あなたはDEA-C02試験参考書の更新をどのぐらいでリリースしていますか？

すべての試験参考書は常に更新されますが、固定日付には更新されません。弊社の専門チームは、試験のアップデートに十分の注意を払い、彼らは常にそれに応じてDEA-C02試験内容をアップグレードします。

更新されたDEA-C02試験参考書を得ることができ、取得方法？

はい、購入後に1年間の無料アップデートを享受できます。更新があれば、私たちのシステムは更新されたDEA-C02試験参考書をあなたのメールボックスに自動的に送ります。

Tech4Examはどんな試験参考書を提供していますか？

テストエンジン：DEA-C02試験試験エンジンは、あなた自身のデバイスにダウンロードして運行できます。インタラクティブでシミュレートされた環境でテストを行います。
PDF（テストエンジンのコピー）：内容はテストエンジンと同じで、印刷をサポートしています。

あなたのテストエンジンはどのように実行しますか？

あなたのPCにダウンロードしてインストールすると、Snowflake DEA-C02テスト問題を練習し、'練習試験'と '仮想試験'2つの異なるオプションを使用してあなたの質問と回答を確認することができます。
仮想試験 - 時間制限付きに試験問題で自分自身をテストします。
練習試験 - 試験問題を1つ1つレビューし、正解をビューします。

購入後、どれくらいDEA-C02試験参考書を入手できますか？

あなたは5-10分以内にSnowflake DEA-C02試験参考書を付くメールを受信します。そして即時ダウンロードして勉強します。購入後にDEA-C02試験参考書を入手しないなら、すぐにメールでお問い合わせください。

返金するポリシーはありますか？ 失敗した場合、どうすれば返金できますか？

はい。弊社はあなたが我々の練習問題を使用して試験に合格しないと全額返金を保証します。返金プロセスは非常に簡単です：購入日から60日以内に不合格成績書を弊社に送っていいです。弊社は成績書を確認した後で、返金を行います。お金は７日以内に支払い口座に戻ります。

DEA-C02テストエンジンはどのシステムに適用しますか？

オンラインテストエンジンは、WEBブラウザをベースとしたソフトウェアなので、Windows / Mac / Android / iOSなどをサポートできます。どんな電設備でも使用でき、自己ペースで練習できます。オンラインテストエンジンはオフラインの練習をサポートしていますが、前提条件は初めてインターネットで実行することです。
ソフトテストエンジンは、Java環境で運行するWindowsシステムに適用して、複数のコンピュータにインストールすることができます。
PDF版は、Adobe ReaderやFoxit Reader、Google Docsなどの読書ツールに読むことができます。

割引はありますか？

我々社は顧客にいくつかの割引を提供します。 特恵には制限はありません。 弊社のサイトで定期的にチェックしてクーポンを入手することができます。

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

1. You have a Snowflake view that joins three large tables: ORDERS, CUSTOMERS, and PRODUCTS. The query accessing this view is frequently used but performs poorly. You suspect inefficient join processing and potential skew in the data'. Which of the following strategies can be used to optimize the view's performance? (Select all that apply)

A) Increase the virtual warehouse size to provide more resources for query processing. Convert the view into a table using CREATE TABLE AS SELECT (CTAS).
B) Use JOIN hints, such as BROADCAST or MERGE, to guide the query optimizer on join strategies.
C) Analyze the query profile to identify bottlenecks and potential data skew issues, and then re-cluster the underlying tables based on the most frequently used join keys.
D) Replace the view with a materialized view to precompute and store the results.
E) Partition the underlying tables based on the join keys to improve data locality.

2. You have a Snowflake table, 'raw_data', which contains a column 'data url' storing URLs pointing to CSV files with varying schemas. Each CSV file represents sales data, but the column names and data types can differ. You need to create a process to automatically discover the schema of each CSV file, load the data into Snowflake, and standardize the column names to 'order id', 'product id', 'quantity', and 'price'. Which of the following approaches best addresses this requirement, considering scalability and minimal manual intervention?

