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NEW QUESTION # 23
In a managed access schema, what are characteristics of the roles that can manage object privileges? (Select TWO).
- A. Users with the SECURITYADMIN role or higher, can grant object privileges in a managed access schema.
- B. Users with the SYSADMIN role can grant object privileges in a managed access schema.
- C. Users who are database owners can grant object privileges in a managed access schema.
- D. Users who are schema owners can grant object privileges in a managed access schema.
- E. Users who are object owners can grant object privileges in a managed access schema.
Answer: A,D
Explanation:
In a managed access schema, the privilege management is centralized with the schema owner, who has the authority to grant object privileges within the schema. Additionally, the SECURITYADMIN role has the capability to manage object grants globally, which includes within managed access schemas. Other roles, such as SYSADMIN or database owners, do not inherently have this privilege unless explicitly granted.
References: The verified answers are based on Snowflake's official documentation, which outlines the roles and privileges associated with managed access schemas12.
NEW QUESTION # 24
Who can provide permission to EXECUTE TASK?
- A. THE TASK OWNER
- B. ACCOUNTADMIN
- C. SYSADMIN
Answer: B
NEW QUESTION # 25
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.
Answer: C
NEW QUESTION # 26
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. 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.
- B. 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.
- C. Developers create their own datasets to work against transformed versions of the live data.
- D. 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.
- E. Production and development run in different databases in the same account, and Developers need to see production-like data but with specific columns masked.
Answer: B,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 References:
* 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 # 27
A new user user_01 is created within Snowflake. The following two commands are executed:
Command 1# SHOW GRANTS TO USER user_01;
Command 2# SHOW GRANTS ON USER user_01;
What inferences can be made about these commands?
- A. Command 1 defines which role owns user_01Command 2 defines all the grants which have been given to user_01
- B. Command 1 defines all the grants which are given to user_01Command 2 defines which user owns user_01
- C. Command 1 defines which user owns user_01Command 2 defines all the grants which have been given to user_01
- D. Command 1 defines all the grants which are given to user_01Command 2 defines which role owns user_01
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To understand this, we must differentiate between:
* SHOW GRANTS TO <object>
* SHOW GRANTS ON <object>
1. SHOW GRANTS TO USER user_01;
This commandlists all the privileges granted to the useruser_01. These can include:
* Roles granted to the user
* Global privileges such as MONITOR, USAGE, etc.
#Official Documentation Extract:
"SHOW GRANTS TO USERdisplays the roles and other privileges that have been granted to the specified user." Source: Snowflake Docs - SHOW GRANTS TO
NEW QUESTION # 28
A company has a Snowflake account named ACCOUNTA in AWS us-east-1 region. The company stores its marketing data in a Snowflake database named MARKET_DB. One of the company's business partners has an account named PARTNERB in Azure East US 2 region. For marketing purposes the company has agreed to share the database MARKET_DB with the partner account.
Which of the following steps MUST be performed for the account PARTNERB to consume data from the MARKET_DB database?
- A. From account ACCOUNTA create a share of database MARKET_DB, and create a new database out of this share locally in AWS us-east-1 region. Then make this database the provider and share it with the PARTNERB account.
- B. Create a new account (called AZABC123) in Azure East US 2 region. From account ACCOUNTA create a share of database MARKET_DB, create a new database out of this share locally in AWS us-east-1 region, and replicate this new database to AZABC123 account. Then set up data sharing to the PARTNERB account.
- C. Create a share of database MARKET_DB, and create a new database out of this share locally in AWS us-east-1 region. Then replicate this database to the partner's account PARTNERB.
- D. Create a new account (called AZABC123) in Azure East US 2 region. From account ACCOUNTA replicate the database MARKET_DB to AZABC123 and from this account set up the data sharing to the PARTNERB account.
Answer: D
Explanation:
* Snowflake supports data sharing across regions and cloud platforms using account replication and share replication features. Account replication enables the replication of objects from a source account to one or more target accounts in the same organization. Share replication enables the replication of shares from a source account to one or more target accounts in the same organization1.
