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高品質なDEA-C02日本語解説集試験-試験の準備方法-有効的なDEA-C02出題範囲

このラインのプロのモデル企業として、DEA-C02トレーニング資料の成功は予見できる結果になります。 一部の厳選された顧客でさえ、彼らの高品質と正確さの実践をやめることはできません。 DEA-C02のexma質問の質には非妥協的であり、あなたは彼らの習熟度を厳しく完全に確信することができます。 長年の訂正と修正を受けて、DEA-C02試験問題はすでに完璧になっています。 DEA-C02トレーニングガイドの合格率は99％〜100％です。

DEA-C02学習ガイドの高品質と高効率は、同じ業界の製品で際立っています。私たちの教材は常にユーザーのために考慮されています。 DEA-C02試験問題を選択すると、より良い自己になります。 DEA-C02実際の試験では、輝かしい未来に貢献したいと考えています。私たちの教材は常に改善されています。良いアイデアがあれば、私たちの教材は喜んで受け入れます。 DEA-C02試験資料は、このファミリーに参加するパートナーを増やすことを楽しみにしています。私たちは一緒に進歩し、より良くなります。

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DEA-C02出題範囲 & DEA-C02無料過去問

MogiExamはあなたの１００パーセントの合格率を保証します。例外がないです。いまMogiExamを選んで、あなたが始めたいトレーニングを選んで、しかも次のテストに受かったら、最も良いソース及び市場適合性と信頼性を得ることができます。MogiExamのSnowflakeのDEA-C02問題集と解答はDEA-C02認定試験に一番向いているソフトです。

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) 認定 DEA-C02 試験問題 (Q79-Q84):

質問 # 79
You are loading JSON data into a Snowflake table with a 'VARIANT' column. The JSON data contains nested arrays with varying depths. You need to extract specific values from the nested arrays and load them into separate columns in your Snowflake table. Which approach would provide the BEST performance and flexibility?

• A. Create a view with nested 'FLATTEN' functions to extract the values from the 'VARIANT column. The view serves as the source for further transformations.
• B. Use a stored procedure to parse the JSON data and insert values into the table row by row.
• C. Load the entire JSON into a 'VARIANT column and then use SQL with nested 'FLATTEN' functions to extract the desired values during query time.
• D. Use a 'COPY' command with a 'TRANSFORM' clause that uses JavaScript UDFs to parse the JSON and extract the values during the load process. Load the extracted values directly into the target columns.
• E. Use Snowpipe with auto-ingest, loading directly into the table with the 'VARIANT column. Define data quality checks with pre-load data transformation.

正解：D

解説：
Using a 'COPY command with a 'TRANSFORM' clause and JavaScript UDFs allows for efficient parsing and extraction of values during the load process. This minimizes the amount of data stored in the 'VARIANT column and avoids expensive query-time parsing. Stored procedures perform row by row operations which are inefficient. Using Flatten functions could be useful to denormalise json, but javascript parsing during load is better. Snowpipe and auto-ingest just move the challenge to a real-time streaming scenario, which may not be optimized for transforming data into a relational structure.

 

質問 # 80
A data engineering team is implementing a change data capture (CDC) process using Snowflake Streams on a table 'CUSTOMER DATA'. After several days, they observe that some records are missing from the target table after the stream is consumed. The stream 'CUSTOMER DATA STREAM' is defined as follows: 'CREATE STREAM CUSTOMER DATA STREAM ON TABLE CUSTOMER DATA;' and the transformation code to process the data is shown below. What could be the possible reasons for the missing records, considering the interaction between Time Travel and Streams? Assume all table sizes are significantly larger than micro-partitions, making full table scans inefficient.

• A. DML operations (e.g., DELETE, UPDATE) performed directly against the target table are interfering with the stream's ability to track changes consistently.
• B. The underlying table 'CUSTOMER DATA' was dropped and recreated with the same name, invalidating the stream's tracking capabilities.
• C. The stream's 'AT' or 'BEFORE clause in the consumer query is incorrectly configured, causing it to skip some historical changes.
• D. The stream's offset persistence is reliant on Time Travel, If the data being ingested is older than the set Time Travel duration, the change may not be seen by the stream.
• E. The parameter for the database containing 'CUSTOMER_DATR is set to a value lower than the stream's offset persistence, causing some changes to be purged before the stream could consume them.

正解：D、E

解説：
Option A is correct: If the 'DATA RETENTION_TIME IN DAYS' is less than the time it takes to consume the stream, Time Travel will not be able to retrieve the changes, leading to missing records. Option E is also correct, as time travel duration plays a significant role.

 

質問 # 81
A data engineering team is implementing a data governance strategy in Snowflake. They need to track the lineage of a critical table 'SALES DATA' from source system ingestion to its final consumption in a dashboard. They have implemented masking policies on sensitive columns in 'SALES DATA. Which combination of Snowflake features and actions will MOST effectively allow them to monitor data lineage and object dependencies, including visibility into masking policies?

