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質問 # 14
How do you refresh a materialized view?
- A. ALTER VIEW <MV_NAME> REFRESH
- B. Materialized views are automatically refreshed by snowflake and does not require manual intervention
- C. REFRESH MATERIALIZED VIEW <MV_NAME>
正解：B
質問 # 15
An Architect has a VPN_ACCESS_LOGS table in the SECURITY_LOGS schema containing timestamps of the connection and disconnection, username of the user, and summary statistics.
What should the Architect do to enable the Snowflake search optimization service on this table?
- A. Assume role with OWNERSHIP on VPN_ACCESS_LOGS and ADD SEARCH OPTIMIZATION in the SECURITY_LOGS schema.
- B. Assume role with ALL PRIVILEGES on VPN_ACCESS_LOGS and ADD SEARCH OPTIMIZATION in the SECURITY_LOGS schema.
- C. Assume role with OWNERSHIP on future tables and ADD SEARCH OPTIMIZATION on the SECURITY_LOGS schema.
- D. Assume role with ALL PRIVILEGES including ADD SEARCH OPTIMIZATION in the SECURITY LOGS schema.
正解：A
解説：
According to the SnowPro Advanced: Architect Exam Study Guide, to enable the search optimization service on a table, the user must have the ADD SEARCH OPTIMIZATION privilege on the table and the schema.
The privilege can be granted explicitly or inherited from a higher-level object, such as a database or a role. The OWNERSHIP privilege on a table implies the ADD SEARCH OPTIMIZATION privilege, so the user who owns the table can enable the search optimization service on it. Therefore, the correct answer is to assume a role with OWNERSHIP on VPN_ACCESS_LOGS and ADD SEARCH OPTIMIZATION in the SECURITY_LOGS schema. This will allow the user to enable the search optimization service on the VPN_ACCESS_LOGS table and any future tables created in the SECURITY_LOGS schema. The other options are incorrect because they either grant excessive privileges or do not grant the required privileges on the table or the schema. References:
* SnowPro Advanced: Architect Exam Study Guide, page 11, section 2.3.1
* Snowflake Documentation: Enabling the Search Optimization Service
質問 # 16
The Business Intelligence team reports that when some team members run queries for their dashboards in parallel with others, the query response time is getting significantly slower What can a Snowflake Architect do to identify what is occurring and troubleshoot this issue?
- A.
- B.
- C.
- D.
正解：A
質問 # 17
Which of the following are characteristics of how row access policies can be applied to external tables?
(Choose three.)
- A. External tables are supported as mapping tables in a row access policy.
- B. A row access policy can be applied to the VALUE column of an existing external table.
- C. A row access policy cannot be applied to a view created on top of an external table.
- D. While cloning a database, both the row access policy and the external table will be cloned.
- E. A row access policy cannot be directly added to a virtual column of an external table.
- F. An external table can be created with a row access policy, and the policy can be applied to the VALUE column.
正解：B、E、F
解説：
These three statements are true according to the Snowflake documentation and the web search results. A row access policy is a feature that allows filtering rows based on user-defined conditions. A row access policy can be applied to an external table, which is a table that reads data from external files in a stage. However, there are some limitations and considerations for using row access policies with external tables.
* An external table can be created with a row access policy by using the WITH ROW ACCESS POLICY clause in the CREATE EXTERNAL TABLE statement. The policy can be applied to the VALUE column, which is the column that contains the raw data from the external files in a VARIANT data type1.
* A row access policy can also be applied to the VALUE column of an existing external table by using the ALTER TABLE statement with the SET ROW ACCESS POLICY clause2.
* A row access policy cannot be directly added to a virtual column of an external table. A virtual column is a column that is derived from the VALUE column using an expression. To apply a row access policy to a virtual column, the policy must be applied to the VALUE column and the expression must be
* repeated in the policy definition3.
* External tables are not supported as mapping tables in a row access policy. A mapping table is a table that is used to determine the access rights of users or roles based on some criteria. Snowflake does not support using an external table as a mapping table because it may cause performance issues or errors4.
* While cloning a database, Snowflake clones the row access policy, but not the external table. Therefore, the policy in the cloned database refers to a table that is not present in the cloned database. To avoid this issue, the external table must be manually cloned or recreated in the cloned database4.
* A row access policy can be applied to a view created on top of an external table. The policy can be applied to the view itself or to the underlying external table. However, if the policy is applied to the view, the view must be a secure view, which is a view that hides the underlying data and the view definition from unauthorized users5.