* To share data from the MARKET_DB database in the ACCOUNTA account in AWS us-east-1 region with the PARTNERB account in Azure East US 2 region, the following steps must be performed:
* Create a new account (called AZABC123) in Azure East US 2 region. This account will act as a bridge between the source and the target accounts. The new account must be linked to the ACCOUNTA account using an organization2.
* From the ACCOUNTA account, replicate the MARKET_DB database to the AZABC123 account using the account replication feature. This will create a secondary database in the AZABC123 account that is a replica of the primary database in the ACCOUNTA account3.
* From the AZABC123 account, set up the data sharing to the PARTNERB account using the share replication feature. This will create a share of the secondary database in the AZABC123 account and grant access to the PARTNERB account. The PARTNERB account can then create a database from the share and query the data4.
* Therefore, option C is the correct answer.
References: : Replicating Shares Across Regions and Cloud Platforms : Working with Organizations and Accounts : Replicating Databases Across Multiple Accounts : Replicating Shares Across Multiple Accounts
NEW QUESTION # 29
Which feature provides the capability to define an alternate cluster key for a table with an existing cluster key?
- A. Result cache
- B. Search optimization
- C. External table
- D. Materialized view
Answer: D
Explanation:
A materialized view is a feature that provides the capability to define an alternate cluster key for a table with an existing cluster key. A materialized view is a pre-computed result set that is stored in Snowflake and can be queried like a regular table. A materialized view can have a different cluster key than the base table, which can improve the performance and efficiency of queries on the materialized view. A materialized view can also support aggregations, joins, and filters on the base table data. A materialized view is automatically refreshed when the underlying data in the base table changes, as long as the AUTO_REFRESH parameter is set to true1.
Materialized Views | Snowflake Documentation
NEW QUESTION # 30
An Architect has chosen to separate their Snowflake Production and QA environments using two separate Snowflake accounts.
The QA account is intended to run and test changes on data and database objects before pushing those changes to the Production account. It is a requirement that all database objects and data in the QA account need to be an exact copy of the database objects, including privileges and data in the Production account on at least a nightly basis.
Which is the LEAST complex approach to use to populate the QA account with the Production account's data and database objects on a nightly basis?
- A. 1) Enable replication for each database in the Production account
2) Create replica databases in the QA account
3) Create clones of the replica databases on a nightly basis
4) Run tests directly on those cloned databases - B. 1) Create a stage in the Production account
2) Create a stage in the QA account that points to the same external object-storage location
3) Create a task that runs nightly to unload each table in the Production account into the stage
4) Use Snowpipe to populate the QA account - C. 1) Create a share in the Production account for each database
2) Share access to the QA account as a Consumer
3) The QA account creates a database directly from each share
4) Create clones of those databases on a nightly basis
5) Run tests directly on those cloned databases - D. 1) In the Production account, create an external function that connects into the QA account and returns all the data for one specific table
2) Run the external function as part of a stored procedure that loops through each table in the Production account and populates each table in the QA account
Answer: A
Explanation:
Explanation
* This approach is the least complex because it uses Snowflake's built-in replication feature to copy the data and database objects from the Production account to the QA account. Replication is a fast and efficient way to synchronize data across accounts, regions, and cloud platforms. It also preserves the privileges and metadata of the replicated objects. By creating clones of the replica databases, the QA account can run tests on the cloned data without affecting the original data. Clones are also zero-copy, meaning they do not consume any additional storage space unless the data is modified. This approach does not require any external stages, tasks, Snowpipe, or external functions, which can add complexity and overhead to the data transfer process.
References:
* Introduction to Replication and Failover
* Replicating Databases Across Multiple Accounts
* Cloning Considerations
NEW QUESTION # 31
The IT Security team has identified that there is an ongoing credential stuffing attack on many of their organization's system.
What is the BEST way to find recent and ongoing login attempts to Snowflake?
- A. View the Users section in the Account tab in the Snowflake UI and review the last login column.
- B. Call the LOGIN_HISTORY Information Schema table function.
- C. View the History tab in the Snowflake UI and set up a filter for SQL text that contains the text
"LOGIN". - D. Query the LOGIN_HISTORY view in the ACCOUNT_USAGE schema in the SNOWFLAKE database.
