This view can be used to query login attempts by Snowflake users within the last 365 days (1 year). It provides information such as the event timestamp, the user name, the client IP, the authentication method, the success or failure status, and the error code or message if the login attempt was unsuccessful. By querying this view, the IT Security team can identify any suspicious or malicious login attempts to Snowflake and take appropriate actions to prevent credential stuffing attacks1. The other options are not the best ways to find recent and ongoing login attempts to Snowflake. Option A is incorrect because the LOGIN_HISTORY Information Schema table function only returns login events within the last 7 days, which may not be sufficient to detect credential stuffing attacks that span a longer period of time2. Option C is incorrect because the History tab in the Snowflake UI only shows the queries executed by the current user or role, not the login events of other users or roles3. Option D is incorrect because the Users section in the Account tab in the Snowflake UI only shows the last login time for each user, not the details of the login attempts or the failures.

Explanation
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]

Explanation
* The error message indicates that the schema STAGING does not have time travel data available for the requested timestamp, because the current version of the schema was created on2021-06-02 23:00:00, which is after the timestamp of 2021-06-01 08:00:00. Therefore, the CLONE statement cannot access the historical data of the schema at that point in time.
* Option A is incorrect, because undropping the STAGING schema will not restore the previous version of the schema that was active on 2021-06-01 08:00:00. Instead, it will create a new version of the schema with the same name and no data or objects.
* Option B is incorrect, because modifying the timestamp to 2021-05-01 10:00:00 will not clone the schema as it looked one week ago, but as it looked when it was first created. This may not reflect the desired state of the schema and its objects.
* Option C is correct, because renaming the STAGING schema and performing an UNDROP to retrieve the previous STAGING schema version will restore the schema that was dropped on 2021-06-02
23:00:00. This schema has time travel data available for the requested timestamp of 2021-06-01
08:00:00, and can be cloned using the CLONE statement.
* Option D is incorrect, because cloning can be accomplished by using the UNDROP command to access the previous version of the schema that was active during the proposed time travel period.
References: : Cloning Considerations : Understanding & Using Time Travel : CREATE <object> ... CLONE

When multiple teams perform selective point-lookups on different columns of the same large table, traditional clustering is not effective because a table can have only one clustering key. Creating a composite clustering key across many columns often leads to poor clustering depth and limited pruning benefits.
The Search Optimization Service (SOS) is specifically designed for this scenario (Answer C). It enables fast point-lookups across multiple columns without requiring table reclustering. SOS maintains additional search access paths that accelerate highly selective predicates and is ideal when queries vary across many lookup columns.
Materialized views introduce additional storage, maintenance overhead, and operational complexity, and Snowflake does not automatically replace base tables with materialized views. This question tests an architect' s ability to choose the right optimization technique for multi-access-path workloads, a core SnowPro Architect competency.
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