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正確的なDEA-C02最速合格試験-試験の準備方法-有効的なDEA-C02資格取得

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DEA-C02試験問題は、シラバスの変更および理論と実践の最新の進展に応じて完全に改訂および更新されます。高品質の製品を提供するために、DEA-C02テストガイドを慎重に準備します。製品のすべての改訂と更新により、DEA-C02ガイドトレントに関する正確な情報を取得でき、大多数の学生が簡単に習得でき、重要な情報の内容を簡素化できます。当社の製品DEA-C02テストガイドは、より重要な情報をより少ない質問と回答で提供します。

DEA-C02試験にすばやく合格できるようにする必要があるため、信頼できる製品を選択する必要があります。 DEA-C02試験の教材は当局によって認定されており、ユーザーによってテストされています。これは間違いなく自信を持って使用できる製品です。もちろん、私たちのデータはあなたをもっと安心させるかもしれません。 DEA-C02準備準備の合格率は99％に達しました。これは非常に信じられない価値ですが、私たちはそれを行いました。製品について詳しく知りたい場合は、スタッフに相談するか、DEA-C02練習エンジンの無料試用版をダウンロードしてください。ご参加をお待ちしております。

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PassTestのSnowflakeのDEA-C02問題集を選んだら、成功を選ぶのに等しいです。もしうちの学習教材を購入するなら、PassTestは一年間で無料更新サービスを提供することができます。PassTestのSnowflakeのDEA-C02認定試験の合格率は１００パーセントになっています。不合格になる場合或いはSnowflakeのDEA-C02問題集がどんな問題があれば、私たちは全額返金することを保証いたします。

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) 認定 DEA-C02 試験問題 (Q229-Q234):

質問 # 229
You are tasked with implementing a data recovery strategy for a critical table 'SALES DATA' in Snowflake. The table is frequently updated, and you need to ensure you can recover to a specific point in time in case of accidental data corruption. Which approach provides the most efficient and granular recovery option, minimizing downtime and data loss? Consider performance and storage implications of each method.

• A. Regularly creating full clones of the 'SALES_DATR table to a separate database.
• B. Using the 'UNDROP TABLE command in conjunction with the 'AT' clause to revert the table to a previous state.
• C. Creating a Snowflake Stream on 'SALES_DATR and capturing all DML changes for point-in-time recovery.
• D. Create a scheduled task that takes a snapshot of the sales data and store it to an external staging location.
• E. Relying solely on Snowflake's Time Travel feature with the default data retention period.

正解：C

解説：
Option C is correct: Streams provide a granular way to capture DML changes. While Time Travel provides recovery, a stream specifically captures the changes, allowing for more controlled replay and point-in-time recovery. Full clones are resource-intensive, "UNDROP" is for dropped tables, not general recovery, and relying solely on default Time Travel may not meet specific recovery time objectives.

 

質問 # 230
You are designing a data pipeline that requires applying a complex scoring algorithm to customer data in Snowflake. This algorithm involves multiple steps, including feature engineering, model loading, and prediction. You want to encapsulate this logic within a reusable component and apply it to incoming data streams efficiently. Which of the following approaches is most suitable and scalable for implementing this scoring logic as a UDF/UDTF, considering real-time data processing and low latency requirements?

• A. A JavaScript UDF that uses basic JavaScript functions to perform the entire scoring algorithm without external dependencies.
• B. A Python UDF that loads a pre-trained machine learning model (e.g., using scikit-learn) and performs predictions on the input data.
• C. A Java UDTF that leverages a custom Java library for feature engineering and model prediction, deployed as a JAR file to Snowflake's internal stage.
• D. A SQL UDF containing a series of nested CASE statements to implement the entire scoring algorithm.
• E. A Python UDTF using Snowpark, leveraging external libraries like 'torch' for accelerated calculations and ML model inference by GPU.

正解：E

解説：
Python UDTFs in Snowpark provide a powerful and scalable way to perform complex scoring algorithms, especially when combined with the GPU. Snowpark optimizes data processing within Snowflake's engine, and integration with Anaconda allows for using machine learning libraries such as scikit-learn or Pytorch for model loading and prediction and external libraries like 'torch' for accelerated calculations and ML model inference by GPU. SQL UDFs are not suitable for complex algorithms. JavaScript UDFs lack the necessary functionality and performance for advanced scoring. While Java UDTFs can be used, managing JAR files and potentially less efficient integration can be disadvantages. Using Python with SNOWPARK for GPU is suitable for real-time scoring and low latency.

