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DAA-C01試験の準備方法｜認定するDAA-C01日本語版参考資料試験｜完璧なSnowPro Advanced: Data Analyst Certification Exam最新知識
SnowPro Advanced: Data Analyst Certification Examテストの準備は、主に当社のクライアントは、DAA-C01試験に合格するのを助けると認証を得ることができます。この認証は、クライアントに大きなメリットをもたらします。クライアントは大企業に参入し、高給を稼ぐことができます。 DAA-C01試験に合格すると、給与を2倍にすることができます。認定資格を所有している場合、DAA-C01クイズトレントを十分にマスターし、優れた能力を所有していることを証明し、会社または工場で尊敬されます。あなたの仕事を変えたいなら、それはあなたにとっても良いことです。
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Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q64-Q69):
質問 # 64
A data analyst needs to process a large JSON payload stored in a VARIANT column named 'payload' in a table called 'raw events' The payload contains an array of user sessions, each with potentially different attributes. Each session object in the array has a 'sessionld' , 'userld' , and an array of 'eventS. The events array contains objects with 'eventType' and 'timestamp'. The analyst wants to use a table function to flatten this nested structure into a relational format for easier analysis. Which approach is most efficient and correct for extracting and transforming this data?
- A. Employ a combination of LATERAL FLATTEN and Snowpark DataFrames, using LATERAL FLATTEN to partially flatten the JSON and then Snowpark to handle the remaining complex transformations and data type handling.
- B. Use LATERAL FLATTEN with multiple levels of nesting, specifying 'path' for each level and directly selecting the desired attributes.
- C. Create a recursive UDF (User-Defined Function) in Python to traverse the nested JSON and return a structured result, then call this UDF in a SELECT statement.
- D. Load the JSON data into a temporary table, then write a series of complex SQL queries with JOINs and UNNEST operations to flatten the data.
- E. Utilize a Snowpark DataFrame transformation with multiple 'explode' operations and schema inference to flatten the nested structure and load data into a new table.
正解：B
解説：
Option A is the most efficient and Snowflake-native approach. LATERAL FLATTEN is optimized for handling nested data structures within Snowflake. While other options might work, they introduce overhead (UDF execution), are less efficient (temporary tables and complex SQL), or rely on external frameworks (Snowpark), making them less suitable for this scenario. Specifying the path ensures specific fields are targeted, avoiding unnecessary processing of irrelevant data. LATERAL flatten allows you to join the output of a table function with each row of the input table. This is essential to maintain the context (e.g., userId) from the outer table.
質問 # 65
What attributes of the Query Profile are typically assessed to understand query performance in Snowflake?
- A. Query history and user access logs
- B. Number of user sessions and database objects accessed
- C. Execution time and query complexity
- D. Hardware configuration and system resources
正解：C
解説：
Query Profile attributes like execution time and query complexity are evaluated to comprehend query performance and optimize it in Snowflake.
質問 # 66
When loading data into Snowflake using Snowsight, what functionalities does this method offer?
(Select all that apply)
- A. Batch data loading
- B. Real-time data loading
- C. Support for only structured data formats
- D. Streamlined data import from external/internal stages
正解：A、D
解説：
Snowsight supports batch data loading and streamlined data import from both external and internal stages.
質問 # 67
A marketing team wants to visualize website traffic data in Snowsight. They have a table named 'WEBSITE TRAFFIC' with columns 'VISIT DATE' (DATE), 'PAGE URL' (VARCHAR), and 'VISITOR COUNT' (INTEGER). The team wants to create a line chart showing the daily visitor count for a specific page, filtered by date range, allowing users to dynamically select the page URL from a dropdown. Which of the following steps are MOST effective and correct to achieve this using Snowsight dashboards?
- A. Write a Python script using the Snowflake Connector to query the data, process it using Pandas, and then generate a chart using Matplotlib. Embed the chart as an image in the Snowsight dashboard.
