Snowflake ADA-C01 Exam With Confidence Using Practice Dumps

According to the Network Policies documentation, a network policy can be applied to an account, a security integration, or a user. If there are network policies applied to more than one of these, the most specific network policy overrides more general network policies. The following summarizes the order of precedence:

•Account: Network policies applied to an account are the most general network policies. They are overridden by network policies applied to a security integration or user.

•Security Integration: Network policies applied to a security integration override network policies applied to the account, but are overridden by a network policy applied to a user.

•User: Network policies applied to a user are the most specific network policies. They override both accounts and security integrations.

Therefore, if both the account_level and user_level network policies are defined, the user_level policy will take effect and the account_level policy will be ignored. The other options are incorrect because:

•The account_level policy will not override the user_level policy, as explained above.

•The user_level network policies will be supported, as they are part of the network policy feature.

•A network policy error will not be generated, as there is no conflict between the account_level and user_level network policies.

According to the Snowflake documentation1, a standard listing is a type of listing that provides free access to the full data product, with no payment required. A standard listing can be added and queried immediately by the consumer, as long as they accept the terms and conditions of the listing. A monetized listing is a type of listing that charges for access to the data product, using the pricing models offered by Snowflake. A monetized listing requires the consumer to provide payment information and agree to the billing terms before accessing the data product. A regional listing is not a type of listing, but a way to specify the regions where the listing is available. A personalized listing is a type of listing that provides limited trial access to the data product, with unlimited access to the full data product available upon request. A personalized listing requires the consumer to request access from the provider and wait for the provider to grant access before accessing the data product. Therefore, the only type of listing that can be added and queried immediately is the standard listing.

According to the Snowflake Warehouse Cost Optimization blog post, one of the strategies to reduce the cost of running a warehouse is to use a multi-cluster warehouse with auto-scaling enabled. This allows the warehouse to automatically adjust the number of clusters based on the concurrency demand and the queue size. A multi-cluster warehouse can also be configured with a minimum and maximum number of clusters, as well as a scaling policy to control the scaling behavior. This way, the warehouse can handle the parallel load queries efficiently without wasting resources or credits. The blog post also suggests using a smaller warehouse size, such as SMALL or XSMALL, for loading data, as it can perform better than a larger warehouse size for small INSERTs. Therefore, the best option to reduce the costs while minimizing the overall load times for migrating data warehouse history is to keep the warehouse as a SMALL or XSMALL and configure it as a multi-cluster warehouse to handle the parallel load queries. The other options are incorrect because:

•A. Deploying another 2XL warehouse to handle a portion of the load queries will not reduce the costs, but increase them. It will also introduce complexity and potential inconsistency in managing the data loading process across multiple warehouses.

•B. Changing the 2XL warehouse to 4XL will not reduce the costs, but increase them. It will also provide more compute resources than needed for small INSERTs, which are not CPU-intensive but I/O-intensive.

•D. Converting the INSERTs to several tables will not reduce the costs, but increase them. It will also create unnecessary data duplication and fragmentation, which will affect the query performance and data quality.