Provide Valid ARA-C01 Dumps To Help You Prepare For SnowPro Advanced Architect Certification Exam Jun 25, 2026
Snowflake ARA-C01 Dumps Questions [2026] Pass for ARA-C01 Exam
One of the unique features of the SnowPro Advanced Architect Certification exam is that it is an online, proctored exam. This means that candidates can take the exam from anywhere in the world, at any time, while being monitored by a proctor through a webcam. This format ensures the integrity and security of the exam, while also providing convenience and flexibility to candidates.
NEW QUESTION # 37
An Architect is troubleshooting a query with poor performance using the QUERY_HIST0RY function. The Architect observes that the COMPILATIONJHME is greater than the EXECUTIONJTIME.
What is the reason for this?
- A. The query is reading from remote storage.
- B. The query is processing a very large dataset.
- C. The query has overly complex logic.
- D. The query is queued for execution.
Answer: C
Explanation:
Compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. It also involves some pruning of partition files, making the query execution efficient2 If the compilation time is greater than the execution time, it means that the optimizer spent more time analyzing the query than actually running it. This could indicate that the query has overly complex logic, such as multiple joins, subqueries, aggregations, or expressions. The complexity of the query could also affect the size and quality of the query plan, which could impact the performance of the query3 To reduce the compilation time, the Architect can try to simplify the query logic, use views or common table expressions (CTEs) to break down the query into smaller parts, or use hints to guide the optimizer. The Architect can also use the EXPLAIN command to examine the query plan and identify potential bottlenecks or inefficiencies4 References:
* 1: SnowPro Advanced: Architect | Study Guide 5
* 2: Snowflake Documentation | Query Profile Overview 6
* 3: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time 7
* 4: Snowflake Documentation | Optimizing Query Performance 8
* : SnowPro Advanced: Architect | Study Guide
* : Query Profile Overview
* : Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time
* : Optimizing Query Performance
NEW QUESTION # 38
Cloud services can help in pruning even if the columns are variant columns.
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 39
How do Snowflake databases that are created from shares differ from standard databases that are not created from shares? (Choose three.)
- A. Shared databases can also be created as transient databases.
- B. Shared databases must be refreshed in order for new data to be visible.
- C. Shared databases will have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share.
- D. Shared databases are not supported by Time Travel.
- E. Shared databases cannot be cloned.
- F. Shared databases are read-only.
Answer: D,E,F
Explanation:
Explanation
According to the SnowPro Advanced: Architect documents and learning resources, the ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares are:
* Shared databases are read-only. This means that the data consumers who access the shared databases cannot modify or delete the data or the objects in the databases. The data providers who share the databases have full control over the data and the objects, and can grant or revoke privileges on them1.
* Shared databases cannot be cloned. This means that the data consumers who access the shared databases cannot create a copy of the databases or the objects in the databases. The data providers who share the databases can clone the databases or the objects, but the clones are not automatically shared2.
* Shared databases are not supported by Time Travel. This means that the data consumers who access the shared databases cannot use the AS OF clause to query historical data or restore deleted data. The data providers who share the databases can use Time Travel on the databases or the objects, but the historical data is not visible to the data consumers3.
The other options are incorrect because they are not ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares. Option B is incorrectbecause shared databases do not need to be refreshed in order for new data to be visible. The data consumers who access the shared databases can see the latest data as soon as the data providers update the data1. Option E is incorrect because shared databases will not have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share. The data consumers who access the shared databases can only see the objects that the data providers grant to the share, and the PUBLIC and INFORMATION_SCHEMA schemas are not granted by default4. Option F is incorrect because shared databases cannot be created as transient databases. Transient databases are databases that do not support Time Travel or Fail-safe, and can be dropped without affecting the retention period of the data. Shared databases are always created as permanent databases, regardless of the type of the source database5. References: Introduction to Secure Data Sharing | Snowflake Documentation, Cloning Objects | Snowflake Documentation, Time Travel | Snowflake Documentation, Working with Shares | Snowflake Documentation, CREATE DATABASE | Snowflake Documentation
NEW QUESTION # 40
Based on the Snowflake object hierarchy, what securable objects belong directly to a Snowflake account?