 

質問 # 231
You are tasked with creating a resilient data ingestion pipeline using Snowpipe and external tables on AWS S3. The data consists of JSON files, some of which may occasionally contain invalid JSON structures (e.g., missing closing brackets, incorrect data types). You want to ensure that even if some files are corrupted, the valid data is still ingested into your target Snowflake table, and the corrupted files are logged for later investigation. Which of the following steps would BEST achieve this?

• A. Use Snowflake's => 'JSON', job_id => function against the external stage before ingesting data with Snowpipe to pre-validate files. Then ingest only validated files to your target table
• B. Set the 'ON ERROR option to 'ABORT STATEMENT in the Snowpipe definition. This will stop the entire Snowpipe process when a JSON error is detected, allowing you to manually investigate and fix the corrupted files before restarting the pipeline.
• C. Configure the external table definition with 'VALIDATION MODE = 'RETURN ERRORS" and then create a view on top of the external table that filters out rows where the 'METADATA$FILE ROW NUMBER column contains errors.
• D. Create a custom error handler using a Snowflake stored procedure that catches the 'JSON PARSER ERROR exception and logs the filename to a separate error table. Use the ERROR = 'CONTINUE" copy option in the Snowpipe definition.
• E. Configure Snowpipe to use the 'ON ERROR = 'SKIP FILE" copy option and then create a separate task to query the 'VALIDATION MODE metadata column in the external table to identify and log the corrupted files.

正解：E

解説：
Configuring ERROR = 'SKIP FILE'' will ensure that Snowpipe skips any file containing errors and continues processing other valid files. Using the 'VALIDATION MODE' metadata column in the external table allows you to identify which files were skipped due to errors. While custom error handlers could be used, using Snowpipe built-in feature with metadata column is more simpler and effective for the task. Validate function needs a job_id and it is not commonly used for external stages. 'ON ERROR = 'ABORT STATEMENT'' will cause pipeline to stop and hence is less preferable.

 

質問 # 232
A data engineering team is building a real-time dashboard in Snowflake to monitor website traffic. The dashboard relies on a complex query that joins several large tables. The query execution time is consistently exceeding the acceptable threshold, impacting dashboard responsiveness. Historical data is stored in a separate table and rarely changes. You suspect caching is not being utilized effectively. Which of the following actions would BEST improve the performance of this dashboard and leverage Snowflake's caching features?

• A. Increase the size of the virtual warehouse. A larger warehouse will have more resources to execute the query, and the results will be cached for a longer period.
• B. Replace the complex query with a series of simpler queries. This will reduce the amount of data that needs to be processed at any one time.
• C. Materialize the historical data into a separate table that utilizes clustering and indexing for faster query performance. Refresh this table periodically.
• D. Create a materialized view that pre-computes the results of the complex query. Snowflake will automatically refresh the materialized view when the underlying data changes.
• E. Use 'RESULT_SCAN' to cache the query result in the user session for subsequent queries. This is especially effective for large datasets that don't change frequently.

正解：D

解説：
Materialized views are the best option in this scenario. They pre-compute the results of the complex query and store them in a separate table. Snowflake automatically refreshes the materialized view when the underlying data changes, ensuring that the dashboard always displays the most up-to-date information. While increasing the virtual warehouse size (D) can help initially, it's a more expensive and less targeted solution. 'RESULT_SCAN' (A) is session-specific and not suitable for persistent caching for a dashboard accessed by multiple users. Materializing the historical data (B) might help, but it doesn't address the core issue of the complex query. Breaking the query into smaller parts (E) might not be efficient and can introduce complexity.

 

質問 # 233
A Snowflake Data Engineer is tasked with identifying all downstream dependencies of a view named 'CUSTOMER SUMMARY. This view is used by multiple dashboards and reports. They want to use SQL to efficiently find all tables and views that directly depend on 'CUSTOMER SUMMARY. Which of the following SQL queries against the ACCOUNT USAGE schema is the MOST efficient and accurate way to achieve this?

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

正解：E

解説：
The view in the ACCOUNT_USAGE schema is specifically designed to track object dependencies. option B directly queries this view using the and to find objects that depend on the 'CUSTOMER_SUMMARY view. Options A and C rely on parsing 'QUERY_TEXT , which is less reliable and can lead to false positives or misses. Option D looks for base object which has the opposite meaning of finding dependancies on a target view. Option E finds the OBJECT_ID for the view and is unnecessary and introduces an extra step.

 

質問 # 234
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