Vce ARA-C01 Questions Latest

The error message indicates that the IP address in the error message is not allowed to access Snowflake because it is not in the allowed list of the network policy. The network policy is a feature that allows restricting access to Snowflake based on IP addresses or ranges. To resolve this error, the Architect should take the following steps:

• Add the IP address in the error message to the allowed list in the network policy. This will allow the IP address to access Snowflake using the Private Link URLs. Alternatively, the Architect can disable the network policy if it is not required for security reasons.
• Update the configuration of the Azure AD SSO to use the Private Link URLs. This will ensure that the SSO authentication process uses the Private Link URLs instead of the public URLs. The configuration can be updated by following the steps in the Azure documentation1.

These two steps should resolve the error and ensure that the account is accessed using only Private Link. The other options are not necessary or relevant for this scenario. Altering the Azure security integration to use the Private Link URLs is not required because the security integration is used for SCIM provisioning, not for SSO authentication. Generating a new SCIM access token using system$generate_scim_access_token and saving it to Azure AD is not required because the SCIM access token is used for SCIM provisioning, not for SSO authentication. Opening a case with Snowflake Support to authorize the Private Link URLs’ access to the account is not required because the authorization can be done by the account administrator using the SYSTEM$AUTHORIZE_PRIVATELINK function2.

To improve the performance of queries on semi-structured data, such as JSON stored in a VARIANT column, Snowflake’s search optimization service can be utilized. By adding search optimization specifically for the longitude and latitude fields within the VARIANT column, the system can perform point lookups and substring queries more efficiently. This will allow for faster retrieval of weather statistics, which is critical for the drivers to receive timely updates.

References: The solution is supported by Snowflake documentation that details how search optimization can enhance query performance for semi-structured data1.

 According to the Snowflake documentation, the best practices for choosing clustering keys are:

• Choose columns that are frequently used in join predicates. This can improve the join performance by reducing the number of micro-partitions that need to be scanned and joined.
• Choose columns that are most actively used in selective filters. This can improve the scan efficiency by skipping micro-partitions that do not match the filter predicates.
• Avoid using low cardinality columns, such as gender or country, as clustering keys. This can result in poor clustering and high maintenance costs.
• Avoid using TIMESTAMP columns with nanoseconds, as they tend to have very high cardinality and low correlation with other columns. This can also result in poor clustering and high maintenance costs.
• Avoid using columns with duplicate values or NULLs, as they can cause skew in the clustering and reduce the benefits of pruning.
• Cluster on multiple columns if the queries use multiple filters or join predicates. This can increase the chances of pruning more micro-partitions and improve the compression ratio.
• Clustering is not always useful, especially for small or medium-sized tables, or tables that are not frequently queried or updated. Clustering can incur additional costs for initially clustering the data and maintaining the clustering over time.

References:

• Clustering Keys & Clustered Tables | Snowflake Documentation
• [Considerations for Choosing Clustering for a Table | Snowflake Documentation]