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DEA-C02유효한시험 & DEA-C02합격보장가능덤프자료

우리ExamPassdump에는 아주 엘리트한 전문가들로 구성된 팀입니다. 우리는 아주 정확하게 또한 아주 신속히Snowflake DEA-C02관한 자료를 제공하며, 업데이트될경우 또한 아주 빠르게 뉴버전을 여러분한테 보내드립니다. ExamPassdump는 관련업계에서도 우리만의 브랜드이미지를 지니고 있으며 많은 고객들의 찬사를 받았습니다. 현재Snowflake DEA-C02인증시험패스는 아주 어렵습니다, 하지만 ExamPassdump의 자료로 충분히 시험 패스할 수 있습니다.

ExamPassdump는 고품질의 IT Snowflake DEA-C02시험공부자료를 제공하는 차별화 된 사이트입니다. ExamPassdump는Snowflake DEA-C02응시자들이 처음 시도하는Snowflake DEA-C02시험에서의 합격을 도와드립니다. 가장 적은 시간은 투자하여 어려운Snowflake DEA-C02시험을 통과하여 자격증을 많이 취득하셔서 IT업계에서 자신만의 가치를 찾으세요.

>> DEA-C02유효한 시험 <<

DEA-C02합격보장 가능 덤프자료, DEA-C02완벽한 공부문제

다른 사이트에서도Snowflake DEA-C02인증시험관련 자료를 보셨다고 믿습니다.하지만 우리 ExamPassdump의 자료는 차원이 다른 완벽한 자료입니다.100%통과 율은 물론ExamPassdump을 선택으로 여러분의 직장생활에 더 낳은 개변을 가져다 드리며 ,또한ExamPassdump를 선택으로 여러분은 이미 충분한 시험준비를 하였습니다.우리는 여러분이 한번에 통과하게 도와주고 또 일년무료 업데이트서비스도 드립니다.

최신 SnowPro Advanced DEA-C02 무료샘플문제 (Q34-Q39):

질문 # 34
You are tasked with implementing a data governance strategy in Snowflake for a large data warehouse. Your objective is to classify sensitive data columns, such as customer phone numbers and email addresses, using tags. You want to define a flexible tagging system that allows different levels of sensitivity (e.g., 'Confidential', 'Restricted') to be applied to various columns. Furthermore, you need to ensure that any data replicated to different regions maintains these classifications. Which of the following statements accurately describe best practices for implementing and maintaining data classification using tags in Snowflake, especially in a multi-region setup? Choose TWO.

A. Create a scheduled task that automatically identifies sensitive data based on regular expressions and applies the appropriate tags. This automates the classification process.
B. Always grant the ACCOUNTADMIN role to users who need to apply tags. This simplifies the process and ensures they have all necessary privileges.
C. Define tag schemas at the account level and replicate them to all regions. This ensures consistency of tag definitions across the entire organization.
D. Tags and tag values must be uniquely defined across all schemas to avoid conflicts and ensure accurate data classification; Snowflake enforces uniqueness implicitly.
E. When replicating data between regions, the tags are automatically replicated along with the data, provided that replication is configured using database replication or failover groups including the tagging schema.

정답：C,E

설명：
Defining tag schemas at the account level (Option A) ensures consistency in tag definitions across the entire Snowflake account, including all regions. This is a best practice for managing tags in a multi-region environment. When replicating data between regions (Option C) using database replication or failover groups, the tags are automatically replicated along with the data, assuming the tagging schema is included in the replication configuration. Option B describes a valid approach to tag application automation, but it isn't a core best practice related to multi- region replication and tag management. Option D is incorrect because granting the ACCOUNTADMIN role provides excessive privileges and is not a recommended practice. Option E is incorrect because tag names need only be unique within their schema.

질문 # 35
You're tasked with building a data pipeline using Snowpark Python to incrementally load data into a target table 'SALES SUMMARY from a source table 'RAW SALES. The pipeline needs to ensure that only new or updated records from 'RAW SALES are merged into 'SALES SUMMARY' based on a 'TRANSACTION ID'. You want to use Snowpark's 'MERGE' operation for this, but you also need to handle potential conflicts and log any rejected records to an error table 'SALES SUMMARY ERRORS'. Which of the following approaches offers the MOST robust and efficient solution for handling errors and ensuring data integrity within the MERGE statement?