Answer: D
Explanation:
This view can be used to query login attempts by Snowflake users within the last 365 days (1 year). It provides information such as the event timestamp, the user name, the client IP, the authentication method, the success or failure status, and the error code or message if the login attempt was unsuccessful. By querying this view, the IT Security team can identify any suspicious or malicious login attempts to Snowflake and take appropriate actions to prevent credential stuffing attacks1. The other options are not the best ways to find recent and ongoing login attempts to Snowflake. Option A is incorrect because the LOGIN_HISTORY Information Schema table function only returns login events within the last 7 days, which may not be sufficient to detect credential stuffing attacks that span a longer period of time2. Option C is incorrect because the History tab in the Snowflake UI only shows the queries executed by the current user or role, not the login events of other users or roles3. Option D is incorrect because the Users section in the Account tab in the Snowflake UI only shows the last login time for each user, not the details of the login attempts or the failures.
NEW QUESTION # 32
Role A has the following permissions:
. USAGE on db1
. USAGE and CREATE VIEW on schemal in db1
. SELECT on tablel in schemal
Role B has the following permissions:
. USAGE on db2
. USAGE and CREATE VIEW on schema2 in db2
. SELECT on table2 in schema2
A user has Role A set as the primary role and Role B as a secondary role.
What command will fail for this user?
- A. use database db2;
use schema schema2;
select * from db1.schemal.tablel union select * from table2; - B. use database db2;
use schema schema2;
create view v2 as select * from dbl.schemal. tablel; - C. use database db1;
use schema schemal;
select * from db2.schema2.table2; - D. use database db1;
use schema schemal;
create view v1 as select * from db2.schema2.table2;
Answer: B
Explanation:
This command will fail because while the user has USAGE permission on db2 and schema2 through Role B, and can create a view in schema2, they do not have SELECT permission on db1.schemal.table1 with Role B.
Since Role A, which has SELECT permission on db1.schemal.table1, is not the currently active role when the view v2 is being created in db2.schema2, the user does not have the necessary permissions to read from db1.schemal.table1 to create the view. Snowflake's security model requires that the active role have all necessary permissions to execute the command.
NEW QUESTION # 33
Which organization-related tasks can be performed by the ORGADMIN role? (Choose three.)
- A. Changing the name of an account
- B. Deleting an account
- C. Enabling the replication of a database
- D. Creating an account
- E. Viewing a list of organization accounts
- F. Changing the name of the organization
Answer: C,D,E
Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, the organization-related tasks that can be performed by the ORGADMIN role are:
* Creating an account in the organization. A user with the ORGADMIN role can use the CREATE ACCOUNT command to create a new account that belongs to the same organization as the current account1.
* Viewing a list of organization accounts. A user with the ORGADMIN role can use the SHOW ORGANIZATION ACCOUNTS command to view the names and properties of all accounts in the organization2. Alternatively, the user can use the Admin Accounts page in the web interface to view the organization name and account names3.
* Enabling the replication of a database. A user with the ORGADMIN role can use the SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER function to enable database replication for an
* account in the organization. This allows the user to replicate databases across accounts in different regions and cloud platforms for data availability and durability4.
The other options are incorrect because they are not organization-related tasks that can be performed by the ORGADMIN role. Option A is incorrect because changing the name of the organization is not a task that can be performed by the ORGADMIN role. To change the name of an organization, the user must contact Snowflake Support3. Option D is incorrect because changing the name of an account is not a task that can be performed by the ORGADMIN role. To change the name of an account, the user must contact Snowflake Support5. Option E is incorrect because deleting an account is not a task that can be performed by the ORGADMIN role. To delete an account, the user must contact Snowflake Support. References: CREATE ACCOUNT | Snowflake Documentation, SHOW ORGANIZATION ACCOUNTS | Snowflake Documentation, Getting Started with Organizations | Snowflake Documentation, SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER | Snowflake Documentation, ALTER ACCOUNT | Snowflake Documentation, [DROP ACCOUNT | Snowflake Documentation]
NEW QUESTION # 34
A data platform team creates two multi-cluster virtual warehouses with the AUTO_SUSPEND value set to NULL on one. and '0' on the other. What would be the execution behavior of these virtual warehouses?