A) Use Snowpipe with auto-ingest to continuously load the CSV files into a VARIANT column in a staging table. Create a series of views on top of the staging table, each view attempting to extract data based on different potential schema variations. Union all the views together to create a single consolidated view.
B) Leverage a combination of Snowflake Scripting and External functions: create external function that infer the schema of the CSV, create temporary table based on identified schema, fetch the CSV data using SYSTEM$URL GET using snowflake scripting, copy the data into the temporary table, tranform the data into required structure, ingest into target table and finally drop the temporary table
C) Create a Snowflake external table that points to the external stage. Define a single file format to be used by external table. Define a pipe that uses 'COPY INTO' to ingest data into external table from the files found at the file URLs.
D) Create a stored procedure that iterates through each URL in 'raw_data' , downloads the CSV file using 'SYSTEM$URL_GET , parses the CSV header to determine the column names, manually maps the discovered column names to the standardized names, creates a temporary table with the discovered schema, loads the data into the temporary table, transforms the data to use the standardized column names, and then inserts the transformed data into a final target table. Drop the temporary table after successful insertion.
E) Create a Python-based external function that downloads the CSV file from the URL using a library like 'pandas', infers the schema using 'pandas.read_csv' , maps the discovered column names to the standardized names, and returns the data as a JSON string. Then, create a Snowflake table with a VARIANT column, call the external function for each URL, and load the returned JSON data into the table. Create a view on top of it.

3. A Snowflake data engineer is troubleshooting a slow-running query that joins two large tables, 'ORDERS' (1 billion rows) and 'CUSTOMER' (10 million rows), using the 'CUSTOMER ID' column. The query execution plan shows a significant amount of data spilling to local disk. The query is as follows:

Which of the following are the MOST likely root causes of the disk spilling and the best corresponding solutions? Select two options that directly address the disk spilling issue.

A) The statistics on the tables are outdated. Run 'ANALYZE TABLE ORDERS' and 'ANALYZE TABLE CUSTOMER to update the statistics.
B) The query is performing a full table scan on the 'ORDERS' table. Add an index on the 'CUSTOMER ID column in the 'ORDERS table.
C) The 'CUSTOMER_ID column is not properly clustered in either the 'ORDERS' or 'CUSTOMER table. Define a clustering key on 'CUSTOMER_ID for both tables.
D) The join operation is resulting in a large intermediate result set that exceeds the available memory. Apply a filter on the 'ORDERS' table to reduce the data volume before the join.
E) The virtual warehouse is undersized for the amount of data being processed. Increase the virtual warehouse size to provide more memory.

4. You're designing a Snowpark Scala stored procedure that must execute a series of complex data quality checks on a Snowflake table.
These checks involve multiple steps, including validating data types, checking for null values, and verifying data consistency against external reference data'. You want to ensure that the stored procedure is resilient to errors, provides detailed logging, and can be easily monitored. Which of the following approaches would be the MOST robust and scalable for handling errors and logging within this Snowpark Scala stored procedure?

A) Rely on Snowflake's built-in error handling and logging mechanisms. If an error occurs, the stored procedure will automatically fail, and the error details can be retrieved from Snowflake's query history.
B) Use Scala's 'Option' type to handle potential null values and exceptions. Return a string message indicating success or failure for each check. Log these messages using 'System.out.println'.
C) Wrap each data quality check in a try-catch block and use 'println' statements to log error messages to the Snowflake console.
D) Use Scala's 'Try' monad to handle exceptions, mapping successes to informational messages and failures to error messages. Log these messages using Snowflake's event tables.
E) Implement a custom logging framework within the Scala stored procedure that writes detailed logs to a dedicated Snowflake table. Use try-catch blocks to handle exceptions and log error details, including timestamps, error codes, and relevant data values. Use Snowflake's 'SYSTEM$LAST QUERY ID()' function to track query lineage.

5. A Snowflake data pipeline utilizes Snowpipe to ingest JSON data from cloud storage into a raw staging table 'RAW DATA' Subsequently, a series of transformation tasks are executed to cleanse, transform, and load the data into fact and dimension tables. You've noticed significant performance degradation in the transformation tasks, especially when dealing with large JSON payloads and deeply nested structures. Which of the following optimization techniques, applied at different stages of the pipeline, would MOST likely improve the overall performance of the data transformation tasks?

A) Using the file format option when defining the Snowpipe integration to remove the outer array from the JSON data before ingestion.
B) Replacing the transformation tasks with external functions implemented in Python using Snowpark, leveraging the power of Pandas DataFrames for JSON processing.
C) Employing Snowflake's 'LATERAL FLATTEN' function with appropriate 'PATH' expressions to efficiently extract the required attributes from the JSON data during transformation.
D) Partitioning the 'RAW DATA' staging table based on the ingestion timestamp to reduce the amount of data scanned during transformation.
E) Increasing the virtual warehouse size used by the transformation tasks to provide more compute resources.

質問と回答：