(Select THREE).
- A. Table
- B. Schema
- C. Warehouse
- D. Stage
- E. Role
- F. Database
Answer: C,E,F
Explanation:
A securable object is an entity to which access can be granted in Snowflake. Securable objects include databases, schemas, tables, views, stages, pipes, functions, procedures, sequences, tasks, streams, roles, warehouses, and shares1.
The Snowflake object hierarchy is a logical structure that organizes the securable objects in a nested manner.
The top-most container is the account, which contains all the databases, roles, and warehouses for the customer organization. Each database contains schemas, which in turn contain tables, views, stages, pipes, functions, procedures, sequences, tasks, and streams. Each role can be granted privileges on other roles or securable objects. Each warehouse can be used to execute queries on securable objects2.
Based on the Snowflake object hierarchy, the securable objects that belong directly to a Snowflake account are databases, roles, and warehouses. These objects are created and managed at the account level, and do not depend on any other securable object. The other options are not correct because:
Schemas belong to databases, not to accounts. A schema must be created within an existing database3.
Tables belong to schemas, not to accounts. A table must be created within an existing schema4.
Stages belong to schemas or tables, not to accounts. A stage must be created within an existing schema or table.
1: Overview of Access Control | Snowflake Documentation
2: Securable Objects | Snowflake Documentation
3: CREATE SCHEMA | Snowflake Documentation
4: CREATE TABLE | Snowflake Documentation
[5]: CREATE STAGE | Snowflake Documentation
NEW QUESTION # 41
Is it possible for a data provider account with a Snowflake Business Critical edition to share data with an Enterprise edition data consumer account?
- A. A Business Critical account cannot be a data sharing provider to an Enterprise consumer. Any consumer accounts must also be Business Critical.
- B. If a user in the provider account with a share owning role sets share_restrictions to False when adding an Enterprise consumer account, it can import the share.
- C. If a user in the provider account with role authority to create or alter share adds an Enterprise account as a consumer, it can import the share.
- D. If a user in the provider account with a share owning role which also has override share restrictions privilege share_restrictions set to False when adding an Enterprise consumer account, it can import the share.
Answer: D
NEW QUESTION # 42
An Architect wants to build an automated ETL pipeline that reads data from an external stage using an external table, performs transformations on changed data, joins with dimension tables, and loads results into a target table.
What should the Architect use?
- A. Dynamic tables
- B. Streams and tasks
- C. Materialized views
- D. Snowpipe with auto-ingest
Answer: B
Explanation:
Streams and tasks provide Snowflake's native framework for building automated, incremental ETL pipelines.
A stream can track changes in the external table (or staging table), while tasks orchestrate the execution of transformation logic and loading into target tables (Answer A).
This approach supports complex transformations, joins with dimension tables, and controlled scheduling or event-driven execution. Materialized views cannot perform joins with arbitrary tables and are not suitable for complex ETL. Dynamic tables simplify transformations but are not designed to consume change data directly from external tables. Snowpipe focuses on ingestion only and does not support downstream transformations.
SnowPro Architect exams frequently test understanding of when to use streams and tasks versus newer abstractions like dynamic tables.
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NEW QUESTION # 43
Assuming all Snowflake accounts are using an Enterprise edition or higher, in which development and testing scenarios would be copying of data be required, and zero-copy cloning not be suitable? (Select TWO).
- A. Developers create their own datasets to work against transformed versions of the live data.
- B. Production and development run in different databases in the same account, and Developers need to see production-like data but with specific columns masked.
- C. Data is in a production Snowflake account that needs to be provided to Developers in a separate development/testing Snowflake account in the same cloud region.
- D. The release process requires pre-production testing of changes with data of production scale and complexity. For security reasons, pre-production also runs in the production account.
- E. Developers create their own copies of a standard test database previously created for them in the development account, for their initial development and unit testing.
