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DEA-C02模擬モードを選択し,SnowPro Advanced: Data Engineer (DEA-C02)に合格する

Snowflake試験に実際に参加して資料を選択する前に、このようなDEA-C02証明書を保持することの重要性を思い出してください。 このようなSnowflake証明書を取得すると、昇給、昇進の機会、上司や同僚からの信頼など、将来の多くの同意結果を習得するのに役立ちます。 これらすべての快い結果は、もはやあなたにとってDEA-C02夢ではありません。 そして、SnowflakeのDEA-C02試験準備の助けを借りて、DEA-C02成績を改善し、人生の状態を変え、キャリアの驚くべき変化を得ることができます。 すべてはSnowflakeの学習質問から始まります。

最近では、コンピューター支援ソフトウェアを使用してDEA-C02試験に合格することが新しいトレンドになっています。新しい技術には明確な利点があるため、便利で包括的です。この傾向を追うために、当社の製品はDEA-C02試験問題を提供しており、従来の方法と斬新な方法を組み合わせて学習することができます。教材の合格率は最大99％です。一度にDEA-C02認定資格を取得できない場合は、目標に到達して夢が実現するまで、さまざまな割引でDEA-C02製品を無制限に使用できます。

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従来の試験によってPassTest が今年のSnowflakeのDEA-C02認定試験を予測してもっとも真実に近い問題集を研究し続けます。PassTestは100%でSnowflakeのDEA-C02「SnowPro Advanced: Data Engineer (DEA-C02)」認定試験に合格するのを保証いたします。

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) 認定 DEA-C02 試験問題 (Q278-Q283):

質問 # 278
You are tasked with implementing row-level security (RLS) on a 'SALES' table to restrict access based on the 'REGION' column. Users with the 'NORTH REGION ROLE should only see data where 'REGION = 'NORTH". You've created a row access policy named north_region_policy'. After applying the policy to the 'SALES table, users with the 'NORTH REGION ROLE are still seeing all rows.
Which of the following is the MOST likely reason for this and how can it be corrected?

• A. The ' does not have the USAGE privilege on the database and schema containing the 'SALES' table. Grant the USAGE privilege to the role.
• B. The policy function within is not using the correct context function to determine the user's role. It should use 'CURRENT ROLE()' instead of 'CURRENT_USER()'
• C. The policy needs to be explicitly refreshed. Execute 'REFRESH ROW ACCESS POLICY north_region_policy ON SALES;'
• D. The is not enabled. Execute 'ALTER ROW ACCESS POLICY ON SALES SET ENABLED = TRUE;'
• E. The user has not logged out and back in since the role was granted to them. Force the user to re-authenticate.

正解：A

解説：
Row access policies require the role to have USAGE privilege on the database and schema. Without this privilege, the policy cannot be enforced. The other options, while potentially relevant in other scenarios, are not the most likely cause for the described issue. Row access policies are automatically enabled when applied and the correct context function would be CURRENT_ROLE(). A refresh command is not required.

 

質問 # 279
You're designing a Snowpark data transformation pipeline that requires running a Python function on each row of a large DataFrame. The Python function is computationally intensive and needs access to external libraries. Which of the following approaches will provide the BEST combination of performance, scalability, and resource utilization within the Snowpark architecture?

• A. Create a Snowpark UDF using input_types=[StringType()], return_type=StringType())' and apply it to the DataFrame using
• B. Load the DataFrame into a Pandas DataFrame using and then apply the Python function using Pandas DataFrame operations.
• C. Use 'DataFrame.foreach(lambda row: my_python_function(row))' to iterate through each row and apply the Python function.
• D. Create a Snowpark UDTF using gudtf(output_schema=StructType([StructField('result', StringType())]), and apply it to the DataFrame using with a lateral flatten operation.
• E. Define a stored procedure in Snowflake and use it to execute the Python code on each row by calling it in a loop.

正解：A、D

解説：
Options B and D are the best choices. UDFs and UDTFs allow you to leverage Snowflake's compute resources for parallel processing. The function execution happens on Snowflake's servers, close to the data, minimizing data transfer. By specifying 'packages=['my_package']' , you ensure that the external libraries are available in the execution environment. A UDF is suitable for one-to-one row transformations, while a UDTF is more appropriate if the Python function needs to return multiple rows for each input row (one-to-many). Option A, DataFrame.foreacW , is inefficient for large DataFrames as it processes rows sequentially. Option C, loading into Pandas, is also not ideal as it can lead to out-of-memory errors for very large DataFrames and transfers the data to the client machine. Option E, stored procedures with loops, is less scalable and efficient than UDFs or UDTFs.

 

質問 # 280
You have a Snowflake task that executes a complex stored procedure. This stored procedure performs several UPDATE statements on a large table. After enabling the 'QUERY TAG' parameter, you notice that the task history in Snowflake shows frequent suspensions due to exceeding warehouse resource limits. The warehouse is already scaled to the largest size. Which combination of the following strategies would BEST address this issue and minimize task suspensions, assuming you CANNOT further scale the warehouse?

• A. Set a lower 'SUSPEND TASK AFTER N FAILURES' value to proactively suspend the task before it consumes excessive resources.
• B. Optimize the UPDATE statements in the stored procedure to reduce resource consumption by using techniques such as clustering keys, partitioning and avoiding full table scans.
• C. Break down the stored procedure into smaller, more manageable transactions and commit changes more frequently. Consider utilizing batch processing techniques.
• D. Increase the 'ERROR ON N parameter for the task to allow for more consecutive timeouts before the task is suspended.
• E. Implement a retry mechanism within the task's SQL code to automatically retry failed UPDATE statements after a short delay.

