Free Snowflake ADA-C01 Exam Dumps July 2026

According to the Snowflake documentation1, clustering keys are most effective when the data is evenly distributed across the key values. If the data is skewed, such as in this case where most sales come from only 20 stores out of 1000, then the micro-partitions will not be well-clustered and the pruning will be poor. This means that more bytes will be scanned by queries, even if they filter by STORE_ID. Option A is incorrect because the data type of the clustering key does not affect the pruning. Option B is incorrect because the table is large enough to benefit from clustering, if the data was more balanced. Option D is incorrect because the cardinality of the clustering key is not relevant for pruning, as long as the key values are distinct.

1: Considerations for Choosing Clustering for a Table | Snowflake Documentation

This command will generate a sample of 20% of the table by using the SYSTEM (or BLOCK) sampling method, which selects each block of rows with a probability of 20/100. This method is suitable for taking data by blocks of rows, as the question requires. According to theSnowflake documentation, ''SYSTEM (or BLOCK): Includes each block of rows with a probability of p/100. Similar to flipping a weighted coin for each block of rows. This method does not support fixed-size sampling.'' The other options are either incorrect or do not meet the requirements of the question. Option A uses the BERNOULLI (or ROW) sampling method, which selects each row with a probability of 20/100, but does not take data by blocks of rows. Option C uses the BLOCK sampling method, but specifies a fixed number of rows (20) instead of a percentage (20%). Option D uses the SYSTEM sampling method, but specifies a seed value (1), which makes the sampling repeatable, contrary to the question.

The inclusion of the CURRENT_TIMESTAMP() function in the second query prevents it from re-using the first query's persisted result cache because this function makes each execution unique due to the constantly changing timestamp. According to theSnowflake documentation, ''The query does not include non-reusable functions, which return different results for successive runs of the same query. UUID_STRING, RANDOM, and RANDSTR are good examples of non-reusable functions.'' The CURRENT_TIMESTAMP() function is another example of a non-reusable function, as it returns the current date and time at the start of query execution, which varies for each run. Therefore, the second query is not identical to the first query, and the result cache is not reused. The other options are either incorrect or irrelevant to the question. Option B is incorrect, as the CURRENT_DATE() function is a reusable function, as it returns the same value for all queries executed within the same day. Option C is irrelevant, as the virtual warehouse used to execute the query does not affect the result cache reuse. Option D is also irrelevant, as the role used to execute the query does not affect the result cache reuse, as long as the role has the necessary access privileges for all the tables used in the query.

To retrieve a list of schemas deleted within the last 2 days from the DB1 database, you need a metadata view that includes historical data, including dropped (deleted) objects.

Let's review the options:

B. SNOWFLAKE.ACCOUNT_USAGE.SCHEMATA

This is the correct choice because:

It includes metadata for all schemas, even deleted ones, within the retention period.

It contains a DELETED column and a DELETED_ON timestamp column.

You can filter rows with:

sql

CopyEdit

SELECT *

FROM SNOWFLAKE.ACCOUNT_USAGE.SCHEMATA

WHERE DELETED IS TRUE

AND DELETED_ON >= DATEADD(DAY, -2, CURRENT_TIMESTAMP())

AND CATALOG_NAME = 'DB1';

A. SHOW SCHEMAS IN DATABASE DB1;

Only shows current (active) schemas --- does not include deleted schemas.

C. DB1.INFORMATION_SCHEMA.SCHEMATA

Like option A, this view only includes active schemas in the current database.

No info on deleted schemas is retained.

D. SNOWFLAKE.ACCOUNT_USAGE.DATABASES

This metadata view tracks databases, not individual schemas.

SnowPro Administrator Reference:

SNOWFLAKE.ACCOUNT_USAGE.SCHEMATA documentation

Metadata includes both active and deleted schemas (within retention window).

The REFRESH DATABASE command is used to refresh a secondary database with the latest data and metadata from the primary database1. The command must be executed in the target account where the secondary database resides2. Therefore, the answer is A, as the location is AWS_WEST and the command is REFRESH DATABASE PROD_AWS_WEST REFRESH. The other options are incorrect because they either use the wrong location, the wrong command, or the wrong database name.

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.

According to the Snowflake documentation, when a database or schema is cloned, the clone inherits all granted privileges on the clones of all child objects contained in the source object, such as tables and views. However, the clone of the container itself does not inherit the privileges granted on the source container. Therefore, the ANALYST role will have SELECT privilege on all tables and views in DatabaseA_clone.Schema1, but not USAGE privilege on the database or schema. The type of view (secure or non-secure) does not affect the cloning of privileges.