Answer: A,C
Explanation:
Zero-copy cloning is a feature that allows creating a clone of a table, schema, or database without physically copying the data. Zero-copy cloning is suitable for scenarios where the cloned object needs to have the same data and metadata as the original object, and where the cloned object does not need to be modified or updated frequently. Zero-copy cloning is also suitable for scenarios where the cloned object needs to be shared within the same Snowflake account or across different accounts in the same cloud region2 However, zero-copy cloning is not suitable for scenarios where the cloned object needs to have different data or metadata than the original object, or where the cloned object needs to be modified or updated frequently. Zero-copy cloning is also not suitable for scenarios where the cloned object needs to be shared across different accounts in different cloud regions. In these scenarios, copying of data would be required, either by using the COPY INTO command or by using data sharing with secure views3 The following are examples of development and testing scenarios where copying of data would be required, and zero-copy cloning would not be suitable:
Developers create their own datasets to work against transformed versions of the live data. This scenario requires copying of data because the developers need to modify the data or metadata of the cloned object to perform transformations, such as adding, deleting, or updating columns, rows, or values. Zero-copy cloning would not be suitable because it would create a read-only clone that shares the same data and metadata as the original object, and any changes made to the clone would affect the original object as well4 Data is in a production Snowflake account that needs to be provided to Developers in a separate development/testing Snowflake account in the same cloud region. This scenario requires copying of data because the data needs to be shared across different accounts in the same cloud region. Zero-copy cloning would not be suitable because it would create a clone within the same account as the original object, and it would not allow sharing the clone with another account. To share data across different accounts in the same cloud region, data sharing with secure views or COPY INTO command can be used5 The following are examples of development and testing scenarios where zero-copy cloning would be suitable, and copying of data would not be required:
Production and development run in different databases in the same account, and Developers need to see production-like data but with specific columns masked. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the production database in the development database, and the clone can have the same data and metadata as the original database. To mask specific columns, secure views can be created on top of the clone, and the developers can access the secure views instead of the clone directly6 Developers create their own copies of a standard test database previously created for them in the development account, for their initial development and unit testing. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the standard test database for each developer, and the clone can have the same data and metadata as the original database. The developers can use the clone for their initial development and unit testing, and any changes made to the clone would not affect the original database or other clones7 The release process requires pre-production testing of changes with data of production scale and complexity. For security reasons, pre-production also runs in the production account. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the production database in the pre-production database, and the clone can have the same data and metadata as the original database. The pre-production testing can use the clone to test the changes with data of production scale and complexity, and any changes made to the clone would not affect the original database or the production environment8 Reference:
1: SnowPro Advanced: Architect | Study Guide 9
2: Snowflake Documentation | Cloning Overview
3: Snowflake Documentation | Loading Data Using COPY into a Table
4: Snowflake Documentation | Transforming Data During a Load
5: Snowflake Documentation | Data Sharing Overview
6: Snowflake Documentation | Secure Views
7: Snowflake Documentation | Cloning Databases, Schemas, and Tables
8: Snowflake Documentation | Cloning for Testing and Development
: SnowPro Advanced: Architect | Study Guide
: Cloning Overview
: Loading Data Using COPY into a Table
: Transforming Data During a Load
: Data Sharing Overview
: Secure Views
: Cloning Databases, Schemas, and Tables
: Cloning for Testing and Development
NEW QUESTION # 44
Company A has recently acquired company B.
The Snowflake deployment for company B is located in the Azure West Europe region.
As part of the integration process, an Architect has been asked to consolidate company B's sales data into company A's Snowflake account which is located in the AWS us-east-1 region.
How can this requirement be met?
- A. Export the sales data from company B's Snowflake account as CSV files, and transfer the files to company A's Snowflake account. Import the data using Snowflake's data loading capabilities.
- B. Replicate the sales data from company B's Snowflake account into company A's Snowflake account using cross-region data replication within Snowflake. Configure a direct share from company B's account to company A's account.
- C. Build a custom data pipeline using Azure Data Factory or a similar tool to extract the sales data from company B's Snowflake account. Transform the data, then load it into company A's Snowflake account.
