Free Snowflake DEA-C02 Exam Dumps Questions & Answers

You are tasked with implementing a data recovery strategy for a critical table 'SALES DATA' in Snowflake. The table is frequently updated, and you need to ensure you can recover to a specific point in time in case of accidental data corruption. Which approach provides the most efficient and granular recovery option, minimizing downtime and data loss? Consider performance and storage implications of each method.

• A.Regularly creating full clones of the 'SALES_DATR table to a separate database.
• B.Relying solely on Snowflake's Time Travel feature with the default data retention period.
• C.Creating a Snowflake Stream on 'SALES_DATR and capturing all DML changes for point-in-time recovery.
• D.Using the 'UNDROP TABLE command in conjunction with the 'AT' clause to revert the table to a previous state.
• E.Create a scheduled task that takes a snapshot of the sales data and store it to an external staging location.

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

You are ingesting data from an external stage (AWS S3) into a Snowflake table using Snowpipe. Data files are continuously being uploaded to the stage. After several hours, you notice that some data files are not being loaded. You check the Snowpipe error notifications and see 'net.snowflake.ingest.errors.FileSizeLimitExceededError'. You have already verified that the Snowpipe is correctly configured and the user has the necessary permissions. What are the MOST LIKELY reasons for this error and how can you resolve them?

• A.Snowflake has reached its maximum allowable data storage capacity. Increase your Snowflake storage capacity to resolve this issue.
• B.The size of the data files in the stage exceeds the maximum allowed size for Snowpipe. Split the large files into smaller files before uploading to the stage.
• C.The Snowpipe configuration is incorrect; specifically, the 'FILE FORMAT parameter is not correctly specified to handle the file type. Reconfigure the Snowpipe with the correct 'FILE FORMAT.
• D.The Snowpipe is encountering a transient network error. Reset the pipe using ALTER PIPE REFRESH;
• E.The data files are being uploaded to the stage faster than Snowpipe can process them. Increase the value of the 'MAX CONCURRENCY parameter in the Snowpipe definition.

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

A data engineer is tasked with optimizing query performance on a Snowflake table named 'SALES DATA, which currently has no clustering key defined. The table contains 'SALE (unique identifier), 'SALE DATE, 'PRODUCT CATEGORY, and 'SALE AMOUNT. The business analysts frequently run queries filtering on 'SALE DATE and then aggregating by 'PRODUCT CATEGORY'. Choosing the right clustering keys for the SALES DATA table is crucial for minimizing disk 1/0 and enhancing query speed. Which of the following clustering key strategies would be MOST effective for the specified query patterns, considering both performance and the potential impact on data loading and DML operations?

• A.Clustering on 'SALE DATE followed by 'PRODUCT CATEGORY.
• B.Clustering only on 'SALE DATE
• C.Clustering only on PRODUCT_CATEGORY.
• D.Creating separate tables for each 'PRODUCT CATEGORY.
• E.Clustering on followed by 'SALE_DATE'.