DEA-C01 Exam Dumps - SnowPro Advanced: Data Engineer Certification Exam

An API integration is required when defining an external function in Snowflake. An API integration is a Snowflake object that defines how Snowflake communicates with an externalservice via HTTPS requests and responses. An API integration specifies parameters such as URL, authentication method, encryption settings, request headers, and timeout values. An API integration is used to create an external function object that invokes the external service from within SQL queries.

The system function SYSTEM$ABORT_TRANSACTION can be used to abort a detached transaction that was left in an open state due to a session disconnect or termination. The function takes one argument: the transaction ID of the detached transaction. The function will abort the transaction and release any locks held by it. The other options are incorrect because they do not address the issue of a detached transaction. The system function SYSTEM$CANCEL_TRANSACTION can be used to cancel a running transaction, but not a detached one. The LOCK_TIMEOUT parameter can be used to set a timeout period for acquiring locks on resources, but it does not affect existing locks. The TRANSACTION_ABORT_ON_ERROR parameter can be used to control whether a transaction should abort or continue when an error occurs, but it does not affect detached transactions.

Snowflake’s query results caching capabilities allow users to reuse the results of previously executed queries without re-executing them. For this to happen, the following conditions must be met:

• The results must be reused within 24 hours (not 72 hours), which is the default time-to-live (TTL) for cached results.
• The query must be executed using any virtual warehouse (not necessarily the same one), as long as it is in the same region and account as the original query.
• The USED_CACHED_RESULT parameter does not need to be included in the query, as it is enabled by default at the account level. However, it can be disabled or overridden at the session or statement level.
• The table structure contributing to the query result cannot have changed, such as adding or dropping columns, changing data types, or altering constraints.
• The new query must have the same syntax as the previously executed query, including whitespace and case sensitivity.
• The micro-partitions cannot have changed due to changes to other data in the table, such as inserting, updating, deleting, or merging rows.

Adding a date column as a cluster key on the table will improve the query performance by reducing the number of micro-partitions that need to be scanned. Since the table is filtered on a single day, clustering by date will make the query more selective and efficient.

The use case that would be best suited for the search optimization service is business users who need fast response times using highly selective filters. The search optimization service is a feature that enables faster queries on tables with high cardinality columns by creating inverted indexes on those columns. High cardinality columns are columns that have a large number of distinct values, such as customer IDs, product SKUs, or email addresses. Queries that use highly selective filters on high cardinality columns can benefit from the search optimization service because they can quickly locate the relevant rows without scanning the entire table. The other options are not best suited for the search optimization service. Option A is incorrect because analysts who need to perform aggregates over high cardinality columns will not benefit from the search optimization service, as they will still need to scan all the rows that match the filter criteria. Option C is incorrect because data scientists who seek specific JOIN statements with large volumes of data will not benefit from the search optimization service, as they will still need to perform join operations that may involve shuffling or sorting data across nodes. Option D is incorrect because data engineers who create clustered tables with frequent reads against clustering keys will not benefit from the search optimization service, as they already have an efficient way to organize and access data based on clustering keys.