- D. Migrate company B's Snowflake deployment to the same region as company A's Snowflake deployment, ensuring data locality. Then perform a direct database-to-database merge of the sales data.
Answer: B
Explanation:
The best way to meet the requirement of consolidating company B's sales data into company A's Snowflake account is to use cross-region data replication within Snowflake. This feature allows data providers to securely share data with data consumers across different regions and cloud platforms. By replicating the sales data from company B's account in Azure West Europe region to company A's account in AWS us-east-1 region, the data will be synchronized and available for consumption. To enable data replication, the accounts must be linked and replication must be enabled by a user with the ORGADMIN role. Then, a replication group must be created and the sales database must be added to the group. Finally, a direct share must be configured from company B's account to company A's account to grant access to the replicated data. This option is more efficient and secure than exporting and importing data using CSV files or migrating the entire Snowflake deployment to another region or cloud platform. It also does not require building a custom data pipeline using external tools.
Reference:
Sharing data securely across regions and cloud platforms
Introduction to replication and failover
Replication considerations
Replicating account objects
NEW QUESTION # 45
When a cloned table is replicated to a secondary database, the data also gets replicated in the secondary database
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 46
Following objects can be cloned in snowflake
- A. Transient table
- B. Internal stages
- C. External tables
- D. Temporary table
- E. Permanent table
Answer: A,C,E
Explanation:
Snowflake supports cloning of various objects, such as databases, schemas, tables, stages, file formats, sequences, streams, tasks, and roles. Cloning creates a copy of an existing object in the system without copying the data or metadata. Cloning is also known as zero-copy cloning1.
Among the objects listed in the question, the following ones can be cloned in Snowflake:
Permanent table: A permanent table is a type of table that has a Fail-safe period and a Time Travel retention period of up to 90 days. A permanent table can be cloned using the CREATE TABLE ... CLONE command2. Therefore, option A is correct.
Transient table: A transient table is a type of table that does not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. A transient table can also be cloned using the CREATE TABLE ... CLONE command2. Therefore, option B is correct.
External table: An external table is a type of table that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table can be cloned using the CREATE EXTERNAL TABLE ... CLONE command3. Therefore, option D is correct.
The following objects listed in the question cannot be cloned in Snowflake:
Temporary table: A temporary table is a type of table that is automatically dropped when the session ends or the current user logs out. Temporary tables do not support cloning4. Therefore, option C is incorrect.
Internal stage: An internal stage is a type of stage that is managed by Snowflake and stores files in Snowflake's internal cloud storage. Internal stages do not support cloning5. Therefore, option E is incorrect.
NEW QUESTION # 47
What is a characteristic of Role-Based Access Control (RBAC) as used in Snowflake?
- A. Privileges can be granted at the database level and can be inherited by all underlying objects.
- B. A user can create managed access schemas to support future grants and ensure only schema owners can grant privileges to other roles.
- C. A user can create managed access schemas to support current and future grants and ensure only object owners can grant privileges to other roles.
- D. A user can use a "super-user" access along with securityadmin to bypass authorization checks and access all databases, schemas, and underlying objects.
Answer: B
Explanation:
Role-Based Access Control (RBAC) is the Snowflake Access Control Framework that allows privileges to be granted by object owners to roles, and roles, in turn, can be assigned to users to restrict or allow actions to be performed on objects. A characteristic of RBAC as used in Snowflake is:
Privileges can be granted at the database level and can be inherited by all underlying objects. This means that a role that has a certain privilege on a database, such as CREATE SCHEMA or USAGE, can also perform the same action on any schema, table, view, or other object within that database, unless explicitly revoked. This simplifies the access control management and reduces the number of grants required.
A user can create managed access schemas to support future grants and ensure only schema owners can grant privileges to other roles. This means that a user can create a schema with the MANAGED ACCESS option, which changes the default behavior of object ownership and privilege granting within the schema. In a managed access schema, object owners lose the ability to grant privileges on their objects to other roles, and only the schema owner or a role with the MANAGE GRANTS privilege can do so. This enhances the security and governance of the schema and its objects.
