Snowflake Certification ADA-C01 Syllabus Exam Questions Answers

According to the Snowflake documentation1, network policies are a feature that allows restricting access to your account based on user IP address. A network policy can be applied to an account, a user, or a security integration, and can specify a list of allowed IP addresses and a list of blocked IP addresses. If there are network policies applied to more than one of these, the most specific network policy overrides more general network policies. In this case, the user1 has a network policy (bar_policy) applied to them, which overrides the account-level network policy (foo_policy). The bar_policy allows access only from the IP range 3.3.3.0/24, and blocks access from the IP address 3.3.3.10. Therefore, the user1 will not be able to log in to Snowflake from IP address 3.3.3.10, as it is found in the BLOCKED_IP_LIST of bar_policy. Option A is incorrect because the ALLOWED_IP_LIST of bar_policy does not override the BLOCKED_IP_LIST of bar_policy. Option C is incorrect because the ALLOWED_IP_LIST of foo_policy does not apply to user1, as it is overridden by the user-level network policy. Option D is incorrect because the ALLOWED_IP_LIST of foo_policy does not matter, as it is overridden by the user-level network policy.

According to the Using Persisted Query Results documentation, the RESULT_SCAN function allows you to query the result set of a previous command as if it were a table. The LAST_QUERY_ID function returns the query ID of the most recent statement executed in the current session. Therefore, the combination of these two functions can be used to access the output of the SHOW WAREHOUSES command, which returns the configurations of all the virtual warehouses in the account. However, to persist the warehouse data in JSON format in the table VWH_META, the OBJECT_CONSTRUCT function is needed to convert the output of the SHOW WAREHOUSES command into a VARIANT column. The OBJECT_CONSTRUCT function takes a list of key-value pairs and returns a single JSON object. Therefore, the correct commands to execute are:

1.SHOW WAREHOUSES;

2.INSERT INTO VWH_META SELECT CURRENT_TIMESTAMP (), OBJECT_CONSTRUCT (*) FROM TABLE (RESULT_SCAN (LAST_QUERY_ID ()));

The other options are incorrect because:

•A. This option does not use the OBJECT_CONSTRUCT function, so it will not persist the warehouse data in JSON format. Also, it is missing the * symbol in the SELECT clause, so it will not select any columns from the result set of the SHOW WAREHOUSES command.

•B. This option does not use the OBJECT_CONSTRUCT function, so it will not persist the warehouse data in JSON format. It will also try to insert multiple columns into a single VARIANT column, which will cause a type mismatch error.

•D. This option does not use the OBJECT_CONSTRUCT function, so it will not persist the warehouse data in JSON format. It will also try to use the RESULT_SCAN function on a subquery, which is not supported. The RESULT_SCAN function can only be used on a query ID or a table name.

According to the Snowflake documentation1, a multi-cluster warehouse is a virtual warehouse that consists of multiple clusters of compute resources that can scale up or down automatically to handle the concurrency and performance needs of the queries submitted to the warehouse. A multi-cluster warehouse has a minimum and maximum number of clusters that can be specified by the administrator. Option A is a possible solution to address the issues happening once a month, as it allows the administrator to use a task to increase the cluster size for the time period that the more complex queries are running and another task to reduce the size of the cluster once the complex queries complete. This way, the warehouse can have more resources available to handle the complex queries without reaching the maximum limit of 10 clusters, and then return to the normal cluster size to save costs. Option B is another possible solution to address the issues happening once a month, as it allows the administrator to have the group running the complex monthly queries use a separate appropriately-sized warehouse to support their workload. This way, the warehouse can isolate the complex queries from the standard workload and avoid queue time and resource contention. Option C is not a recommended solution to address the issues happening once a month, as it would increase the costs and complexity of managing the multi-cluster warehouse, and may not solve the underlying problem of inefficient queries. Option D is a good practice to improve the performance of the queries, but it is not a direct solution to address the issues happening once a month, as it requires analyzing and optimizing the complex queries using clustering keys or materialized views, which may not be feasible or effective in all cases. Option E is not a recommended solution to address the issues happening once a month, as it would increase the costs and waste resources by starting more clusters than needed for the standard workload.

According to the Snowflake documentation1, Snowflake uses a multi-version concurrency control (MVCC) model, which means that each transaction operates on a consistent snapshot of the database at a point in time. This allows queries and DML statements to run concurrently without blocking each other, as they do not modify the same data. Therefore, a deadlock, which occurs when concurrent transactions are waiting on resources that are locked by each other, cannot happen in Snowflake. Option B is incorrect because queries and DML statements do not block each other in Snowflake, unless they are explicitly started transactions and multiple statements in each transaction2. Option C is incorrect because transaction locking in Snowflake is enforced at the partition level, not the row or table level3. Option D is incorrect because queries executed within a given transaction do not see that transaction’s uncommitted changes, but only the committed changes that occurred before the transaction started1.