Snowflake SnowPro Advanced DEA-C02

퍼펙트한 서비스 제공

승진이나 연봉인상을 꿈꾸면 승진과 연봉인상을 시켜주는 회사에 능력을 과시해야 합니다. IT인증시험은 국제적으로 승인해주는 자격증을 취득하는 시험입니다. 시험을 패스하여 자격증을 취득하면 회사에서 꽃길만 걷게 될것입니다. DEA-C02인증시험덤프 덤프구매전 한국어 온라인 상담서비스부터 구매후 덤프 무료 업데이트버전 제공, DEA-C02인증시험 덤프 불합격시 덤프비용 전액환불 혹은 다른 과목으로 교환 등 저희는 구매전부터 구매후까지 철저한 서비스를 제공해드립니다. SnowPro Advanced: Data Engineer (DEA-C02)인증시험 덤프는 인기덤프인데 지금까지 DEA-C02덤프를 구매한후 불합격으로 인해 환불신청하신 분은 아직 한분도 없었습니다.

DEA-C02시험은 최근 제일 인기있는 인증시험입니다. IT업계에 종사하시는 분들은 자격증취득으로 자신의 가치를 업그레이드할수 있습니다. SnowPro Advanced: Data Engineer (DEA-C02) 시험은 유용한 IT자격증을 취득할수 있는 시험중의 한과목입니다. Snowflake SnowPro Advanced 최신버전 덤프는 여러분들이 한방에 시험에서 통과하도록 도와드립니다. DEA-C02덤프를 공부하는 과정은 IT지식을 더 많이 배워가는 과정입니다. DEA-C02시험대비뿐만아니라 많은 지식을 배워드릴수 있는 덤프를 공부하고 시험패스하세요.

시험준비시간 최소화

IT업계 엘리트한 강사들이 퍼펙트한 DEA-C02시험응시 SnowPro Advanced: Data Engineer (DEA-C02)덤프문제집을 제작하여 디테일한 DEA-C02문제와 답으로 여러분이 아주 간단히 SnowPro Advanced: Data Engineer (DEA-C02)시험응시를 패스할 수 있도록 최선을 다하고 있습니다.덤프구매후 2,3일 정도만 공부하시면 바로 시험보셔도 되기에 가장 짧은 시간을 투자하여 시험에서 패스할수 있습니다.

최신 업데이트버전 DEA-C02덤프

저희는 2,3일에 한번씩 DEA-C02덤프자료가 업데이트 가능한지 체크하고 있습니다. SnowPro Advanced: Data Engineer (DEA-C02)덤프가 업데이트된다면 업데이트된 버전을 고객님 구매시 사용한 메일주소로 발송해드립니다. DEA-C02 덤프 업데이트서비스는 구매일로부터 1년내에 유효함으로 1년이 지나면 DEA-C02덤프 업데이트서비스가 자동으로 종료됩니다. SnowPro Advanced: Data Engineer (DEA-C02)덤프 무료 업데이트 서비스를 제공해드림으로 고객님께서 구매하신 DEA-C02덤프 유효기간을 최대한 연장해드립니다.

최신 SnowPro Advanced DEA-C02 무료샘플문제:

1. You need to implement both a row access policy and a dynamic data masking policy on the 'EMPLOYEE table in Snowflake. The requirements are as follows: 1. Employees should only be able to see their own record in the 'EMPLOYEE table. 2. The 'SALARY' column should be masked for all employees except those with the 'HR ADMIN' role. Unmasked values are required for compliance reasons, they need to be available for 'HR ADMIN' role. Given the following table structure: CREATE TABLE EMPLOYEE ( EMPLOYEE ID INT, EMPLOYEE NAME STRING, SALARY NUMBER, EMAIL STRING ) ; Which of the following sets of steps correctly implement the row access policy and dynamic data masking policy?

A) Option C
B) Option B
C) Option A
D) Option D
E) Option E

2. You are responsible for monitoring the performance of several data pipelines in Snowflake that heavily rely on streams. You notice that some streams consistently lag behind the base tables. You need to proactively identify the root cause and implement solutions. Which of the following metrics and monitoring techniques would be MOST helpful in diagnosing and resolving the stream lag issue? (Select all that apply)

A) Regularly query the 'CURRENT_TIMESTAMP and columns of the stream to calculate the data latency.
B) Increase the 'DATA RETENTION TIME IN DAYS for the base tables to ensure that historical data is always available for the streams, even if they lag behind.
C) Analyze the query history in Snowflake to identify any long-running queries that are consuming data from the streams and potentially blocking new changes from being processed.
D) Monitor resource consumption (CPU, memory, disk) of the virtual warehouse(s) used for processing data from the streams.
E) Monitor the 'SYSTEM$STREAM HAS DATA function's output for the affected streams to quickly determine if there are pending changes.

3. You are developing a Snowpark Python application that needs to process data from a Kafka topic. The data is structured as Avro records. You want to leverage Snowpipe for ingestion and Snowpark DataFrames for transformation. What is the MOST efficient and scalable approach to integrate these components?

A) Convert Avro data to JSON using a Kafka Streams application before ingestion. Use Snowpipe to ingest the JSON data to a VARIANT column and then process it using Snowpark DataFrames.
B) Use Snowpipe to ingest the Avro data to a raw table stored as binary. Then, use a Snowpark Python UDF with an Avro deserialization library to convert the binary data to a Snowpark DataFrame.
C) Create external functions to pull the Avro data into a Snowflake stage and then read the data with Snowpark DataFrames for transformation.
D) Create a Kafka connector that directly writes Avro data to a Snowflake table. Then, use Snowpark DataFrames to read and transform the data from that table.
E) Configure Snowpipe to ingest the raw Avro data into a VARIANT column in a staging table. Utilize a Snowpark DataFrame with Snowflake's get_object field function on the variant to get an object by name, and create columns based on each field.

4. You are using Snowpark Python to perform a complex data transformation involving multiple tables and several intermediate dataframes. During the transformation, an error occurs within one of the Snowpark functions, causing the entire process to halt. To ensure data consistency, you need to implement transaction management. Which of the following Snowpark DataFrameWriter options or session configurations would be MOST appropriate for rolling back the entire transformation in case of an error during the write operation to the final target table?

A) Set the session parameter to 'TRUE to ensure all DDL operations are atomic and can be rolled back.
B) Set the session parameter to 'TRUE and wrap the entire transformation within a 'try...except block, explicitly calling in the 'excepts block.
C) Use True)' to automatically rollback the write operation if an error occurs during the write process.
D) Wrap the entire transformation in a stored procedure and call 'SYSTEM$QUERY within the stored procedure's exception handler.
E) Use and manually track intermediate dataframes to delete them in case of failure.

5. You are designing a data governance strategy for a Snowflake data warehouse. One of the key requirements is to track data lineage for sensitive data, specifically Personally Identifiable Information (PII). You need to understand how PII data flows through various transformations and tables. Which Snowflake feature, when combined with appropriate tagging and metadata management practices, can BEST help you achieve this?

A) Snowflake Data Masking policies
B) Snowflake Cloning
C) Snowflake Secure Data Sharing
D) Snowflake's Resource Monitors
E) Snowflake INFORMATION_SCHEMA views and Account Usage views

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