DEA-C02 무료 덤프문제 온라인 액세스

You are implementing row access policies on a 'SALES DATA table to restrict access based on the 'REGION' column. Different users are allowed to see data only for specific regions. You have a mapping table 'USER REGION MAP' with columns 'USERNAME' and 'REGION'. You want to create a row access policy that dynamically filters the 'SALES DATA' based on the user and their allowed region. Which of the following options represents a correct approach to create and apply this row access policy?

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

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

You are developing a Snowpark Python application that reads data from a large Snowflake table, performs several transformations, and then writes the results back to a new table. You notice that the write operation is taking significantly longer than the read and transformation steps. The target table is not clustered. Which of the following actions, either individually or in combination, would likely improve the write performance most significantly ?

• A.Disable auto-tuning for the warehouse to ensure consistent performance
• B.Use the 'DataFrame.repartition(numPartitions)' method before writing to the table. Choose a 'numPartitionS value that is significantly higher than the number of virtual warehouses in your warehouse size.
• C.Use the FILE SIZE', value)' method to reduce the size of the output files, potentially leading to more parallelism during the write operation.
• D.Increase the size of the Snowflake warehouse used for the Snowpark session.
• E.Cluster the target table on the primary key before writing to it. Then, ensure the data being written is pre-sorted according to the clustering key.

You are designing a data sharing solution for a multi-tenant application where each tenant's data must be isolated. You have a 'sales' table with a 'tenant_id' column. You need to implement row-level security to ensure that each tenant can only access their own data when querying the shared table. Which of the following approaches, considering performance and security, is the MOST suitable for implementing this row-level filtering in Snowflake?

• A.Implement a row access policy on the 'sales' table that filters data based on the 'tenant_id' column and the current role or user context.
• B.Implement a user-defined function (UDF) that checks the current user's tenant ID and returns a boolean value indicating whether the row should be visible. Use this UDF in a WHERE clause in every query.
• C.Use Snowflake's data masking policies to mask all data for tenants other than the one currently querying the table.
• D.Create a scheduled task that duplicates the sales table into a new table for each tenant, filtering by the tenant_id.
• E.Create a separate VIEW for each tenant, filtering by 'tenant_id'. Grant each tenant access only to their respective view.

You have a table named 'TRANSACTIONS with the following definition: CREATE TABLE TRANSACTIONS ( TRANSACTION ID NUMBER, TRANSACTION DATE DATE, CUSTOMER_ID NUMBER, AMOUNT PRODUCT_CATEGORY VARCHAR(50) Users frequently query this table using filters on both 'TRANSACTION_DATE and 'PRODUCT CATEGORY. You want to optimize query performance. What is the MOST effective approach?

• A.Cluster the table on ' TRANSACTION_DATE and then create a materialized view filtered by PRODUCT_CATEGORY&.
• B.Create a materialized view joining 'TRANSACTIONS' with a dimension table containing product category information.
• C.Create separate indexes on 'TRANSACTION DATE' and 'PRODUCT CATEGORY.
• D.Cluster the table using a composite key of '(TRANSACTION_DATE, PRODUCT CATEGORY)'.
• E.Partition the table by 'TRANSACTION DATE