A. Employ the 'MERGE statement with 'WHEN MATCHED THEN UPDATE' and 'WHEN NOT MATCHED THEN INSERT clauses, and use a stored procedure that executes the 'MERGE statement and then conditionally inserts rejected records into the 'SALES SUMMARY ERRORS' table based on criteria defined within the stored procedure. This will use the table function on the output.
B. Use a single 'MERGE statement with 'WHEN MATCHED THEN UPDATE and 'WHEN NOT MATCHED THEN INSERT clauses. Capture rejected records by leveraging the ' SYSTEM$PIPE STATUS function after the 'MERGE operation to identify rows that failed during the merge.
C. Utilize the 'WHEN MATCHED THEN UPDATE and 'WHEN NOT MATCHED THEN INSERT clauses with a 'WHERE' condition in each clause to filter out potentially problematic records. Log these filtered records to using a separate 'INSERT statement after the 'MERGE operation.
D. Use the 'WHEN MATCHED THEN UPDATE' clause to update existing records and the 'WHEN NOT MATCHED THEN INSERT clause to insert new records. Implement a separate process to periodically compare 'SALES_SUMMARY with 'RAW_SALES' to identify and log any inconsistencies.
E. Incorporate an 'ELSE clause in the 'MERGE' statement to capture records that do not satisfy the update or insert conditions due to data quality issues. Use this 'ELSE clause to insert rejected records into 'SALES SUMMARY ERRORS'

정답：A

설명：
Option E provides the most robust solution. Using a stored procedure to execute the MERGE allows for more complex error handling logic. Critically, the result_scan function of the MERGE query can then be used to identify and analyze the success or failure of each individual record processed within the MERGE. This avoids separate processes or post-merge comparisons and is therefore more robust. Option A requires a separate process for inconsistency checking, which is less efficient and may miss real-time errors. Options B, C, and D do not offer a reliable and atomic way to capture and log all rejected records. The SYSTEM$PIPE_STATUS function is relevant for Snowpipe, not direct MERGE operations.

질문 # 36
A company stores raw clickstream data in AWS S3. They need to query this data occasionally (less than once per day) for ad-hoc analysis and auditing purposes without ingesting it into Snowflake. Which of the following approaches is MOST suitable and cost- effective, and which considerations regarding data freshness are crucial?

A. Load the data into a Snowflake table using COPY INTO and schedule a daily task to refresh the table. Data freshness is guaranteed by the scheduled task.
B. Create an external table pointing directly to the S3 bucket. Data freshness is automatically maintained by Snowflake whenever the external table is queried. No additional configuration is required.
C. Create an external table pointing directly to the S3 bucket. The data freshness depends on manually refreshing the metadata using 'ALTER EXTERNAL TABLE ... REFRESH'. Automate this refresh with a scheduled task.
D. Create an external table pointing directly to the S3 bucket. The data freshness depends on the automatic refresh enabled using the object. Ensure a metadata notification service (e.g., SQS) is configured to trigger the metadata refresh whenever new files are added to S3.
E. Create an internal stage in Snowflake and regularly copy the data from S3 into the internal stage using a Snowpipe. Query the data in the internal stage. Data freshness depends on the Snowpipe configuration.

정답：D

설명：
External tables are ideal for infrequent queries of data residing in external storage. The 'EVENT_TABLE object provides an automatic refresh, but requires the setup of a notification service to trigger the refresh when new files are written to S3. Manually refreshing the metadata with "ALTER EXTERNAL TABLE ... REFRESH' is also a valid approach, but requires manual intervention or scheduling. Data freshness is NOT automatically maintained; a metadata refresh is REQUIRED.

질문 # 37
A data pipeline ingests clickstream data from various sources into a raw Snowflake table CRAW CLICKS). A transformation job then processes this data and loads it into a more structured 'CLICK EVENTS table, performing filtering, cleaning, and data enrichment. The data engineering team notices significant performance bottlenecks during this transformation process, leading to data freshness issues.
The team wants to optimize this process, considering the following:

A. Create a materialized view on top of 'RAW CLICKS' that pre-computes the necessary transformations and aggregations, allowing the 'CLICK EVENTS' table to be populated directly from the materialized view.
B. Use a larger virtual warehouse for the transformation job and partition the 'RAW CLICKS table on the ingestion timestamp to improve data pruning and reduce the amount of data processed during the transformation.
C. Replace the transformation job with a series of smaller, more specialized jobs, each running on a separate virtual warehouse optimized for the specific task, and orchestrate these jobs using a data pipeline tool.
D. Implement a change data capture (CDC) mechanism on the source systems to only ingest changed data into 'RAW CLICKS, reducing the overall data volume and the amount of data processed by the transformation job.
E. Optimize the transformation queries by identifying and rewriting inefficient SQL patterns, ensuring appropriate use of joins, filtering conditions, and data type conversions.

정답：A,D,E

설명：
Implementing CDC reduces the amount of data processed. Materialized views pre-compute results, avoiding repeated transformations. Optimizing SQL queries improves the efficiency of the transformation logic. Using a larger warehouse helps, but doesn't address the fundamental issue of processing unnecessary data. Breaking the transformation job into smaller jobs may add overhead and complexity without a clear performance benefit. Partitioning is a database term and clustering is snowflake, the question relates to snowflake.