References:
* CREATE EXTERNAL TABLE | Snowflake Documentation
* ALTER EXTERNAL TABLE | Snowflake Documentation
* Understanding Row Access Policies | Snowflake Documentation
* Snowflake Data Governance: Row Access Policy Overview
* Secure Views | Snowflake Documentation
質問 # 18
A user has the appropriate privilege to see unmasked data in a column.
If the user loads this column data into another column that does not have a masking policy, what will occur?
- A. Masked data will be loaded into the new column.
- B. Unmasked data will be loaded into the new column and no users will be able to see the unmasked data.
- C. Unmasked data will be loaded into the new column but only users with the appropriate privileges will be able to see the unmasked data.
- D. Unmasked data will be loaded in the new column.
正解：D
解説：
Explanation
According to the SnowPro Advanced: Architect documents and learning resources, column masking policies are applied at query time based on the privileges of the user who runs the query. Therefore, if a user has the privilege to see unmasked data in a column, they will see the original data when they query that column. If they load this column data into another column that does not have amasking policy, the unmasked data will be loaded in the new column, and any user who can query the new column will see the unmasked data as well.
The masking policy does not affect the underlying data in the column, only the query results.
References:
* Snowflake Documentation: Column Masking
* Snowflake Learning: Column Masking
質問 # 19
A company has an external vendor who puts data into Google Cloud Storage. The company's Snowflake account is set up in Azure.
What would be the MOST efficient way to load data from the vendor into Snowflake?
- A. Copy the data from Google Cloud Storage to Azure Blob storage using external tools and load data from Blob storage to Snowflake.
- B. Ask the vendor to create a Snowflake account, load the data into Snowflake and create a data share.
- C. Create a Snowflake Account in the Google Cloud Platform (GCP), ingest data into this account and use data replication to move the data from GCP to Azure.
- D. Create an external stage on Google Cloud Storage and use the external table to load the data into Snowflake.
正解：D
質問 # 20
A group of Data Analysts have been granted the role analyst role. They need a Snowflake database where they can create and modify tables, views, and other objects to load with their own dat a. The Analysts should not have the ability to give other Snowflake users outside of their role access to this data.
How should these requirements be met?
- A. Make every schema in the database a managed access schema, owned by SYSADMIN, and grant create privileges on each schema to the ANALYST_ROLE for each type of object that needs to be created.
- B. Grant ANALYST_R0LE OWNERSHIP on the database, but make sure that ANALYST_ROLE does not have the MANAGE GRANTS privilege on the account.
- C. Grant SYSADMIN ownership of the database, but grant the create schema privilege on the database to the ANALYST_ROLE.
- D. Grant ANALYST_ROLE ownership on the database, but grant the ownership on future [object type] s in database privilege to SYSADMIN.
正解：B
解説：
Granting ANALYST_ROLE OWNERSHIP on the database allows the analysts to create and modify tables, views, and other objects within the database. However, to prevent the analysts from giving other Snowflake users outside of their role access to this data, the ANALYST_ROLE should not have the MANAGE GRANTS privilege on the account. The MANAGE GRANTS privilege enables a role to grant or revoke privileges on any object in the account, regardless of the ownership of the object1. Therefore, by removing this privilege from the ANALYST_ROLE, the analysts can only grant or revoke privileges on the objects that they own within the database, and not on any other objects in the account2.
The other options are not correct because:
B) Granting SYSADMIN ownership of the database and granting the create schema privilege on the database to the ANALYST_ROLE would allow the analysts to create schemas within the database, but not to create or modify tables, views, or other objects within those schemas. The analysts would need to have the create [object type] privilege on each schema to create or modify objects within the schema3.
C) Making every schema in the database a managed access schema, owned by SYSADMIN, and granting create privileges on each schema to the ANALYST_ROLE for each type of object that needs to be created would allow the analysts to create and modify objects within the schemas, but not to grant or revoke privileges on those objects. A managed access schema is a schema that requires explicit grants for any access to the objects within the schema, regardless of the ownership of the objects4. Therefore, the analysts would need to have the grant privilege on each schema to grant or revoke privileges on the objects within the schema.
D) Granting ANALYST_ROLE ownership on the database and granting the ownership on future [object type] s in database privilege to SYSADMIN would allow the analysts to create and modify objects within the database, but also to grant or revoke privileges on those objects. The ownership on future [object type] s in database privilege enables a role to automatically become the owner of any new object of the specified type that is created in the database. Therefore, by granting this privilege to SYSADMIN, the analysts would not be able to prevent SYSADMIN from accessing or modifying the objects that they create within the database.
