DEA-C02 試験問題を無料オンラインアクセス

You have a Snowflake Task that is designed to transform and load data into a target table. The task relies on a Stream to detect changes in a source table. However, you notice that the task is intermittently failing with a 'Stream STALE' error, even though the data in the source table is continuously updated. What are the most likely root causes and the best combination of solutions to prevent this issue? (Select TWO)

• A.The Stream is not configured with 'SHOW INITIAL ROWS = TRUE, causing initial changes to be missed and eventually leading to staleness. Recreate the stream with this parameter set to TRUE.
• B.The source table is being modified with DDL operations (e.g., ALTER TABLE ADD COLUMN), which are not supported by Streams. Use Table History to track schema changes and manually adjust the Stream's query if needed. Use 'COPY GRANTS' during the DDL.
• C.The Stream has reached its maximum age (default 14 days) and expired. There is no way to recover data from an expired Stream. You need to recreate the Stream and reload the source table.
• D.DML operations (e.g., UPDATE, DELETE) being performed on the source table are affecting rows older than the Stream's retention period. Reduce the stream's 'DATA RETENTION TIME IN DAYS' to match the oldest DML operation on the source table.
• E.The Task is not running frequently enough, causing the Stream to accumulate too many changes before being consumed, exceeding its retention period. Increase the task's execution frequency or increase the stream's 'DATA RETENTION TIME IN DAYS

You are tasked with building a Snowpipe to ingest JSON data from an AWS S3 bucket into a Snowflake table named 'SALES DATA'. The data is landing in the bucket frequently, and you want to use Snowpipe's auto-ingest feature. However, you are observing significant latency in data appearing in your Snowflake table after it lands in S3, despite verifying that S3 event notifications are correctly configured and the Snowflake event queue is receiving them. You've already checked that the pipe is enabled and has the necessary permissions. The Snowflake Pipe definition is as follows:

What is the MOST LIKELY reason for this delay, and what steps can you take to further troubleshoot?

• A.There is a backlog of files in the internal Snowflake queue waiting to be processed. Monitor the 'SYSTEM$PIPE STATUS' function and consider increasing the 'MAX CONCURRENCY' parameter (if applicable, based on underlying infrastructure considerations) on the pipe definition.
• B.Snowpipe auto-ingest only supports CSV files. Convert your JSON data to CSV format before loading.
• C.The S3 bucket is not in the same region as the Snowflake account. Ensure the S3 bucket and Snowflake account are in the same region to reduce network latency.
• D.The Snowflake virtual warehouse associated with the pipe is undersized. Increase the warehouse size to improve ingestion performance.
• E.Snowflake's internal metadata cache is out of sync. Run 'ALTER PIPE SALES PIPE to refresh the cache.

You are designing a data protection strategy for a Snowflake environment that processes sensitive payment card industry (PCI) data'. You decide to use a combination of column-level security and external tokenization. Which of the following statements are TRUE regarding the advantages of using both techniques together? (Select TWO)

• A.Combining masking policies and external tokenization allows for complete elimination of PCl data from the Snowflake environment, even during processing.
• B.The use of both techniques increases query performance drastically.
• C.Masking policies and external tokenization provide independent layers of security. If one is compromised, the other still provides protection.
• D.Column-level security can be used to restrict access to the tokenization UDF itself, ensuring that only authorized users can perform tokenization or detokenization operations.
• E.Tokenization ensures compliance with PCl DSS standards, while masking policies are primarily useful for internal access control and obfuscation for development environments. Using both doesn't increase security.

You are designing a data protection strategy for a Snowflake database. You need to implement dynamic data masking on the 'CREDIT CARD' column in the 'TRANSACTIONS' table. The requirement is that users with the 'FINANCE ADMIN' role should see the full credit card number, while all other users should see only the last four digits. You have the following masking policy:

What is the next step to apply this masking policy to the 'CREDIT CARD' column?

• A.
• B.
• C.
• D.
• E.

You are tasked with sharing a subset of a customer table (CUSTOMER DATA') residing in your organization's Snowflake account with a partner organization. You need to mask personally identifiable information (PII) while providing near real-time updates. You decide to use a secure view. Which of the following SQL statements is the MOST efficient and secure way to accomplish this, assuming the partner only needs 'customer id', 'masked_email', 'city', and 'state'? The email should be masked using SHA256.

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