질문 # 38
A data team is using Snowflake to analyze sensor data from thousands of IoT devices. The data is ingested into a table named 'SENSOR READINGS' which contains columns like 'DEVICE ID', 'TIMESTAMP', 'TEMPERATURE', 'PRESSURE', and 'LOCATION' (a GEOGRAPHY object). Analysts frequently run queries that calculate the average temperature and pressure for devices within a specific geographic area over a given time period. These queries are slow, especially when querying data from multiple months. Which of the following approaches, when combined, will BEST optimize the performance of these queries using the query acceleration service?

A. Enable search optimization on 'TEMPERATURE and 'PRESSURE columns and enable query acceleration.
B. Partition the ' SENSOR_READINGS table by 'TIMESTAMP (e.g., daily partitions). Enable search optimization on the 'LOCATION' column and enable query acceleration.
C. Create a materialized view that pre-calculates the average temperature and pressure by device and location. Then enable query acceleration on the virtual warehouse.
D. Enable Automatic Clustering on 'DEVICE_ID' , then enable query acceleration on the virtual warehouse.
E. Cluster the table by 'LOCATION' and 'TIMESTAMP , and enable search optimization on the 'LOCATION' column, and then enable query acceleration.

정답：E

설명：
Clustering by 'LOCATION' and 'TIMESTAMP' will group related data together, allowing Snowflake to quickly identify the relevant data for spatial queries. Enabling search optimization on the 'LOCATION' column allows queries filtering by geographic area to be accelerated. This combination provides the best performance because it addresses both the time-based and spatial aspects of the queries. Partitioning isn't directly supported by Snowflake but Clustering plays the equivalent role, and search optimization on Geography objects is critical. Materialized views can help, but might not be flexible enough for ad-hoc analysis. Automatic Clustering on 'DEVICE_ID won't help with spatial or time-based filtering. Search optimization on temperature and pressure will not help in spatial search.

질문 # 39
......

저희가 알아본 데 의하면 많은it인사들이Snowflake인증DEA-C02시험을 위하여 많은 시간을 투자하고 잇다고 합니다.하지만 특별한 학습 반 혹은 인터넷강이 같은건 선택하지 않으셨습니다.때문에 패스는 아주 어렵습니다.보통은 한번에 패스하시는 분들이 적습니다.우리 ExamPassdump에서는 아주 믿을만한 학습가이드를 제공합니다.우리 ExamPassdump에는Snowflake인증DEA-C02테스트버전과Snowflake인증DEA-C02문제와 답 두 가지 버전이 있습니다.우리는 여러분의Snowflake인증DEA-C02시험을 위한 최고의 문제와 답 제공은 물론 여러분이 원하는 모든 it인증시험자료들을 선사할 수 있습니다.

DEA-C02합격보장 가능 덤프자료: https://www.exampassdump.com/DEA-C02_valid-braindumps.html

IT업계에서 일자리를 찾고 계시다면 많은 회사에서는Snowflake DEA-C02있는지 없는지에 알고 싶어합니다, Snowflake DEA-C02유효한 시험 성공으로 향하는 길에는 많은 방법과 방식이 있습니다, Snowflake DEA-C02유효한 시험 현황에 만족하지 않고 열심히 하는 모습에 박수를 보내드립니다, ExamPassdump DEA-C02합격보장 가능 덤프자료는 여러분을 성공으로 가는 길에 도움을 드리는 사이트입니다, 그 외에 덤프자료가 항상 최신버전이기를 보장하기 위해 시험문제가 바뀌는 시점에 맞추어 자료를 업데이트하도록 최선을 다하고 있습니다.든든한 DEA-C02시험대비덤프만 마련하시면 DEA-C02시험패스는 바로 눈앞에 있습니다, Snowflake DEA-C02유효한 시험 더는 고민고민 하지마시고 덤프 받아가세요.

등 뒤는 온통 불과 얼음으로 가득 차 있었다, 오로지 층수를 알리는 불빛만이 윤곽을 구분할 수 있을 정도로 엘리베이터 안을 밝혔다, IT업계에서 일자리를 찾고 계시다면 많은 회사에서는Snowflake DEA-C02있는지 없는지에 알고 싶어합니다.

DEA-C02유효한 시험 인기 인증 시험덤프데모

성공으로 향하는 길에는 많은 방법과 방식이 있습니다, 현황에 만족하지DEA-C02않고 열심히 하는 모습에 박수를 보내드립니다, ExamPassdump는 여러분을 성공으로 가는 길에 도움을 드리는 사이트입니다, 그 외에 덤프자료가 항상 최신버전이기를 보장하기 위해 시험문제가 바뀌는 시점에 맞추어 자료를 업데이트하도록 최선을 다하고 있습니다.든든한 DEA-C02시험대비덤프만 마련하시면 DEA-C02시험패스는 바로 눈앞에 있습니다.

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