowflake's replication features for disaster recovery, monitor only the replication lag, and disable all security policies to improve performance since those tasks have already been validated during the initial deployment of the software.

정답：B,D

설명：
Option B and E offers the most comprehensive approach. Snowflake's Data Lineage and Object Dependencies features, combined with Alerts based on 'TASK HISTORY, offer automated monitoring and data flow tracking. Enforcing data masking and row-level security is crucial for data governance and compliance, and tagging enables easy categorisation and discovery. Though Horizon and Data Lineage offers data flow tracking, alerting for task failures, data masking and row-level security policies and tagging can be integrated together. Options A lacks automated lineage tracking and relies on manual documentation, which is error-prone. Option C ignores crucial security policies, which is unacceptable. Option D focuses only on disaster recovery and neglects security and monitoring aspects.

질문 # 250
A data engineering team is building a real-time fraud detection system. They have a large 'TRANSACTIONS table that grows rapidly. They need to calculate the average transaction amount per merchant daily. The following query is used:

This query is run every hour and is performance-critical. Which of the following materialized view definitions would provide the BEST performance improvement, considering the need for near real-time data and minimal latency?

A. Option D
B. Option A
C. Option C
D. Option E
E. Option B

정답：B

설명：
Option A provides the best performance because it pre-computes the aggregation for all time, allowing Snowflake to rewrite the query. Option B adds a WHERE clause that limits the data, negating the benefits of materialized view rewrite. Option C using 'REFRESH COMPLETE ON DEMAND is not ideal for near real-time requirements. Option D filters based on a very short time period and not aligned with original problem where the window is 7 days. Option E calculates SUM and COUNT instead of AVG, doesn't match required output.

질문 # 251
A data engineer accidentally truncated a critical table 'ORDERS' in the 'SALES DB' database. The table contained important historical order data, and the data retention period is set to the default. Which of the following options represents the MOST efficient and reliable way to recover the truncated table and its data, minimizing downtime and potential data loss?

A. Restore the entire Snowflake account to a previous point in time before the table was truncated.
B. Contact Snowflake support and request them to restore the table from a system-level backup.
C. Use Time Travel to create a clone of the truncated table from a point in time before the truncation. Then, swap the original table with the cloned table.
D. Create a new table 'ORDERS' and manually re-insert the data from the application's logs and backups.
E. Use the UNDROP TABLE command to restore the table. If UNDROP fails, clone the entire SALES_DB database to a point in time before the truncation using Time Travel.

정답：C

설명：
Option D is the most efficient and reliable. Cloning the table using Time Travel to a point before the truncation allows quick recovery with minimal data loss. The clone can then replace the truncated table. Option A relies on Snowflake support, which can be slow. Option B, UNDROP TABLE command, if the data retention period has passed or data has been purged then we cannot use it. Option C is manual and error-prone. Option E is an extreme measure and impacts the entire account.

질문 # 252
You're building a data pipeline that ingests JSON data from URLs representing real-time weather information. The data structure varies slightly between different weather providers, but all contain a 'location' object with 'city' and 'country' fields, and a 'temperature' field. You need to create a generic function that can handle these variations and extract the location and temperature, returning a flattened JSON object with keys 'city', 'country', and 'temperature'. You want to avoid explicit schema definition and take advantage of Snowflake's VARIANT data type flexibility Given the following sample JSON structures, which approach will best accomplish this?

A. Create a pipe that uses 'COPY INTO to ingest JSON data directly from the URLs into a VARIANT column. The 'FILE FORMAT object is configured to use = TRUE to handle different data types. Post ingestion create a view to query data.
B. Define a Snowflake stored procedure that uses 'SYSTEM$URL_GET to fetch the JSON data, then uses conditional logic with 'TRY TO BOOLEANS and STRY TO DATE to handle different data types. The stored procedure constructs a new JSON object with 'city', 'country', and 'temperature' fields using 'OBJECT_CONSTRUCT.
C. Define a Snowflake view that selects from a table containing the URLs, using 'SYSTEM$URL GET to fetch the JSON data and to extract the 'city', 'country', and 'temperature' fields. Use 'TRY_CAST to convert the 'temperature' to a numeric type.
D. Define a Snowflake external function (UDF) that fetches the JSON data using a Python library like 'requests' or The function then parses the JSON and extracts the required fields, handling potential missing fields using 'try...except' blocks. The function returns a JSON string representing the flattened object.
E. Create a Snowflake external function written in Java that uses 'java.net.lJRL' to fetch the JSON data and 'com.fasterxml.jackson.databind' library to parse it. Use Jackson's 'JsonNode' to navigate the varying JSON structure and extract 'city', 'country', and 'temperature' fields. Return a JSON string of the result.