- A. Setting a '0' or NULL value means the warehouses will suspend after the default of 600 seconds.
- B. Setting a '0' or NULL value means the warehouses will never suspend.
- C. Setting a '0' or NULL value means the warehouses will suspend immediately.
- D. Setting a '0' value means the warehouses will suspend immediately, and NULL means the warehouses will never suspend.
Answer: D
Explanation:
The AUTO_SUSPEND parameter controls the amount of time, in seconds, of inactivity after which a warehouse is automatically suspended. If the parameter is set to NULL, the warehouse never suspends. If the parameter is set to '0', the warehouse suspends immediately after executing a query. Therefore, the execution behavior of the two virtual warehouses will be different depending on the AUTO_SUSPEND value. The warehouse with NULL value will keep running until it is manually suspended or the resource monitor limits are reached. The warehouse with '0' value will suspend as soon as it finishes a query and release the compute resources. Reference:
ALTER WAREHOUSE
Parameters
NEW QUESTION # 35
Which data models can be used when modeling tables in a Snowflake environment? (Select THREE).
- A. Data vault
- B. lnmon/3NF
- C. Dimensional/Kimball
- D. Data lake
- E. Bayesian hierarchical model
- F. Graph model
Answer: A,B,C
Explanation:
Snowflake is a cloud data platform that supports various data models for modeling tables in a Snowflake environment. The data models can be classified into two categories: dimensional and normalized. Dimensional data models are designed to optimize query performance and ease of use for business intelligence and analytics. Normalized data models are designed to reduce data redundancy and ensure data integrity for transactional and operational systems. The following are some of the data models that can be used in Snowflake:
Dimensional/Kimball: This is a popular dimensional data model that uses a star or snowflake schema to organize data into fact and dimension tables. Fact tables store quantitative measures and foreign keys to dimension tables. Dimension tables store descriptive attributes and hierarchies. A star schema has a single denormalized dimension table for each dimension, while a snowflake schema has multiple normalized dimension tables for each dimension. Snowflake supports both star and snowflake schemas, and allows users to create views and joins to simplify queries.
Inmon/3NF: This is a common normalized data model that uses a third normal form (3NF) schema to organize data into entities and relationships. 3NF schema eliminates data duplication and ensures data consistency by applying three rules: 1) every column in a table must depend on the primary key, 2) every column in a table must depend on the whole primary key, not a part of it, and 3) every column in a table must depend only on the primary key, not on other columns. Snowflake supports 3NF schema and allows users to create referential integrity constraints and foreign key relationships to enforce data quality.
Data vault: This is a hybrid data model that combines the best practices of dimensional and normalized data models to create a scalable, flexible, and resilient data warehouse. Data vault schema consists of three types of tables: hubs, links, and satellites. Hubs store business keys and metadata for each entity. Links store associations and relationships between entities. Satellites store descriptive attributes and historical changes for each entity or relationship. Snowflake supports data vault schema and allows users to leverage its features such as time travel, zero-copy cloning, and secure data sharing to implement data vault methodology.
NEW QUESTION # 36
A table contains five columns and it has millions of records. The cardinality distribution of the columns is shown below:
Column C4 and C5 are mostly used by SELECT queries in the GROUP BY and ORDER BY clauses.
Whereas columns C1, C2 and C3 are heavily used in filter and join conditions of SELECT queries.
The Architect must design a clustering key for this table to improve the query performance.
Based on Snowflake recommendations, how should the clustering key columns be ordered while defining the multi-column clustering key?
- A. C5, C4, C2
- B. C1, C3, C2
- C. C2, C1, C3
- D. C3, C4, C5
Answer: C
Explanation:
According to the Snowflake documentation, the following are some considerations for choosing clustering for a table1:
* Clustering is optimal when either:
* You require the fastest possible response times, regardless of cost.
* Your improved query performance offsets the credits required to cluster and maintain the table.