正解：B、C

解説：
Options A and C are the most effective strategies. Breaking down the stored procedure into smaller transactions (Option A) reduces the resource footprint of each transaction and allows the warehouse to process them more efficiently. Optimizing the UPDATE statements (Option C) directly addresses the root cause of the resource consumption. Option B only delays the inevitable suspension and does not address the underlying resource issue. Option D can lead to resource exhaustion if the retries continue to fail. Option E might prevent total resource exhaustion but doesn't address optimization.

 

質問 # 281
You are tasked with managing a large Snowflake table called 'TRANSACTIONS'. Due to compliance requirements, you need to archive data older than one year to long-term storage (AWS S3) while ensuring the queries against the current 'TRANSACTIONS' table remain performant. What is the MOST efficient strategy using Snowflake features and considering minimal impact on query performance?

• A. Create an external table pointing to S3. Then create new table named 'TRANSACTIONS_ARCHIVE in Snowflake, copy the historical data from 'TRANSACTIONS' table into 'TRANSACTIONS ARCHIVE, and then delete the archived data from the 'TRANSACTIONS' table.
• B. Create a new table 'TRANSACTIONS_ARCHIVE in Snowflake, copy the historical data, and then delete the archived data from the 'TRANSACTIONS table.
• C. Use Time Travel to clone the "TRANSACTIONS' table to a point in time one year ago. Then, export the cloned table to S3 and drop the cloned table. Delete the archived data from the 'TRANSACTIONS table.
• D. Partition the 'TRANSACTIONS table by date. Export the old partitions of the 'TRANSACTIONS' table to S3 using COPY INTO. Then, drop the old partitions from the 'TRANSACTIONS table and create an external table that points to the data in S3.
• E. Export the historical data to S3 using COPY INTO, truncate the 'TRANSACTIONS' table, and then create an external table pointing to the archived data in S3.

正解：E

解説：
Option B is the most efficient. Using 'COPY INTO' to export to S3 is a fast and optimized way to move data. Truncating the table is faster than deleting a large number of rows. Creating an external table allows you to query the archived data in S3 if needed, without ingesting it into Snowflake. Options A & C create another Snowflake table which will consume snowflake credits and storage, which might be costly for a long term storage. Option D cloning is an expensive operation. Option E Partitioning in Snowflake is not natively supported, and would require manual management using external tables and views.

 

質問 # 282
You are a data engineer responsible for data governance in a Snowflake environment. Your company has implemented data classification using tags to identify sensitive data'. The compliance team has requested a report detailing all tables and columns that contain PII data, specifically including the tag name, tag value, the fully qualified name of the table, and the column name. You have the necessary privileges to access the Snowflake metadata views. Which of the following queries would provide the MOST comprehensive and accurate report, considering performance and ease of understanding?

• A.
• B.
• C.
• D.
• E.

正解：B

解説：
Option D provides the MOST comprehensive and accurate report. It directly queries the view, filtering for 'TAG_NAME = 'PII" and 'object_domain = 'COLUMN" to specifically target tags applied to columns. It selects the database, schema, table name, column name, tag name, and tag value, providing all the necessary information. Option A requires a JOIN between "snowflake.account_usage.columns' and , which is unnecessary for this use case and less efficient. option B is missing the OBJECT_DATABASE and OBJECT_SCHEMA which is needed to fully qualify the table. option C attempts to use a table function, which is unnecessary complexity and potentially less performant. Option E does not filter for column-level tags, potentially including tags applied to other object types (e.g., tables, views), leading to inaccurate results. The fully qualified name can be easily constructed from OBJECT DATABASE, OBJECT SCHEMA and OBJECT NAME.

 

質問 # 283
......

当社Snowflakeでは、多くの分野の専門家を雇用してDEA-C02学習ガイドを作成しているため、学習教材の品質を安心してご利用いただけます。 さらに、DEA-C02試験問題のガイダンスに基づいて試験の準備をすることで、PassTest近い将来昇進する機会を増やし、給与を引き上げることができます。 したがって、SnowPro Advanced: Data Engineer (DEA-C02)試験を受ける準備ができたら、DEA-C02学習教材を利用できます。 次の受益者になりたい場合、何を待っていますか？ DEA-C02学習教材を購入してください。

DEA-C02ウェブトレーニング: https://www.passtest.jp/Snowflake/DEA-C02-shiken.html

DEA-C02の参考資料は、間違いを訂正し、何度も何度も間違いを避けるためにあなたを追跡するのに役立つためです、専門家と作業スタッフの全員が高い責任感を維持しているため、DEA-C02試験の資料を選択して長期的なパートナーになる人が非常に多くいます、Snowflake DEA-C02模擬モード それでは、どのようにすればそれを達成できますか、形式に固執することなく、DEA-C02学習クイズは5分以内に取得できます、Snowflake DEA-C02模擬モード 世界経済の急速な発展とさまざまな国との頻繁な接触により、すべての人々にとって良い仕事を探すことはますます難しくなっています、DEA-C02トレーニングブレインダンプの優れた品質と高い合格率のため、私たちは常にここにいます。

言わないかな、本の抜粋からの重要な引用： これらの新しい現実に適応することが急務です、DEA-C02の参考資料は、間違いを訂正し、何度も何度も間違いを避けるためにあなたを追跡するのに役立つためです、専門家と作業スタッフの全員が高い責任感を維持しているため、DEA-C02試験の資料を選択して長期的なパートナーになる人が非常に多くいます。

わかりやすい DEA-C02 合格教本

それでは、どのようにすればそれを達成できますか、形式に固執することなく、DEA-C02学習クイズは5分以内に取得できます、世界経済の急速な発展とさまざまな国との頻繁な接触により、すべての人々にとって良い仕事を探すことはますます難しくなっています。

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