The other options are not characteristics of RBAC as used in Snowflake:
A user can use a "super-user" access along with securityadmin to bypass authorization checks and access all databases, schemas, and underlying objects. This is not true, as there is no such thing as a "super-user" access in Snowflake. The securityadmin role is a predefined role that can manage users and roles, but it does not have any privileges on any database objects by default. To access any object, the securityadmin role must be explicitly granted the appropriate privilege by the object owner or another role with the grant option.
A user can create managed access schemas to support current and future grants and ensure only object owners can grant privileges to other roles. This is not true, as this contradicts the definition of a managed access schema. In a managed access schema, object owners cannot grant privileges on their objects to other roles, and only the schema owner or a role with the MANAGE GRANTS privilege can do so.
Reference:
Overview of Access Control
A Functional Approach For Snowflake's Role-Based Access Controls
Snowflake Role-Based Access Control simplified
Snowflake RBAC security prefers role inheritance to role composition
Overview of Snowflake Role Based Access Control
NEW QUESTION # 48
An Architect on a new project has been asked to design an architecture that meets Snowflake security, compliance, and governance requirements as follows:
1) Use Tri-Secret Secure in Snowflake
2) Share some information stored in a view with another Snowflake customer
3) Hide portions of sensitive information from some columns
4) Use zero-copy cloning to refresh the non-production environment from the production environment To meet these requirements, which design elements must be implemented? (Choose three.)
- A. Create a materialized view.
- B. Define row access policies.
- C. Use the Enterprise edition of Snowflake.
- D. Use the Business-Critical edition of Snowflake.
- E. Use Dynamic Data Masking.
- F. Create a secure view.
Answer: D,E,F
Explanation:
These three design elements are required to meet the security, compliance, and governance requirements for the project.
To use Tri-Secret Secure in Snowflake, the Business Critical edition of Snowflake is required. This edition provides enhanced data protection features, such as customer-managed encryption keys, that are not available in lower editions. Tri-Secret Secure is a feature that combines a Snowflake-maintained key and a customer-managed key to create a composite master key to encrypt the data in Snowflake1.
To share some information stored in a view with another Snowflake customer, a secure view is recommended. A secure view is a view that hides the underlying data and the view definition from unauthorized users. Only the owner of the view and the users who are granted the owner's role can see the view definition and the data in the base tables of the view2. A secure view can be shared with another Snowflake account using a data share3.
To hide portions of sensitive information from some columns, Dynamic Data Masking can be used. Dynamic Data Masking is a feature that allows applying masking policies to columns to selectively mask plain-text data at query time. Depending on the masking policy conditions and the user's role, the data can be fully or partially masked, or shown as plain-text4.
NEW QUESTION # 49
What is a key consideration when setting up search optimization service for a table?
- A. Search optimization service can significantly improve query performance on partitioned external tables.
- B. Search optimization service works best with a column that has a minimum of 100 K distinct values.
- C. The table must be clustered with a key having multiple columns for effective search optimization.
- D. Search optimization service can help to optimize storage usage by compressing the data into a GZIP format.
Answer: B
Explanation:
Search optimization service is a feature of Snowflake that can significantly improve the performance of certain types of lookup and analytical queries on tables. Search optimization service creates and maintains a persistent data structure called a search access path, which keeps track of which values of the table's columns might be found in each of its micro-partitions, allowing some micro-partitions to be skipped when scanning the table1.
Search optimization service can significantly improve query performance on partitioned external tables, which are tables that store data in external locations such as Amazon S3 or Google Cloud Storage. Partitioned external tables can leverage the search access path to prune the partitions that do not contain the relevant data, reducing the amount of data that needs to be scanned and transferred from the external location2.
The other options are not correct because:
* A. Search optimization service works best with a column that has a high cardinality, which means that the column has many distinct values. However, there is no specific minimum number of distinct values required for search optimization service to work effectively. The actual performance improvement depends on the selectivity of the queries and the distribution of the data1.