Reference:
1: MANAGE GRANTS Privilege | Snowflake Documentation
2: Access Control Privileges | Snowflake Documentation
3: CREATE SCHEMA | Snowflake Documentation
4: Managed Access | Snowflake Documentation
: GRANT | Snowflake Documentation
: Ownership on Future Objects | Snowflake Documentation
: Ownership and Revoking Privileges | Snowflake Documentation
質問 # 21
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported.
What could be causing this?
- A. There were variations in string lengths for the JSON values in the recent data imports.
- B. The recent data imports contained fewer fields than usual.
- C. The order of the keys in the JSON was changed.
- D. There were JSON nulls in the recent data imports.
正解：A、C
解説：
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:
* The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column-like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.
* There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column.
Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the
* JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.
The other options are not valid causes for poor query performance:
* There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi-structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.
* The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.
References:
* Considerations for Semi-structured Data Stored in VARIANT
* Snowflake Architect Training
* Snowflake query performance on unique element in variant column
* Snowflake variant performance
質問 # 22
VALIDATION_MODE does not support COPY statements that transform data during a load
- A. TRUE
- B. FALSE
正解：A
質問 # 23
An Architect is using SnowCD to investigate a connectivity issue.
Which system function will provide a list of endpoints that the network must be able to access to use a specific Snowflake account, leveraging private connectivity?
- A. SYSTEMSALLOWLIST ()
- B. SYSTEMSAUTHORIZE_PRIVATELINK
- C. SYSTEMSGET_PRIVATELINK
- D. SYSTEMSALLOWLIST_PRIVATELINK ()
正解：C
解説：
The SYSTEM$GET_PRIVATELINK function is used to retrieve the list of Snowflake service endpoints that need to be accessible when configuring private connectivity (such as AWS PrivateLink or Azure Private Link) for a Snowflake account. The function returns information necessary for setting up the networking infrastructure that allows secure and private access to Snowflake without using the public internet. SnowCD can then be used to verify connectivity to these endpoints.
質問 # 24
Which technique will efficiently ingest and consume semi-structured data for Snowflake data lake workloads?
- A. Schema-on-read
- B. Information schema
- C. IDEF1X
- D. Schema-on-write
正解：A
解説：
Option C is the correct answer because schema-on-read is a technique that allows Snowflake to ingest and consume semi-structured data without requiring a predefined schema. Snowflake supports various semi- structured data formats such as JSON, Avro, ORC, Parquet, and XML, and provides native data types (ARRAY, OBJECT, and VARIANT) for storing them. Snowflake also provides native support for querying semi-structured data using SQL and dot notation. Schema-on-read enables Snowflake to query semi- structured data at the same speed as performing relational queries while preserving the flexibility of schema- on-read. Snowflake's near-instant elasticity rightsizes compute resources, and consumption-based pricing ensures you only pay for what you use.
Option A is incorrect because IDEF1X is a data modeling technique that defines the structure and constraints of relational data using diagrams and notations. IDEF1X is not suitable for ingesting and consuming semi- structured data, which does not have a fixed schema or structure.
Option B is incorrect because schema-on-write is a technique that requires defining a schema before loading and processing data. Schema-on-write is not efficient for ingesting and consuming semi-structured data, which may have varying or complex structures that are difficult to fit into a predefined schema. Schema-on- write also introduces additional overhead and complexity for data transformation and validation.
Option D is incorrect because information schema is a set of metadata views that provide information about the objects and privileges in a Snowflake database. Information schema is not a technique for ingesting and consuming semi-structured data, but rather a way of accessing metadata about the data.
References:
Semi-structured Data
Snowflake for Data Lake
質問 # 25
The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:
1) Finance and Vendor Management team members who require reporting and visualization
2) Data Science team members who require access to raw data for ML model development
3) Sales team members who require engineered and protected data for data monetization What Snowflake data modeling approaches will meet these requirements? (Choose two.)
- A. Create a set of profile-specific databases that aligns data with usage patterns.
- B. Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.
- C. Create a single star schema in a single database to support all consumers' requirements.
- D. Create a raw database for landing and persisting raw data entering the data pipelines.
- E. Consolidate data in the company's data lake and use EXTERNAL TABLES.