- B. Create a materialized view using 'CREATE MATERIALIZED VIEW AS SELECT VISIT_DATE, PAGE_URL, AS TOTAL VISITS FROM WEBSITE_TRAFFIC GROUP BY VISIT DATE, PAGE URL;'. In Snowsight, create a dashboard and a line chart using this materialized view with a filter on and configured as dashboard variables.
- C. Create a stored procedure in Snowflake that accepts 'start_date', 'end_date' , and as parameters, executes the query, and returns the result set. Call the stored procedure from the Snowsight dashboard, passing the filter values.
- D. Create a new chart in a Snowsight dashboard. Use a SQL query like 'SELECT VISIT_DATE, FROM WEBSITE_TRAFFIC WHERE PAGE URL = 'specific_page' AND VISIT DATE BETWEEN 'start_date' AND 'end_date' GROUP BY VISIT DATE ORDER BY VISIT DATE$. Manually update the query for each page.
- E. Create a view 'DAILY PAGE_VISITS using a SQL query like 'CREATE OR REPLACE VIEW DAILY PAGE_VISITS AS SELECT VISIT DATE, PAGE URL, AS TOTAL_VISITS FROM WEBSITE_TRAFFIC GROUP BY VISIT_DATE, PAGE_URL;'. Then, in Snowsight, create a dashboard and a line chart using this view with a filter on 'PAGE URL' and 'VISIT DATE', configured as dashboard variables.
正解：B、E
解説：
Option B is the most effective because it creates a view that aggregates the data, and then utilizes Snowsight dashboard variables for filtering, providing a dynamic and user-friendly experience. Option D is also correct because using a materialized view will significantly improve the performance of the query, especially when dealing with large datasets. Materialized views pre-compute and store the results of the query, making it faster to retrieve the data for the dashboard. Option A is inefficient as it requires manual updates to the query. Option C introduces unnecessary complexity and doesn't leverage Snowflake's built-in capabilities. Option E is overkill and less efficient for this simple scenario.
質問 # 68
You are developing a Snowflake stored procedure that uses an external Python library (e.g., scikit-learn for machine learning). The library is not natively available within Snowflake's Python environment. What is the correct process to include and utilize this external library within your stored procedure?
- A. Use the 'pip install' command within the stored procedure's Python code to install the library from PyPl during each execution of the procedure.
- B. Include the source code of the library directly within the stored procedure's Python code.
- C. Upload the library using the Snowflake web interface, so Snowflake will know which library it should be using.
- D. Simply import the library in your Python code within the stored procedure. Snowflake automatically downloads and installs any missing libraries from PyPl when the procedure is executed.
- E. Create a Snowflake stage, upload the library's '.whl' file to the stage, and then use the 'CREATE PROCEDURE statement with the 'IMPORTS' clause to specify the stage and .whl' file. Snowflake will then install the library during procedure creation.
正解：E
解説：
Option B is the correct approach. Snowflake uses stages and the 'IMPORTS' clause to manage external dependencies for Python stored procedures. You must upload the .whl' file of the library to a stage and then reference it in the 'CREATE PROCEDURE' statement. This ensures that the library is available when the procedure is executed. Option A is incorrect because Snowflake does not automatically download libraries from PyPl. Option C is incorrect because you cannot execute shell commands like 'pip install' within a stored procedure. Option D is generally impractical for larger libraries, and Option E isn't a valid approach.
質問 # 69
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DAA-C01最新知識: https://www.topexam.jp/DAA-C01_shiken.html
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SnowflakeのDAA-C01認定試験に楽に受かるコツが何だろう
あなたは十分の時間でDAA-C01試験を準備することができます、Topexamはあなたと一緒に君のITの夢を叶えるために頑張ります、これはさまざまな試験の実践の検査に合格したもので、SnowflakeのDAA-C01認定試験に合格したかったら、Topexamを選ぶのは絶対正しいことです。