* C. Search optimization service does not help to optimize storage usage by compressing the data into a GZIP format. Search optimization service does not affect the storage format or compression of the data, which is determined by the file format options of the table. Search optimization service only creates an additional data structure that is stored separately from the table data1.
* D. The table does not need to be clustered with a key having multiple columns for effective search optimization. Clustering is a feature of Snowflake that allows ordering the data in a table or a partitioned external table based on one or more clustering keys. Clustering can improve the performance of queries that filter on the clustering keys, as it reduces the number of micro-partitions that need to be scanned. However, clustering is not required for search optimization service to work, as search optimization service can skip micro-partitions based on any column that has a search access path, regardless of the clustering key3.
References:
* 1: Search Optimization Service | Snowflake Documentation
* 2: Partitioned External Tables | Snowflake Documentation
* 3: Clustering Keys | Snowflake Documentation
NEW QUESTION # 50
An Architect has been asked to clone schema STAGING as it looked one week ago, Tuesday June 1st at 8:00 AM, to recover some objects.
The STAGING schema has 50 days of retention.
The Architect runs the following statement:
CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp => '2021-06-01 08:00:00'); The Architect receives the following error: Time travel data is not available for schema STAGING. The requested time is either beyond the allowed time travel period or before the object creation time.
The Architect then checks the schema history and sees the following:
CREATED_ON|NAME|DROPPED_ON
2021-06-02 23:00:00 | STAGING | NULL
2021-05-01 10:00:00 | STAGING | 2021-06-02 23:00:00
How can cloning the STAGING schema be achieved?
- A. Modify the statement: CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp =>
'2021-05-01 10:00:00'); - B. Rename the STAGING schema and perform an UNDROP to retrieve the previous STAGING schema version, then run the CLONE statement.
- C. Undrop the STAGING schema and then rerun the CLONE statement.
- D. Cloning cannot be accomplished because the STAGING schema version was not active during the proposed Time Travel time period.
Answer: B
Explanation:
Explanation
* The error message indicates that the schema STAGING does not have time travel data available for the requested timestamp, because the current version of the schema was created on2021-06-02 23:00:00, which is after the timestamp of 2021-06-01 08:00:00. Therefore, the CLONE statement cannot access the historical data of the schema at that point in time.
* Option A is incorrect, because undropping the STAGING schema will not restore the previous version of the schema that was active on 2021-06-01 08:00:00. Instead, it will create a new version of the schema with the same name and no data or objects.
* Option B is incorrect, because modifying the timestamp to 2021-05-01 10:00:00 will not clone the schema as it looked one week ago, but as it looked when it was first created. This may not reflect the desired state of the schema and its objects.
* Option C is correct, because renaming the STAGING schema and performing an UNDROP to retrieve the previous STAGING schema version will restore the schema that was dropped on 2021-06-02
23:00:00. This schema has time travel data available for the requested timestamp of 2021-06-01
08:00:00, and can be cloned using the CLONE statement.
* Option D is incorrect, because cloning can be accomplished by using the UNDROP command to access the previous version of the schema that was active during the proposed time travel period.
References: : Cloning Considerations : Understanding & Using Time Travel : CREATE <object> ... CLONE
NEW QUESTION # 51
SNOWPIPE_AUTO_INGEST is supported for external stages only
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 52
What are characteristics of the use of transactions in Snowflake? (Select TWO).
- A. A transaction can be started explicitly by executing a begin transaction statement and end explicitly by executing an end transaction statement.
- B. Explicit transactions can contain DDL, DML, and query statements.
- C. A transaction can be started explicitly by executing a begin work statement and end explicitly by executing a commit work statement.
- D. Explicit transactions should contain only DML statements and query statements. All DDL statements implicitly commit active transactions.
- E. The autocommit setting can be changed inside a stored procedure.
Answer: B,C
Explanation:
A: Snowflake's transactions can indeed include DDL (Data Definition Language), DML (Data Manipulation Language), and query statements. When executed within a transaction block, they all contribute to the atomicity of the transaction-either all of them commit together or none at all.