정답：D,E

설명：
Option B is the most flexible and robust. External functions allow leveraging powerful scripting languages (like Python) for parsing and manipulating JSON data, handling variations gracefully. Option E is similarly valid, using Java and Jackson, which gives similar control and flexibility. Option A is less desirable due to the complexity of handling different data types and missing fields directly within SQL. Option C is limited because it relies on predefined paths and doesn't easily handle variations in the JSON structure. Option D is not suitable since 'COPY INTO does not directly support URLs.

질문 # 253
You have a Snowflake Task that is designed to transform and load data into a target table. The task relies on a Stream to detect changes in a source table. However, you notice that the task is intermittently failing with a 'Stream STALE' error, even though the data in the source table is continuously updated. What are the most likely root causes and the best combination of solutions to prevent this issue? (Select TWO)

A. The Stream has reached its maximum age (default 14 days) and expired. There is no way to recover data from an expired Stream. You need to recreate the Stream and reload the source table.
B. DML operations (e.g., UPDATE, DELETE) being performed on the source table are affecting rows older than the Stream's retention period. Reduce the stream's 'DATA RETENTION TIME IN DAYS' to match the oldest DML operation on the source table.
C. The Stream is not configured with 'SHOW INITIAL ROWS = TRUE, causing initial changes to be missed and eventually leading to staleness. Recreate the stream with this parameter set to TRUE.
D. The Task is not running frequently enough, causing the Stream to accumulate too many changes before being consumed, exceeding its retention period. Increase the task's execution frequency or increase the stream's 'DATA RETENTION TIME IN DAYS
E. The source table is being modified with DDL operations (e.g., ALTER TABLE ADD COLUMN), which are not supported by Streams. Use Table History to track schema changes and manually adjust the Stream's query if needed. Use 'COPY GRANTS' during the DDL.

정답：D,E

설명：
A Stream becomes stale if its offset is beyond the retention period. If the task isn't running often enough (B), the Stream can exceed the retention period before being consumed. DDL operations (C) invalidate Streams. Option A is incorrect because SHOW INITIAL ROWS only impacts the first read, not staleness. D is partially incorrect. While Streams do have a maximum age, increasing the retention period or running the task more frequently is preferred. E is wrong because decreasing retention won't help prevent the error and only lead to data losses.

질문 # 254
......

Snowflake DEA-C02시험은 Itcertkr 에서 출시한Snowflake DEA-C02덤프로 도전하시면 됩니다. Snowflake DEA-C02 덤프를 페펙트하게 공부하시면 시험을 한번에 패스할수 있습니다. 구매후 일년무료 업데이트 서비스를 제공해드리기에Snowflake DEA-C02시험문제가 변경되어도 업데이트된 덤프를 받으면 가장 최신시험에 대비할수 있습니다.

DEA-C02최고패스자료: https://www.itcertkr.com/DEA-C02_exam.html

덤프의 세가지 버전, 하지만 모두 다 알고계시는그대로Snowflake인증DEA-C02시험은 간단하게 패스할 수 있는 시험이 아닙니다, 다같이 Snowflake DEA-C02덤프로 시험패스에 주문걸어 보아요, 우리Itcertkr DEA-C02최고패스자료에서 제공되는 모든 덤프들은 모두 100%보장 도를 자랑하며 그리고 우리는 일년무료 업데이트를 제공합니다, 아직도Snowflake DEA-C02시험 때문에 밤잠도 제대로 이루지 못하면서 시험공부를 하고 있습니까, 이건 모두 Itcertkr DEA-C02최고패스자료 인증시험덤프로 공부하였기 때문입니다, Snowflake DEA-C02유효한 시험 믿고 애용해주신 분들께 감사의 인사를 드립니다.

마주 선 두 사람 사이에 묘한 분위기가 흘렀다, 왜, 무슨 문제 있어, 덤프의 세가지 버전, 하지만 모두 다 알고계시는그대로Snowflake인증DEA-C02시험은 간단하게 패스할 수 있는 시험이 아닙니다, 다같이 Snowflake DEA-C02덤프로 시험패스에 주문걸어 보아요.

DEA-C02유효한 시험 완벽한 시험 최신버전 덤프

우리Itcertkr에서 제공되는 모든 덤프들은 모두 100%보장 도를 자랑하며 그리고 우리는 일년무료 업데이트를 제공합니다, 아직도Snowflake DEA-C02시험 때문에 밤잠도 제대로 이루지 못하면서 시험공부를 하고 있습니까?

Sam Shaw Sam Shaw

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DEA-C02유효한시험시험준비에가장좋은기출문제

100% 유효한 DEA-C02유효한 시험 최신덤프자료

최신 SnowPro Advanced DEA-C02 무료샘플문제 (Q249-Q254):

DEA-C02유효한 시험 완벽한 시험 최신버전 덤프

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