* Clustering is most effective when the clustering key is used in the following types of query predicates:
* Filter predicates (e.g. WHERE clauses)
* Join predicates (e.g. ON clauses)
* Grouping predicates (e.g. GROUP BY clauses)
* Sorting predicates (e.g. ORDER BY clauses)
* Clustering is less effective when the clustering key is not used in any of the above query predicates, or when the clustering key is used in a predicate that requires a function or expression to be applied to the key (e.g. DATE_TRUNC, TO_CHAR, etc.).
* For most tables, Snowflake recommends a maximum of 3 or 4 columns (or expressions) per key.
Adding more than 3-4 columns tends to increase costs more than benefits.
Based on these considerations, the best option for the clustering key columns is C. C1, C3, C2, because:
* These columns are heavily used in filter and join conditions of SELECT queries, which are the most effective types of predicates for clustering.
* These columns have high cardinality, which means they have many distinct values and can help reduce the clustering skew and improve the compression ratio.
* These columns are likely to be correlated with each other, which means they can help co-locate similar rows in the same micro-partitions and improve the scan efficiency.
* These columns do not require any functions or expressions to be applied to them, which means they can be directly used in the predicates without affecting the clustering.
1: Considerations for Choosing Clustering for a Table | Snowflake Documentation
NEW QUESTION # 37
An Architect needs to meet a company requirement to ingest files from the company's AWS storage accounts into the company's Snowflake Google Cloud Platform (GCP) account. How can the ingestion of these files into the company's Snowflake account be initiated? (Select TWO).
- A. Configure AWS Simple Notification Service (SNS) to notify Snowpipe when new files have arrived in Amazon S3 storage.
- B. Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage.
- C. Configure the client application to issue a COPY INTO <TABLE> command to Snowflake when new files have arrived in Amazon S3 Glacier storage.
- D. Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 Glacier storage.
- E. Create an AWS Lambda function to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage.
Answer: B,E
Explanation:
Snowpipe is a feature that enables continuous, near-real-time data ingestion from external sources into Snowflake tables. Snowpipe can ingest files from Amazon S3, Google Cloud Storage, or Azure Blob Storage into Snowflake tables on any cloud platform. Snowpipe can be triggered in two ways: by using the Snowpipe REST API or by using cloud notifications2 To ingest files from the company's AWS storage accounts into the company's Snowflake GCP account, the Architect can use either of these methods:
Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage. This method requires the client application to monitor the S3 buckets for new files and send a request to the Snowpipe REST API with the list of files to ingest. The client application must also handle authentication, error handling, and retry logic3 Create an AWS Lambda function to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage. This method leverages the AWS Lambda service to execute a function that calls the Snowpipe REST API whenever an S3 event notification is received. The AWS Lambda function must be configured with the appropriate permissions, triggers, and code to invoke the Snowpipe REST API4 The other options are not valid methods for triggering Snowpipe:
Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 Glacier storage. This option is not feasible because Snowpipe does not support ingesting files from Amazon S3 Glacier storage, which is a long-term archival storage service. Snowpipe only supports ingesting files from Amazon S3 standard storage classes5 Configure AWS Simple Notification Service (SNS) to notify Snowpipe when new files have arrived in Amazon S3 storage. This option is not applicable because Snowpipe does not support cloud notifications from AWS SNS. Snowpipe only supports cloud notifications from AWS SQS, Google Cloud Pub/Sub, or Azure Event Grid6 Configure the client application to issue a COPY INTO <TABLE> command to Snowflake when new files have arrived in Amazon S3 Glacier storage. This option is not relevant because it does not use Snowpipe, but rather the standard COPY command, which is a batch loading method. Moreover, the COPY command also does not support ingesting files from Amazon S3 Glacier storage7 Reference:
1: SnowPro Advanced: Architect | Study Guide 8
2: Snowflake Documentation | Snowpipe Overview 9
3: Snowflake Documentation | Using the Snowpipe REST API 10
4: Snowflake Documentation | Loading Data Using Snowpipe and AWS Lambda 11
5: Snowflake Documentation | Supported File Formats and Compression for Staged Data Files 12
6: Snowflake Documentation | Using Cloud Notifications to Trigger Snowpipe 13
7: Snowflake Documentation | Loading Data Using COPY into a Table
8: SnowPro Advanced: Architect | Study Guide
9: Snowpipe Overview
10: Using the Snowpipe REST API
11: Loading Data Using Snowpipe and AWS Lambda
12: Supported File Formats and Compression for Staged Data Files
13: Using Cloud Notifications to Trigger Snowpipe
14: Loading Data Using COPY into a Table
NEW QUESTION # 38
A healthcare company is deploying a Snowflake account that may include Personal Health Information (PHI). The company must ensure compliance with all relevant privacy standards.
