Free Snowflake ARA-C01 Exam Dumps May 2026

To accommodate the diverse needs of different teams and use cases within a company, a flexible and multi-faceted approach to data modeling is required.

Option B: By creating a raw database for landing and persisting raw data, you ensure that the Data Science team has access to unprocessed data for machine learning model development. This aligns with the best practices of having a staging area or raw data zone in a modern data architecture where raw data is ingested before being transformed or processed for different use cases.

Option C: Having profile-specific databases means creating targeted databases that are designed to meet the specific requirements of each user profile or team within the company. For the Finance and Vendor Management teams, the data can be structured and optimized for reporting and visualization. For the Sales team, the database can include engineered and protected data that is suitable for data monetization efforts. This strategy not only aligns data with usage patterns but also helps in managing data access and security policies effectively.

Dynamic Data Masking is a feature that allows masking sensitive data in query results based on the role of the user who executes the query. A masking policy is a user-defined function that specifies the masking logic and can be applied to one or more columns in one or more tables. A masking policy that is currently set on a table can be dropped using the ALTER TABLE command. A single masking policy can be applied to columns in different tables using the ALTER TABLE command with the SET MASKING POLICY clause. The other options are either incorrect or not supported by Snowflake. A masking policy cannot be applied to the value column of an external table, as external tables do not support column-level security. The role that creates the masking policy will not always see unmasked data in query results, as the masking policy can be applied to the owner role as well. A masking policy cannot be applied to a column with the GEOGRAPHY data type, as Snowflake only supports masking policies for scalar data types.Reference:Snowflake Documentation: Dynamic Data Masking,Snowflake Documentation: ALTER TABLE

According to the Snowflake documentation, the best practices for choosing clustering keys are:

Choose columns that are frequently used in join predicates. This can improve the join performance by reducing the number of micro-partitions that need to be scanned and joined.

Choose columns that are most actively used in selective filters. This can improve the scan efficiency by skipping micro-partitions that do not match the filter predicates.

Avoid using low cardinality columns, such as gender or country, as clustering keys. This can result in poor clustering and high maintenance costs.

Avoid using TIMESTAMP columns with nanoseconds, as they tend to have very high cardinality and low correlation with other columns. This can also result in poor clustering and high maintenance costs.

Avoid using columns with duplicate values or NULLs, as they can cause skew in the clustering and reduce the benefits of pruning.

Cluster on multiple columns if the queries use multiple filters or join predicates. This can increase the chances of pruning more micro-partitions and improve the compression ratio.

Clustering is not always useful, especially for small or medium-sized tables, or tables that are not frequently queried or updated. Clustering can incur additional costs for initially clustering the data and maintaining the clustering over time.

Clustering Keys & Clustered Tables | Snowflake Documentation

[Considerations for Choosing Clustering for a Table | Snowflake Documentation]

The correct answer is A because it grants the specific privilege to create a materialized view on the schema EDW.STG_SCHEMA to the user USER_01 directly.

Option B is incorrect because it grants the privilege to create a materialized view on the entire database EDW, which is too broad and unnecessary. Also, there is a typo in the user name (USERJD1 instead of USER_01).

Option C is incorrect because it grants the privilege to create a materialized view on a different schema (ECW.STG_SCHEKA instead of EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose.

Option D is incorrect because it grants the privilege to create a materialized view on an invalid object (EDW.STG_SCHEMA is not a valid schema name, it should be EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose.Reference:

Snowflake Documentation: CREATE MATERIALIZED VIEW

Snowflake Documentation: Working with Materialized Views

[Snowflake Documentation: GRANT Privileges on a Schema]

The image shows a SQL query that can be used to identify which queries are spilled to remote storage and suggests changing the warehouse parameters to address this issue. Spilling to remote storage occurs when the memory allocated to a warehouse is insufficient to process a query, and Snowflake uses disk or cloud storage as a temporary cache. This can significantly slow down the query performance and increase the cost. To troubleshoot this issue, a Snowflake Architect can run the query shown in the image to find out which queries are spilling, how much data they are spilling, and which warehouses they are using.Then, the architect can adjust the warehouse size, type, or scaling policy to provide enough memory for the queries and avoid spilling12.Reference:

Recognizing Disk Spilling

Managing the Kafka Connector

Partitioning an external table is a technique that improves the performance of queries executed against the table by reducing the amount of data scanned. Partitioning an external table involves creating one or more partition columns that define how the table is logically divided into subsets of data based on the values in those columns. The partition columns can be derived from the file metadata (such as file name, path, size, or modification time) or from the file content (such as a column value or a JSON attribute).Partitioning an external table allows the query optimizer to prune the files that do not match the query predicates, thus avoiding unnecessary data scanning and processing2

The other options are not effective steps for improving the performance of queries executed against an external table:

Shorten the names of the source files. This option does not have any impact on the query performance, as the file names are not used for query processing.The file names are only used for creating the external table and displaying the query results3

Convert the source files' character encoding to UTF-8. This option does not affect the query performance, as Snowflake supports various character encodings for external table files, such as UTF-8, UTF-16, UTF-32, ISO-8859-1, and Windows-1252.Snowflake automatically detects the character encoding of the files and converts them to UTF-8 internally for query processing4

Use an internal stage instead of an external stage to store the source files. This option is not applicable, as external tables can only reference files stored in external stages, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage.Internal stages are used for loading data into internal tables, not external tables5Reference:

1: SnowPro Advanced: Architect | Study Guide

2: Snowflake Documentation | Partitioning External Tables

3: Snowflake Documentation | Creating External Tables

4: Snowflake Documentation | Supported File Formats and Compression for Staged Data Files

5: Snowflake Documentation | Overview of Stages

:SnowPro Advanced: Architect | Study Guide

:Partitioning External Tables

:Creating External Tables

:Supported File Formats and Compression for Staged Data Files

:Overview of Stages

The architecture in the image shows a Snowflake data platform with two databases, DB1 and DB2, and two schemas, SH1 and SH2. DB1 contains a table TBL1 and a stage STAGE1. DB2 contains a table TBL2. The image also shows a snippet of code written in SQL language that copies data from STAGE1 to TBL2 using a file format FF PIPE 1.

To copy data from DB1 to TBL2, there are two possible options among the choices given:

Option B: Use a named external stage that references STAGE1. This option requires creating an external stage object in DB2.SH2 that points to the same location as STAGE1 in DB1.SH1.The external stage can be created using theCREATE STAGEcommand with theURLparameter specifying the location of STAGE11. For example:

SQLAI-generated code. Review and use carefully.More info on FAQ.

use database DB2;

use schema SH2;

create stage EXT_STAGE1

url = @DB1.SH1.STAGE1;

Then, the data can be copied from the external stage to TBL2 using theCOPY INTOcommand with theFROMparameter specifying the external stage name and theFILE FORMATparameter specifying the file format name2. For example:

SQLAI-generated code. Review and use carefully.More info on FAQ.

copy into TBL2

from @EXT_STAGE1

file format = (format name = DB1.SH1.FF PIPE 1);

Option E: Use a cross-database query to select data from TBL1 and insert into TBL2. This option requires using theINSERT INTOcommand with theSELECTclause to query data from TBL1 in DB1.SH1 and insert it into TBL2 in DB2.SH2.The query must use the fully-qualified names of the tables, including the database and schema names3. For example:

SQLAI-generated code. Review and use carefully.More info on FAQ.

use database DB2;

use schema SH2;

insert into TBL2

select * from DB1.SH1.TBL1;

The other options are not valid because:

Option A: It uses an invalid syntax for theCOPY INTOcommand.TheFROMparameter cannot specify a table name, only a stage name or a file location2.

Option C: It uses an invalid syntax for theCOPY INTOcommand.TheFILE FORMATparameter cannot specify a stage name, only a file format name or options2.

Option D: It uses an invalid syntax for theCREATE STAGEcommand.TheURLparameter cannot specify a table name, only a file location1.

1: CREATE STAGE | Snowflake Documentation

2: COPY INTO table | Snowflake Documentation

3: Cross-database Queries | Snowflake Documentation

A multi-cluster warehouse is a virtual warehouse that can scale compute resources by adding or removing clusters based on the workload demand. A multi-cluster warehouse can improve the performance of a particular workload by reducing the query queue time and the data spillage to local storage. To identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse, you need to analyze the query history and look for the following indicators:

High average queued load: This metric shows the average number of queries waiting in the queue for each warehouse cluster. A high value indicates that the warehouse is overloaded and cannot handle the concurrency demand.