C: Snowflake supports explicit transaction control through the use of the BEGIN TRANSACTION (or simply BEGIN) and COMMIT statements. Alternatively, the BEGIN WORK and COMMIT WORK syntax is also supported, which is a standard SQL syntax for initiating and ending transactions, respectively.
Note: The END TRANSACTION statement is not used in Snowflake to end a transaction; the correct statement is COMMIT or COMMIT WORK.
NEW QUESTION # 53
Why does a conditional multi-table insert option support the Data Vault data model?
- A. Data can be inserted in parallel to dimensions and facts using surrogate keys.
- B. Data can be inserted in parallel to hubs and satellites using surrogate keys.
- C. Data can be inserted in sequence to dimensions and facts using surrogate keys.
- D. Data can be inserted in sequence to hubs and satellites using surrogate keys.
Answer: B
Explanation:
The Data Vault modeling approach separates data into hubs (business keys), links, and satellites, often requiring simultaneous insertion of related records derived from the same source dataset. Snowflake's conditional multi-table INSERT enables a single source query to populate multiple target tables in parallel based on conditional logic. This capability aligns well with Data Vault patterns, where hubs and satellites are typically loaded together from the same staging data.
By inserting into hubs and satellites in parallel using surrogate keys (Answer A), architects ensure consistency, atomicity, and efficient processing without re-scanning source data multiple times. This approach reduces compute usage and simplifies ETL logic, which is particularly valuable in large-scale, near-real-time ingestion pipelines.
Options involving dimensions and facts relate more closely to star schema modeling, not Data Vault.
Sequential insertion is also less efficient and not the defining advantage of conditional multi-table inserts. For SnowPro Architect candidates, this question emphasizes understanding how Snowflake SQL features support modern data modeling techniques such as Data Vault.
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NEW QUESTION # 54
Which columns can be included in an external table schema? (Select THREE).
- A. VALUE
- B. METADATA$ISUPDATE
- C. METADATA$ROW_ID
- D. METADATA$FILE_ROW_NUMBER
- E. METADATA$FILENAME
- F. METADATA$EXTERNAL_TABLE_PARTITION
Answer: A,D,E
Explanation:
External tables in Snowflake expose a combination of user-defined columns and system-generated metadata columns. The VALUE column is commonly used to store semi-structured data (such as JSON or Avro records) read directly from external storage (Answer A).
Snowflake also provides metadata columns that describe the source file. METADATA$FILENAME identifies the name of the file from which a given row was read (Answer D), and METADATA$FILE_ROW_NUMBER indicates the row number within that file (Answer E). These columns are frequently used for auditing, debugging, and data lineage tracking.
METADATA$ROW_ID and METADATA$ISUPDATE are associated with streams and change tracking, not external tables. METADATA$EXTERNAL_TABLE_PARTITION is not a valid selectable column in the external table schema definition. This question reinforces SnowPro Architect knowledge of how Snowflake represents external data and exposes file-level metadata for data lake architectures.
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NEW QUESTION # 55
Which of the below commands will use warehouse credits?
- A. SELECT COUNT(*) FROM SNOWFLAKE;
- B. SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID;
- C. SHOW TABLES LIKE 'SNOWFL%';
- D. SELECT MAX(FLAKE_ID) FROM SNOWFLAKE;
Answer: A,B,D
Explanation:
Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake. A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.
Among the commands listed in the question, the following ones will use warehouse credits:
SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.
SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows in the table3. Therefore, option C is correct.
SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.
The command that will not use warehouse credits is:
SHOW TABLES LIKE 'SNOWFL%': This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern 'SNOWFL%' in the current database and schema5. Therefore, option A is incorrect.
NEW QUESTION # 56
An Architect clones a database and all of its objects, including tasks. After the cloning, the tasks stop running.
Why is this occurring?
- A. Cloned tasks are suspended by default and must be manually resumed.
- B. The objects that the tasks reference are not fully qualified.
- C. Tasks cannot be cloned.
- D. The Architect has insufficient privileges to alter tasks on the cloned database.
Answer: A
NEW QUESTION # 57
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