• A. Utilize Snowflake's Data Governance features, specifically enabling Data Lineage using Snowflake Horizon and utilize the view along with query the 'QUERY HISTORY view. These features natively track data flow and policy application.
• B. Enable Account Usage views like 'QUERY_HISTORY, and 'ACCESS_HISTORY. These views directly show table dependencies and policy applications.
• C. Create a custom metadata repository and use Snowflake Scripting to parse query history and object metadata periodically. Manually track dependencies and policy changes by analyzing the output.
• D. Use the INFORMATION_SCHEMA views like 'TABLES', 'COLUMNS', and 'POLICY_REFERENCES'. These views, combined with custom queries to analyze query history logs, will provide a complete lineage and masking policy overview.
• E. Rely solely on a third-party data catalog tool that integrates with Snowflake's metadata API. These tools automatically track lineage and policy information and provide the best and most effective results.

正解：A

解説：
Snowflake Horizon's Data Lineage feature is designed to track the flow of data through your Snowflake environment. Combining this with 'POLICY_REFERENCES (which shows which policies are applied to which objects) and (to see how data is transformed) provides the most complete and native solution. Account Usage views and INFORMATION_SCHEMA views provide valuable metadata, but don't offer lineage tracking out-of-the-box like Snowflake Horizon. While third-party tools and custom solutions are options, leveraging Snowflake's native capabilities is generally more efficient and cost-effective for basic lineage tracking.

 

質問 # 82
You have a table named 'EVENT LOGS with columns including 'EVENT ID, 'EVENT TIMESTAMP', 'USER ID, 'EVENT_TYPE, and 'EVENT DATA (which stores JSON data). Users frequently query the table filtering by specific key-value pairs within the 'EVENT DATA column. Which of the following approaches will BEST improve query performance when filtering on values inside the JSON column, considering the use of search optimization?

• A. Convert the ' EVENT_DATX column to a VARCHAR column and enable search optimization on it.
• B. Extract the frequently queried key-value pairs from the 'EVENT_DATR JSON into separate virtual columns and enable search optimization on these virtual columns.
• C. Increase the warehouse size.
• D. Create a materialized view that extracts the key-value pairs from the ' EVENT_DATX column and enable search optimization on the materialized view's columns.
• E. Enable search optimization directly on the 'EVENT DATA' column.

正解：B

解説：
Extracting the frequently queried key-value pairs into separate virtual columns and then enabling search optimization on these columns is the most effective approach (Option B). Snowflake's search optimization works best on columns with well-defined data types. Direct search optimization on a JSON column (Option A) is not directly supported and will not provide the desired performance benefits. Materialized views (Option C) are an option, but virtual columns are generally more lightweight for this scenario. Converting to VARCHAR (Option D) is not the correct approach for JSON data and would prevent proper JSON parsing and filtering. Increasing the warehouse size (Option E) might improve overall performance but doesn't specifically address the JSON filtering bottleneck.

 

質問 # 83
You need to unload data from a Snowflake table named 'CUSTOMER DATA to an AWS S3 bucket The data should be unloaded in Parquet format, partitioned by the 'CUSTOMER REGION' column, and automatically compressed with GZIP. Furthermore, you only want to unload customers whose 'REGISTRATION DATE is after '2023-01-01'. Which of the following 'COPY INTO' statement correctly achieves this?

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

正解：C

解説：
The correct 'COPY INTO' statement requires using a named stage and a named file format. A subquery is used to filter the data based on the 'REGISTRATION_DATE. The 'PARTITION BY clause is used to partition the data by 'CUSTOMER REGION'. You must create a FILE FORMAT seperately and refer to it later. Other solutions have syntax errors, incorrect stage references, or incorrect ordering of clauses. Option 'A' doesn't use a stage nor it allows for a where condition. Option 'B' doesn't work since 'TYPE' and 'COMPRESSIONS are properties of a file format, not direct arguments to FILE_FORMAT. 'C' includes the 'TYPE and 'COMPRESSION' inline when this is not allowed. 'D' contains the same error with FILE FORMAT as 'C' and 'B' and does not use a stage.

 

質問 # 84
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SnowflakeのDEA-C02試験準備は、テストヒット率が高いため、98％〜100％の合格率です。 したがって、当社のDEA-C02学習教材は効果的であるだけでなく、有用でもあります。 誰もが知っているように、時間は誰にとっても非常に重要です。 一部の候補者は、自分の仕事や家族で非常に忙しいです。 DEA-C02試験の審査に時間をかけることは非常に困難です。 ただし、DEA-C02試験の教材を使用する場合、学習する時間はほとんどなく、SnowPro Advanced: Data Engineer (DEA-C02)合格率は高くなります。 DEA-C02学習教材はあなたの信頼に値します。

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