Which best practice recommendations will meet data protection and compliance requirements? (Choose three.)
- A. Use the External Tokenization feature to obfuscate sensitive data.
- B. Use the Internal Tokenization feature to obfuscate sensitive data.
- C. Avoid sharing data with partner organizations.
- D. Rewrite SQL queries to eliminate projections of PHI data based on current_role().
- E. Use, at minimum, the Business Critical edition of Snowflake.
- F. Create Dynamic Data Masking policies and apply them to columns that contain PHI.
Answer: A,E,F
Explanation:
A healthcare company that handles PHI data must ensure compliance with relevant privacy standards, such as HIPAA, HITRUST, and GDPR. Snowflake provides several features and best practices to help customers meet their data protection and compliance requirements1.
One best practice recommendation is to use, at minimum, the Business Critical edition of Snowflake. This edition provides the highest level of data protection and security, including end-to-end encryption with customer-managed keys, enhanced object-level security, and HIPAA and HITRUST compliance2. Therefore, option A is correct.
Another best practice recommendation is to create Dynamic Data Masking policies and apply them to columns that contain PHI. Dynamic Data Masking is a feature that allows masking or redacting sensitive data based on the current user's role. This way, only authorized users can view the unmasked data, while others will see masked values, such as NULL, asterisks, or random characters3. Therefore, option B is correct.
A third best practice recommendation is to use the External Tokenization feature to obfuscate sensitive data. External Tokenization is a feature that allows replacing sensitive data with tokens that are generated and stored by an external service, such as Protegrity. This way, the original data is never stored or processed by Snowflake, and only authorized users can access the tokenized data through the external service4. Therefore, option D is correct.
Option C is incorrect, because the Internal Tokenization feature is not available in Snowflake. Snowflake does not provide any native tokenization functionality, but only supports integration with external tokenization services4.
Option E is incorrect, because rewriting SQL queries to eliminate projections of PHI data based on current_role() is not a best practice. This approach is error-prone, inefficient, and hard to maintain. A better alternative is to use Dynamic Data Masking policies, which can automatically mask data based on the user's role without modifying the queries3.
Option F is incorrect, because avoiding sharing data with partner organizations is not a best practice. Snowflake enables secure and governed data sharing with internal and external consumers, such as business units, customers, or partners. Data sharing does not involve copying or moving data, but only granting access privileges to the shared objects. Data sharing can also leverage Dynamic Data Masking and External Tokenization features to protect sensitive data5.
NEW QUESTION # 39
A company is designing high availability and disaster recovery plans and needs to maximize redundancy and minimize recovery time objectives for their critical application processes. Cost is not a concern as long as the solution is the best available. The plan so far consists of the following steps:
1. Deployment of Snowflake accounts on two different cloud providers.
2. Selection of cloud provider regions that are geographically far apart.
3. The Snowflake deployment will replicate the databases and account data between both cloud provider accounts.
4. Implementation of Snowflake client redirect.
What is the MOST cost-effective way to provide the HIGHEST uptime and LEAST application disruption if there is a service event?
- A. Connect the applications using the <organization_name>-<connection_name> URL. Use the Business Critical Snowflake edition.
- B. Connect the applications using the <organization_name>-<accountLocator> URL. Use the Business Critical Snowflake edition.
- C. Connect the applications using the <organization_name>-<connection_name> URL. Use the Virtual Private Snowflake (VPS) edition.
- D. Connect the applications using the <organization_name>-<accountLocator> URL. Use the Enterprise Snowflake edition.
