DEA-C02認定テキスト & DEA-C02予想試験

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有益な取引を行うだけでなく、SnowflakeユーザーがDEA-C02証明書を取得するまでの最短時間で試験に合格できるようにしたいと考えています。 DEA-C02試験のプラクティスを選択すると、Japancert試験の準備に20〜30時間しかかかりません。 DEA-C02の学習教材は試験の概要とDEA-C02ガイドの質問の質問に密接に関連しているため、このような短い時間ですべてのコンテンツを終了できるかどうかを尋ねる場合があります。最新の基本的なSnowPro Advanced: Data Engineer (DEA-C02)知識に関連しています。 DEA-C02試験問題に合格した場合のみ、DEA-C02試験に合格します。

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) 認定 DEA-C02 試験問題 (Q178-Q183):

質問 # 178
You are developing a Python script to perform bulk data updates in a Snowflake table. The script needs to update a large number of rows based on values from a Pandas DataFrame. Which of the following approaches is the most efficient and scalable way to achieve this using the Snowflake Python connector, minimizing the number of database operations?

A. Use with the option to insert the updated data into a staging table, then use a 'MERGE' statement to update the target table from the staging table.
B. Use 'SnowflakeCursor.executemany()' with a list of tuples containing the update values.
C. Iterate through the rows of the Pandas DataFrame and execute an 'UPDATE statement for each row using 'cursor.execute()'.
D. Create a temporary table in Snowflake, load the DataFrame into the temporary table using , and then use a single 'UPDATE' statement with a 'JOIN' to the temporary table.
E. Construct a single, large 'UPDATE statement with multiple 'CASE WHEN' clauses to update all rows in a single operation.

正解：A、B

解説：
Options B and D are the most efficient. Option B leverages staging tables and a MERGE statement, which is a highly optimized way to perform bulk updates in Snowflake. This minimizes the number of individual operations and takes advantage of Snowflake's internal optimization. Option D uses 'executemany()' , which sends multiple parameterized queries to Snowflake in a single network round trip, significantly improving performance compared to executing individual UPDATE statements. Option A is the least efficient, as it involves a separate database operation for each row. Option C might be feasible for a small number of updates but becomes unwieldy and inefficient for large datasets. Option E introduces unnecessary complexity with temporary tables; MERGE is a better solution.

質問 # 179
You are building a data pipeline in Snowflake using Snowpark Python. As part of the pipeline, you need to create a dynamic SQL query to filter records from a table named 'PRODUCT REVIEWS based on a list of product categories. The list of categories is passed to a stored procedure as a string argument, where categories are comma separated. The filtered data needs to be further processed within the stored procedure. Which of the following approaches are MOST efficient and secure ways to construct and execute this dynamic SQL query using Snowpark?

A. Constructing the SQL query using 'session.sql()' and string concatenation, ensuring proper escaping of single quotes within the product categories string.
B. Using Snowpark's on the list of product categories after converting them into a Snowflake array, and then using 'session.sql()' to execute the query.
C. Using Python's string formatting to build the SQL query directly, and then executing it using 'session.sql()'.
D. Using the Snowpark "functions.lit()' function to create literal values from the list of product categories and incorporating them into the SQL query, then use 'session.sql()' to run it.
E. Using Python's string formatting along with the and 'session.sql()' functions to build and execute the SQL query securely, avoiding SQL injection vulnerabilities.

正解：D、E

解説：
Options C and E are the most appropriate and secure. Option C leverages 'snowflake.snowpark.functions.lit()' to safely incorporate literal values into the SQL query. The 'lit()' function handles the proper escaping, mitigating potential SQL injection risks. The resulting query is then executed with 'session.sql()'. This solution is very useful when using 'SP' and 'UDTF. Option E uses to create a SQL expression object that safely constructs the 'WHERE clause using string formatting, reducing the risk of SQL injection. session.sql()' is then used to execute the constructed query. Options A and B are generally unsafe due to SQL injection vulnerabilities. Option D may not be efficient for larger category lists and is less readable.

質問 # 180
You are tasked with creating a SQL UDF in Snowflake to mask sensitive customer data (email addresses) before it's used in a reporting dashboard. The masking should replace all characters before the '@' symbol with asterisks, preserving the domain part. For example, 'john.doe@example.com' should become ' @example.com'. Which of the following SQL UDF definitions correctly implements this masking logic, while minimizing the impact on Snowflake compute resources?

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

正解：B

解説：
Option A correctly uses LPAD and SPLIT PART to replace the username portion of the email with asterisks while retaining the domain. It efficiently avoids unnecessary calculations or regular expressions. Option B is incorrect because it simply replaces everything before the @ with ' @', which is not dynamic. Option C's logic is flawed and would not mask correctly. Option D provides additional casing for when an email doesn't have '@' symbol, which is technically correct for handling unexpected input, but is not needed for the explicit requirement. Option E has incorrect syntax. This question assesses understanding of string manipulation functions and their optimal usage within a UDF.

質問 # 181
You have a Snowflake table named 'ORDERS clustered on 'ORDER DATE. After a significant data load, you want to evaluate the effectiveness of the clustering. Which of the following SQL queries, using Snowflake system functions, will provide insights into the clustering depth and overlap of micro-partitions in the 'ORDERS' table, specifically helping you identify whether re-clustering is necessary? Assume that the table

正解：D

解説：
The query SELECT avg_depth, avg_overlap FROM is the correct approach. The function, when given the table name and the clustering key column(s), returns information about the clustering state. Using 'TABLE()' allows you to extract 'avg_deptm and 'avg_overlap', which are key metrics for assessing clustering effectiveness. 'avg_depth' indicates how well the data is clustered (lower is better), and 'avg_overlap' indicates the degree of overlap between micro-partitions (lower is better). A high 'avg_depth' or 'avg_overlap' suggests the need for re-clustering. Option A returns a JSON which is difficult to process to get the required metrics. Option B is missing the clustering key. Option C returns JSON and not the desired output. Option E is not valid SQL syntax in Snowflake.

質問 # 182
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. Break down the stored procedure into smaller, more manageable transactions and commit changes more frequently. Consider utilizing batch processing techniques.
B. Set a lower 'SUSPEND TASK AFTER N FAILURES' value to proactively suspend the task before it consumes excessive resources.
C. 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.
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：A、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 # 183
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DEA-C02認定テキスト & DEA-C02予想試験

Snowflake DEA-C02 Exam | DEA-C02認定テキスト - DEA-C02試験製品の無料ダウンロード

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) 認定 DEA-C02 試験問題 (Q178-Q183):

DEA-C02試験の準備方法 | 検証するDEA-C02認定テキスト試験 | 真実的なSnowPro Advanced: Data Engineer (DEA-C02)予想試験

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