Free Practice Questions for Snowflake ADA-C01 Exam

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.

Option B is the correct SQL command to create a resource monitor that meets the requirements. It sets the credit quota to 3000, the frequency to yearly, the start timestamp to January 1, 2022, and the triggers to notify and suspend the warehouse at the specified thresholds. Option A is incorrect because it does not specify the frequency. Option C is incorrect because it does not specify the frequency and it uses notify and suspend instead of suspend and suspend_immediate. Option D is incorrect because it does not specify the start timestamp. For more information about resource monitors, see Working with Resource Monitors and CREATE RESOURCE MONITOR.

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.

According to the Using OAuth 2.0 with Snowflake - Blog, only the ACCOUNTADMIN role can create and manage integrations, so an administrator must assume that role when creating a security integration for OAuth. The other roles do not have the necessary privileges to create the object required to setup OAuth 2.0 integration.

According to the Snowflake documentation1, the AUTO_SUSPEND property specifies the number of seconds of inactivity after which a warehouse is automatically suspended. If the property is set to 0 or NULL, the warehouse never suspends automatically. For a warehouse that supports several dashboards, issuing various queries on the same database, setting AUTO_SUSPEND to 0 or NULL can help to keep the data cache warm, which means that the data used by the queries is already loaded into the warehouse memory and does not need to be fetched from the storage layer. This can improve the performance of similar queries that access the same data. Option A is incorrect because setting AUTO_SUSPEND to 0 or NULL does not save costs on warehouse shutdowns and startups, but rather increases the costs by keeping the warehouse running continuously. Option B is incorrect because setting AUTO_SUSPEND to 0 or NULL does not run the warehouse as little as possible, but rather runs the warehouse as much as possible. Option D is incorrect because setting AUTO_SUSPEND to 0 or NULL does not affect the query result cache, which is a separate cache that stores the results of previous queries for a period of time. The query result cache is not dependent on the warehouse state, but on the query criteria2.

According to the Snowflake documentation1, you can change a managed access schema to a regular schema using the ALTER SCHEMA statement with the DISABLE MANAGED ACCESS keywords. This will disable the managed access feature on the schema and revert the access control to the default behavior. Option B is incorrect because dropping and recreating the schema will also delete all the objects and metadata in the schema, which is not necessary to disable the managed access. Option C is incorrect because revoking the privileges on the future tables from the role is not required to disable the managed access. Option D is incorrect because there is no WITHOUT MANAGED ACCESS option in the CREATE SCHEMA statement.

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 Understanding & Using Time Travel documentation, Time Travel is a feature that allows you to query, clone, and restore historical data in tables, schemas, and databases for up to 90 days. To meet the requirements of the scenario, the following best practices should be followed:

•The fact and dimension tables should have the same DATA_RETENTION_TIME_IN_DAYS. This parameter specifies the number of days for which the historical data is preserved and can be accessed by Time Travel. To ensure that the fact and dimension tables can be reverted to a consistent state in case of any anomalies in the latest load, they should have the same retention period. Otherwise, some tables may lose their historical data before others, resulting in data inconsistency and quality issues.

•The fact and dimension tables should be cloned together using the same Time Travel options to reduce potential referential integrity issues with the restored data. Cloning is a way of creating a copy of an object (table, schema, or database) at a specific point in time using Time Travel. To ensure that the fact and dimension tables are cloned with the same data set, they should be cloned together using the same AT or BEFORE clause. This will avoid any referential integrity issues that may arise from cloning tables at different points in time.

The other options are incorrect because:

•Related data should not be placed together in the same schema. Facts and dimension tables should each have their own schemas. This is not a best practice for Time Travel, as it does not affect the ability to query, clone, or restore historical data. However, it may be a good practice for data modeling and organization, depending on the use case and design principles.

•The DATA_RETENTION_TIME_IN_DAYS should be kept at the account level and neverused for lower level containers (databases and schemas). This is not a best practice for Time Travel, as it limits the flexibility and granularity of setting the retention period for different objects. The retention period can be set at the account, database, schema, or table level, and the most specific setting overrides the more general ones. This allows for customizing the retention period based on the data needs and characteristics of each object.

•Only TRANSIENT tables should be used to ensure referential integrity between the fact and dimension tables. This is not a best practice for Time Travel, as it does not affect the referential integrity between the tables. Transient tables are tables that do not have a Fail-safe period, which means that they cannot be recovered by Snowflake after the retention period ends. However, they still support Time Travel within the retention period, and can be queried, cloned, and restored like permanent tables. The choice of table type depends on the data durability and availability requirements, not on the referential integrity.

According to the Replication considerations documentation, the Time Travel retention period for a secondary database can be different from the primary database. The retention period can be set at the database, schema, or table level using the DATA_RETENTION_TIME_IN_DAYS parameter. Therefore, to extend the Time Travel retention policy to 60 days on the secondary database only, the best option is to set the data retention policy on the secondary database to 60 days using the ALTER DATABASE command. The other options are incorrect because:

•B. Setting the data retention policy on the schemas in the secondary database to 60 days will not affect the database-level retention period, which will remain at 30 days. The most specific setting overrides the more general ones, so the schema-level setting will apply to the tables in the schema, but not to the database itself.

•C. Setting the data retention policy on the primary database to 30 days and the schemas to 60 days will not affect the secondary database, which will have its own retention period. The replication process does not copy the retention period settings from the primary to the secondary database, so they can be configured independently.

•D. Setting the data retention policy on the primary database to 60 days will not affect the secondary database, which will have its own retention period. The replication process does not copy the retention period settings from the primary to the secondary database, so they can be configured independently.