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DEA-C02全真問題集、DEA-C02日本語pdf問題

当社は、すべての受験者が試験に簡単に合格できるようにDEA-C02最新の練習教材を開発することに専念しており、10年以上の開発の後に大きな成果を上げています。認定資格は非常に価値が高いため、適切なDEA-C02試験ガイドは、バターを通過するホットナイフのようなDEA-C02試験に合格するための強力な推進力となります。そして、DEA-C02試験ガイドの質の高いDEA-C02学習ガイドは、98％以上の高い合格率によって証明されているため、DEA-C02試験問題はまさにあなたにとって正しいものです。

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DEA-C02日本語pdf問題、DEA-C02最新受験攻略

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) 認定 DEA-C02 試験問題 (Q280-Q285):

質問 # 280
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 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.
B. 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
C. 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.
D. 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.
E. 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.

正解：A、B

解説：
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.

質問 # 281
You are tasked with implementing a projection policy in Snowflake to restrict access to certain columns of the 'EMPLOYEE table based on the user's role. The table contains columns like 'EMPLOYEE 'NAME, 'SALARY', and 'DEPARTMENT. Users with the 'HR MANAGER role should have access to all columns, while other users should only be able to see 'EMPLOYEE ID, 'NAME, and DEPARTMENT. The initial attempt to create the projection policy results in an error. What could be the reasons?

A. The projection policy definition might contain syntax errors or reference non-existent roles or columns.
B. Projection policies can only be applied at the database level, not at the table level.
C. The user attempting to create the projection policy does not have the 'OWNERSHIP' privilege on the 'EMPLOYEE table.
D. Projection policies are not supported in Snowflake.
E. The EMPLOYEE table must have row-level security policies enabled before applying a projection policy.

正解：A、C

解説：
Options D and E are correct. To create a projection policy, the user needs the 'OWNERSHIP privilege on the table. A syntax error can cause an error. Option A is incorrect since projection policies are applied at the table or view level. Projection policies exist in Snowflake, and do not depend on Row Access Policies to be enabled so B and C are incorrect.

質問 # 282
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. Wrap the entire transformation in a stored procedure and call 'SYSTEM$QUERY within the stored procedure's exception handler.
D. Use True)' to automatically rollback the write operation if an error occurs during the write process.
E. Use and manually track intermediate dataframes to delete them in case of failure.

正解：B

解説：
Setting 'TRANSACTION_ABORT ON ERROR to 'TRUE ensures that any error will abort the transaction. Wrapping the code in a 'try...except' block allows you to catch the exception and explicitly call 'session.rollback()' to undo any changes made within the transaction. Option A is relevant to DDL operations, not general data transformations. Option B involves manual tracking, which is error-prone. Option D is not a valid Snowpark DataFrameWriter option. Option E, while potentially useful for cancelling queries, does not directly manage transaction rollback from within the Snowpark session.

質問 # 283
You are working with a directory table named associated with an external stage containing a large number of small JSON files. You need to process only the files containing specific sensor readings based on a substring match within their filenames (e.g., files containing 'temperature' in the filename). You also want to load these files into a Snowflake table 'sensor_readings. Consider performance and cost-effectiveness. Which of the following approaches is the MOST efficient and cost-effective to achieve this? Choose TWO options.

A. Load all files from the stage using 'COPY INTO' into a staging table, and then use a Snowflake task to filter and move the relevant records into the 'sensor_readingS table.
B. Use 'COPY INTO' with the 'PATTERN' parameter, constructing a regular expression that includes the substring match against the filename obtained from the directory table's 'relative_path' column.
C. Use a Python UDF to iterate through the files listed in , filter based on filename, and then load each matching file individually using the Snowflake Python Connector.
D. Create a masking policy based on filenames to control which files users can see.
E. Create a view on top of the directory table that filters the 'relative_patW based on the substring match, and then use 'COPY INTO' with the 'FILES' parameter to load the filtered files.