High bytes spilled to local storage: This metric shows the amount of data that was spilled from memory to local disk during query processing. A high value indicates that the warehouse size is too small and cannot fit the data in memory.

High variation in workload: This metric shows the fluctuation in the number of queries submitted to the warehouse over time. A high variation indicates that the workload is unpredictable and dynamic, and requires a flexible scaling policy.

The query in option C is the best one to identify these indicators, as it selects the date, warehouse name, bytes spilled to local storage, and sum of average queued load from the query history table, and filters the results where bytes spilled to local storage is greater than zero. This query will show the days and warehouses that experienced data spillage and high queue time, and could benefit from a multi-cluster warehouse with auto-scale mode.

The query in option A is not correct, as it only selects the date and warehouse name, and does not include any metrics to measure the performance of the workload. The query in option B is not correct, as it selects the date, warehouse name, and average execution time, which is not a good indicator of the need for a multi-cluster warehouse. The query in option D is not correct, as it selects the date, warehouse name, and average credits used, which is not a good indicator of the need for a multi-cluster warehouse either.

To maximize memory and compute resources for a Snowpark stored procedure, you need to set the MAX_CONCURRENCY_LEVEL parameter for the warehouse that executes the stored procedure. This parameter determines the maximum number of concurrent queries that can run on a single warehouse. By setting it to 16, you ensure that the warehouse can use all the available CPU cores and memory on a single node, which is the optimal configuration for Snowpark-optimized warehouses. This will improve the performance and efficiency of the stored procedure, as it will not have to share resources with other queries or nodes. The other options are incorrect because they either do not change the MAX_CONCURRENCY_LEVEL parameter, or they set it to a lower value than 16, which will reduce the memory and compute resources for the stored procedure.Reference:

[Snowpark-optimized Warehouses]1

[Training Machine Learning Models with Snowpark Python]2

[Snowflake Shorts: Snowpark Optimized Warehouses]3

The architecture in the image shows a Snowflake data platform with two databases, DB1 and DB2, and two schemas, SH1 and SH2. DB1 contains a table TBL1 and a stage STAGE1. DB2 contains a table TBL2. The image also shows a snippet of code written in SQL language that copies data from STAGE1 to TBL2 using a file format FF PIPE 1.

To copy data from DB1 to TBL2, there are two possible options among the choices given:

Option B: Use a named external stage that references STAGE1. This option requires creating an external stage object in DB2.SH2 that points to the same location as STAGE1 in DB1.SH1.The external stage can be created using theCREATE STAGEcommand with theURLparameter specifying the location of STAGE11. For example:

SQLAI-generated code. Review and use carefully.More info on FAQ.

use database DB2;

use schema SH2;

create stage EXT_STAGE1

url = @DB1.SH1.STAGE1;

Then, the data can be copied from the external stage to TBL2 using theCOPY INTOcommand with theFROMparameter specifying the external stage name and theFILE FORMATparameter specifying the file format name2. For example:

SQLAI-generated code. Review and use carefully.More info on FAQ.

copy into TBL2

from @EXT_STAGE1

file format = (format name = DB1.SH1.FF PIPE 1);

Option E: Use a cross-database query to select data from TBL1 and insert into TBL2. This option requires using theINSERT INTOcommand with theSELECTclause to query data from TBL1 in DB1.SH1 and insert it into TBL2 in DB2.SH2.The query must use the fully-qualified names of the tables, including the database and schema names3. For example:

SQLAI-generated code. Review and use carefully.More info on FAQ.

use database DB2;

use schema SH2;

insert into TBL2

select * from DB1.SH1.TBL1;

The other options are not valid because:

Option A: It uses an invalid syntax for theCOPY INTOcommand.TheFROMparameter cannot specify a table name, only a stage name or a file location2.

Option C: It uses an invalid syntax for theCOPY INTOcommand.TheFILE FORMATparameter cannot specify a stage name, only a file format name or options2.

Option D: It uses an invalid syntax for theCREATE STAGEcommand.TheURLparameter cannot specify a table name, only a file location1.

1: CREATE STAGE | Snowflake Documentation

2: COPY INTO table | Snowflake Documentation

3: Cross-database Queries | Snowflake Documentation