正解：A、D
解説：
These two approaches are recommended by Snowflake for data modeling in a data lake scenario. Creating a raw database allows the data engineering team to ingest data from various sources without any transformation or cleansing, preserving the original data quality and format. This enables the data science team to access the raw data for ML model development. Creating a set of profile-specific databases allows the data engineering team to apply different transformations and optimizations for different use cases and data consumer requirements. For example, the finance and vendor management team can access a dimensional database that supports reporting and visualization, while the sales team can access a secure database that supports data monetization.
References:
* Snowflake Data Lake Architecture | Snowflake Documentation
* Snowflake Data Lake Best Practices | Snowflake Documentation
質問 # 26
Which SQL alter command will MAXIMIZE memory and compute resources for a Snowpark stored procedure when executed on the snowpark_opt_wh warehouse?
- A. Option D
- B. Option C
- C. Option A
- D. Option B
正解：C
解説：
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
質問 # 27
The following DDL command was used to create a task based on a stream:
Assuming MY_WH is set to auto_suspend - 60 and used exclusively for this task, which statement is true?
- A. The warehouse MY_WH will only be active when there are results in the stream.
- B. The warehouse MY_WH will be made active every five minutes to check the stream.
- C. The warehouse MY_WH will never suspend.
- D. The warehouse MY_WH will automatically resize to accommodate the size of the stream.
正解：A
解説：
The warehouse MY_WH will only be active when there are results in the stream. This is because the task is created based on a stream, which means that the task will only be executed when there are new data in the stream. Additionally, the warehouse is set to auto_suspend - 60, which means that the warehouse will automatically suspend after 60 seconds of inactivity. Therefore, the warehouse will only be active when there are results in the stream. References:
* [CREATE TASK | Snowflake Documentation]
* [Using Streams and Tasks | Snowflake Documentation]
* [CREATE WAREHOUSE | Snowflake Documentation]
質問 # 28
What conditions should be true for a table to consider search optimization
- A. The table size is at least 100 GB
- B. The table can be of any size
- C. The table is not clustered OR The table is frequently queried on columns other than the primary cluster key
正解：A、C
質問 # 29
A Snowflake Architect created a new data share and would like to verify that only specific records in secure views are visible within the data share by the consumers.
What is the recommended way to validate data accessibility by the consumers?
- A. Create a row access policy as shown below and assign it to the data share.
create or replace row access policy rap_acct as (acct_id varchar) returns boolean -> case when
'acctl_role' = current_role() then true else false end; - B. Set the session parameter called SIMULATED_DATA_SHARING_C0NSUMER as shown below in order to impersonate the consumer accounts.
alter session set simulated_data_sharing_consumer - 'Consumer Acctl* - C. Alter the share settings as shown below, in order to impersonate a specific consumer account.
alter share sales share set accounts = 'Consumerl' share restrictions = true - D. Create reader accounts as shown below and impersonate the consumers by logging in with their credentials.
create managed account reader_acctl admin_name = userl , adroin_password 'Sdfed43da!44T , type = reader;
正解：B
解説：
The SIMULATED_DATA_SHARING_CONSUMER session parameter allows a data provider to simulate the data access of a consumer account without creating a reader account or logging in with the consumer credentials. This parameter can be used to validate the data accessibility by the consumers in a data share, especially when using secure views or secure UDFs that filter data based on the current account or role. By setting this parameter to the name of a consumer account, the data provider can see the same data as the consumer would see when querying the shared database. This is a convenient and efficient way to test the data sharing functionality and ensure that only the intended data is visible to the consumers.
References:
* Using the SIMULATED_DATA_SHARING_CONSUMER Session Parameter
* SnowPro Advanced: Architect Exam Study Guide
質問 # 30
When a cloned table is replicated to a secondary database, the data also gets replicated in the secondary database
- A. TRUE
- B. FALSE
正解：A
質問 # 31
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. Undrop the STAGING schema and then rerun the CLONE statement.
- B. Cloning cannot be accomplished because the STAGING schema version was not active during the proposed Time Travel time period.
- C. Modify the statement: CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp =>
'2021-05-01 10:00:00'); - D. Rename the STAGING schema and perform an UNDROP to retrieve the previous STAGING schema version, then run the CLONE statement.
正解：D
質問 # 32
A group of Data Analysts have been granted the role analyst role. They need a Snowflake database where they can create and modify tables, views, and other objects to load with their own data. The Analysts should not have the ability to give other Snowflake users outside of their role access to this data.
How should these requirements be met?