正解：B、E

解説：
Options B and C are the most efficient and cost-effective. Option B (Create a view and use COPY INTO with FILES): Creating a view that filters the directory table allows you to isolate the relevant filenames. Then, using 'COPY INTO' with the 'FILES' parameter pointing to this filtered view directly instructs Snowflake to load only the specified files, minimizing unnecessary data processing. This is efficient as it leverages Snowflake's built-in capabilities. Option C (COPY INTO with the PATTERN parameter): The 'PATTERN' parameter within the 'COPY INTO' command allows you to specify a regular expression. By incorporating the substring match into this regular expression against the metadata$filename" , you can directly filter which files are loaded during the 'COPY INTO operation. This avoids loading irrelevant data and is generally more performant than iterating through files using a UDF. Other options are less efficient or less cost-effective: Option A (Python UDF): Using a Python UDF for this task is generally less efficient. Snowflake is designed to handle this processing natively, and using UDF can lead to performance overhead due to data serialization and deserialization between Snowflake and the UDF environment. Option D (Load all and filter later): Loading all files into a staging table and then filtering is wasteful. It increases data processing time and costs since you're loading unnecessary data. It's always better to filter data closer to the source if possible. Option E (Masking Policy): Masking policies are for security, not data transformation. They are applied at the query level to prevent users from seeing data, but do not help in efficiently processing only specific files.

質問 # 284
You are tasked with loading data from a set of highly nested JSON files into Snowflake. Some files contain an inconsistent structure where a particular field might be a string in some records and an object in others. You want to avoid data loss and ensure that you capture both string and object representations of the field. What is the most efficient approach to achieve this, minimizing data transformation outside of Snowflake?

A. Create two separate external tables, one with the field defined as VARCHAR and another with the field defined as VARIANT. Load data into both, then UNION the results in a view.
B. Define the field in the external table as VARCHAR. During data loading, use a UDF written in Python or Java to handle the different data types, transforming objects to strings. This approach requires deploying the UDF to Snowflake.
C. Define the field as a VARCHAR in an internal stage and use a COPY INTO statement with the VALIDATE function to identify records with object representations. Load the valid VARCHAR values. Create a separate table for the invalid object representations identified during validation.
D. Pre-process the JSON files using a scripting language (e.g., Python) to transform object representations to string representations before loading them into Snowflake. This ensures consistent data type for the field.
E. Use a single external table with the field defined as VARIANT. During data loading, use the TRY CAST function within a SELECT statement to convert the field to VARCHAR when possible,V otherwise retain the VARIANT representation. Handle further processing in subsequent views or queries.

正解：E

解説：
Option B is the most efficient. Defining the field as VARIANT allows Snowflake to handle different data types within the same column. TRY CAST attempts to convert the field to VARCHAR if it's a string, and retains the VARIANT representation if it's an object, avoiding data loss. This approach minimizes the need for separate tables or external data processing. A, C, D and E involve either creating multiple objects, or external stage which are not efficient.

質問 # 285
......

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DEA-C02日本語pdf問題: https://www.it-passports.com/DEA-C02.html

Snowflake DEA-C02全真問題集数万人のお客様が弊社の試験資料の恩恵を受けて、簡単に試験に合格しました、DEA-C02学習教材を選択することは間違いなくあなたの正しい決断です、大切なのは、DEA-C02復習資料の合格率が高いで、多くの受験者がDEA-C02試験をパスしたということです、It-Passports DEA-C02日本語pdf問題ただし、DEA-C02日本語pdf問題 - SnowPro Advanced: Data Engineer (DEA-C02)の学習に関する質問はSnowflake DEA-C02日本語pdf問題その方法ではありません、DEA-C02模擬試験の合格率はほぼ100％ですが、合格しない可能性がある場合は、全額返金することができます、Snowflake DEA-C02全真問題集しかし、難しい試験といっても、試験を申し込んで受験する人が多くいます。

ではとにかく一通り、事情だけは話して見る事にしましょう、一体何があっ大丈夫です、数万人のお客様が弊社の試験資料の恩恵を受けて、簡単に試験に合格しました、DEA-C02学習教材を選択することは間違いなくあなたの正しい決断です。

高品質なDEA-C02全真問題集試験-試験の準備方法-有効的なDEA-C02日本語pdf問題

大切なのは、DEA-C02復習資料の合格率が高いで、多くの受験者がDEA-C02試験をパスしたということです、It-Passportsただし、SnowPro Advanced: Data Engineer (DEA-C02)の学習に関する質問はSnowflakeその方法ではありません、DEA-C02模擬試験の合格率はほぼ100％ですが、合格しない可能性がある場合は、全額返金することができます。

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