DEA-C02 試験問題を無料オンラインアクセス

You have a requirement to continuously load data from a cloud storage location into a Snowflake table. The source data is in Avro format and is being appended to the cloud storage location frequently. You want to automate this process using Snowpipe. You've already created the Snowpipe and the associated stage and file format. However, you notice that some files are being skipped during the ingestion process, and data is missing in your Snowflake table. What is the MOST likely reason for this issue, assuming all necessary permissions and configurations (stage, file format, pipe definition) are correctly set up?

• A.The file format definition in Snowflake is incompatible with the Avro schema.
• B.The data files in cloud storage are not being automatically detected by Snowpipe.
• C.The Snowpipe is paused due to exceeding the daily quota.
• D.The cloud storage event notifications are not properly configured to trigger Snowpipe.
• E.Snowflake does not support Avro format for Snowpipe.

You are using Snowpark Python to perform data transformation on a large dataset stored in a Snowflake table named customer transactions'. This table contains columns such as 'customer id', 'transaction date', 'transaction amount', and product_category'. Your task is to identify customers who have made transactions in more than one product category within the last 30 days. Which of the following Snowpark Python snippets is the most efficient way to achieve this, minimizing data shuffling and maximizing query performance?

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

You are designing a data sharing solution in Snowflake where a provider account shares a view with a consumer account. The view is based on a table that undergoes frequent DML operations (inserts, updates, deletes). The consumer account needs to see a consistent snapshot of the data, even during these DML operations. Which of the following strategies, or combination of strategies, would be MOST effective in ensuring data consistency from the consumer's perspective, and what considerations should be made?

• A.Using Snowflake's Time Travel feature by querying the view with a specific 'AT' or 'BEFORE' clause in the consumer account. The provider account needs to inform the consumer account of a specific timestamp that guarantees consistency, adding administrative overhead.
• B.Creating a materialized view in the provider account and sharing that materialized view. This adds compute costs to the provider but ensures a consistent snapshot for the consumer account. The materialized view needs to be refreshed periodically, based on the rate of DML changes.
• C.A and B
• D.Creating a stream on the base table in the provider account and building a view on top of the stream. This way, changes are only reflected when the stream is consumed, allowing for batch processing and controlled updates in the consumer account.
• E.Creating a standard view in the provider account and relying on Snowflake's inherent transactional consistency. The consumer account will always see a consistent snapshot of the data as it existed at the beginning of their query execution. No additional configurations are necessary.

A data engineering team is implementing Row Access Policies (RAP) on a table 'employee_data' containing sensitive salary information. They need to ensure that only managers can see the salary information of their direct reports. A user-defined function (UDF) 'GET returns a comma-separated string of manager usernames for a given username. Which of the following SQL statements correctly creates and applies a RAP to achieve this?

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

Snowpark DataFrame 'employee_df' contains employee data, including 'employee_id', 'department', and 'salary'. You need to calculate the average salary for each department and also retrieve all the employee details along with the department average salary.
Which of the following approaches is the MOST efficient way to achieve this?

• A.Use a correlated subquery within the SELECT statement to calculate the average salary for each department for each employee.
• B.Use the 'window' function with 'avg' to compute the average salary per department and include it as a new column in the original DataFrame.
• C.Use 'groupBV to get a dataframe containing average salary by department and then use a Python UDF to iterate through the 'employee_df and add the value to each row
• D.Create a separate DataFrame with average salaries per department, then join it back to the original DataFrame.
• E.Create a temporary table with average salaries per department, then join it back to the original DataFrame.