- A. Make every schema in the database a managed access schema, owned by SYSADMIN, and grant create privileges on each schema to the ANALYST_ROLE for each type of object that needs to be created.
- B. Grant ANALYST_R0LE OWNERSHIP on the database, but make sure that ANALYST_ROLE does not have the MANAGE GRANTS privilege on the account.
- C. Grant SYSADMIN ownership of the database, but grant the create schema privilege on the database to the ANALYST_ROLE.
- D. Grant ANALYST_ROLE ownership on the database, but grant the ownership on future [object type] s in database privilege to SYSADMIN.
正解：B
解説：
Granting ANALYST_ROLE OWNERSHIP on the database allows the analysts to create and modify tables, views, and other objects within the database. However, to prevent the analysts from giving other Snowflake users outside of their role access to this data, the ANALYST_ROLE should not have the MANAGE GRANTS privilege on the account. The MANAGE GRANTS privilege enables a role to grant or revoke privileges on any object in the account, regardless of the ownership of the object1. Therefore, by removing this privilege from the ANALYST_ROLE, the analysts can only grant or revoke privileges on the objects that they own within the database, and not on any other objects in the account2.
The other options are not correct because:
* B. Granting SYSADMIN ownership of the database and granting the create schema privilege on the database to the ANALYST_ROLE would allow the analysts to create schemas within the database, but not to create or modify tables, views, or other objects within those schemas. The analysts would need to have the create [object type] privilege on each schema to create or modify objects within the schema3.
* C. Making every schema in the database a managed access schema, owned by SYSADMIN, and granting create privileges on each schema to the ANALYST_ROLE for each type of object that needs to be created would allow the analysts to create and modify objects within the schemas, but not to grant or revoke privileges on those objects. A managed access schema is a schema that requires explicit grants for any access to the objects within the schema, regardless of the ownership of the objects4. Therefore, the analysts would need to have the grant privilege on each schema to grant or revoke privileges on the objects within the schema.
* D. Granting ANALYST_ROLE ownership on the database and granting the ownership on future [object type] s in database privilege to SYSADMIN would allow the analysts to create and modify objects within the database, but also to grant or revoke privileges on those objects. The ownership on future
* [object type] s in database privilege enables a role to automatically become the owner of any new object of the specified type that is created in the database. Therefore, by granting this privilege to SYSADMIN, the analysts would not be able to prevent SYSADMIN from accessing or modifying the objects that they create within the database.
References:
* 1: MANAGE GRANTS Privilege | Snowflake Documentation
* 2: Access Control Privileges | Snowflake Documentation
* 3: CREATE SCHEMA | Snowflake Documentation
* 4: Managed Access | Snowflake Documentation
* : GRANT | Snowflake Documentation
* : Ownership on Future Objects | Snowflake Documentation
* : Ownership and Revoking Privileges | Snowflake Documentation
質問 # 33
A company's Architect needs to find an efficient way to get data from an external partner, who is also a Snowflake user. The current solution is based on daily JSON extracts that are placed on an FTP server and uploaded to Snowflake manually. The files are changed several times each month, and the ingestion process needs to be adapted to accommodate these changes.
What would be the MOST efficient solution?
- A. Keep the current structure but request that the partner stop changing files, instead only appending new files.
- B. Ask the partner to create a share and add the company's account.
- C. Ask the partner to set up a Snowflake reader account and use that account to get the data for ingestion.
- D. Ask the partner to use the data lake export feature and place the data into cloud storage where Snowflake can natively ingest it (schema-on-read).
正解：C
質問 # 34
What are some of the characteristics of result set caches? (Choose three.)
- A. The data stored in the result cache will contribute to storage costs.
- B. Each time persisted results for a query are used, a 24-hour retention period is reset.
- C. Time Travel queries can be executed against the result set cache.
- D. The result set cache is not shared between warehouses.
- E. The retention period can be reset for a maximum of 31 days.
- F. Snowflake persists the data results for 24 hours.
正解：B、D、F
解説：
In Snowflake, the characteristics of result set caches include persistence of data results for 24 hours (B), each use of persisted results resets the 24-hour retention period (C), and result set caches are not shared between different warehouses (F). The result set cache is specifically designed to avoid repeated execution of the same query within this timeframe, reducing computational overhead and speeding up query responses. These caches do not contribute to storage costs, and their retention period cannot be extended beyond the default duration nor up to 31 days, as might be misconstrued.References: Snowflake Documentation on Result Set Caching.
質問 # 35
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