Answer: B
Explanation:
To provide the highest uptime and least application disruption in case of a service event, the best option is to use the Business Critical Snowflake edition and connect the applications using the
<organization_name>-<accountLocator> URL. The Business Critical Snowflake edition offers the highest level of security, performance, and availability for Snowflake accounts. It includes features such as customer-managed encryption keys, HIPAA compliance, and 4-hour RPO and RTO SLAs. It also supports account replication and failover across regions and cloud platforms, which enables business continuity and disaster recovery. By using the <organization_name>-<accountLocator> URL, the applications can leverage the Snowflake Client Redirect feature, which automatically redirects the client connections to the secondary account in case of a failover. This way, the applications can seamlessly switch to the backup account without any manual intervention or configuration changes. The other options are less cost-effective or less reliable because they either use a lower edition of Snowflake, which does not support account replication and failover, or they use the <organization_name>-<connection_name> URL, which does not support client redirect and requires manual updates to the connection string in case of a failover. References:
* [Snowflake Editions] 1
* [Replication and Failover/Failback] 2
* [Client Redirect] 3
* [Snowflake Account Identifiers] 4
NEW QUESTION # 40
Data sharing is supported only between provider and consumer accounts in same region
- A. FALSE
- B. TRUE
Answer: A
NEW QUESTION # 41
What is a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka?
- A. The Connector creates and manages its own stage, file format, and pipe objects.
- B. The Connector works with all file formats, including text, JSON, Avro, Ore, Parquet, and XML.
- C. Loads using the Connector will have lower latency than Snowpipe and will ingest data in real time.
- D. The Connector only works in Snowflake regions that use AWS infrastructure.
Answer: A
Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka is that the Connector creates and manages its own stage, file format, and pipe objects. The stage is an internal stage that is used to store the data files from the Kafka topics. The file format is a JSON or Avro file format that is used to parse the data files. The pipe is a Snowpipe object that is used to load the data files into the Snowflake table. The Connector automatically creates and configures these objects based on the Kafka configuration properties, and handles the cleanup and maintenance of these objects1.
The other options are incorrect because they are not characteristics of loading data into Snowflake using the Snowflake Connector for Kafka. Option A is incorrect because the Connector works in Snowflake regions that use any cloud infrastructure, not just AWS. The Connector supports AWS, Azure, and Google Cloud platforms, and can load data across different regions and cloud platforms using data replication2. Option B is incorrect because the Connector does not work with all file formats, only JSON and Avro. The Connector expects the data in the Kafka topics to be in JSON or Avro format, and parses the data accordingly. Other file formats, such as text, ORC, Parquet, or XML, are not supported by the Connector3. Option D is incorrect because loads using the Connector do not have lower latency than Snowpipe, and do not ingest data in real time. The Connector uses Snowpipe to load data into Snowflake, and inherits the same latency and performance characteristics of Snowpipe. The Connector does not provide real-time ingestion, but near real-time ingestion, depending on the frequency and size of the data files4. Reference: Installing and Configuring the Kafka Connector | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation, Overview of the Kafka Connector | Snowflake Documentation, Using Snowflake Connector for Kafka With Snowpipe Streaming | Snowflake Documentation
NEW QUESTION # 42
Consider the following scenario where a masking policy is applied on the CREDICARDND column of the CREDITCARDINFO table. The masking policy definition Is as follows:
Sample data for the CREDITCARDINFO table is as follows:
NAME EXPIRYDATE CREDITCARDNO
JOHN DOE 2022-07-23 4321 5678 9012 1234
if the Snowflake system rotes have not been granted any additional roles, what will be the result?
- A. Anyone with the Pl_ANALYTICS role will see the last 4 characters of the CREDICARDND column data in dear text.
- B. The owner of the table will see the CREDICARDND column data in clear text.
- C. The sysadmin can see the CREDICARDND column data in clear text.
- D. Anyone with the Pl_ANALYTICS role will see the CREDICARDND column as*** 'MASKED* **'.
Answer: D
Explanation:
* The masking policy defined in the image indicates that if a user has the PI_ANALYTICS role, they will be able to see the last 4 characters of the CREDITCARDNO column data in clear text. Otherwise, they will see 'MASKED'. Since Snowflake system roles have not been granted any additional roles, they won't have the PI_ANALYTICS role and therefore cannot view the last 4 characters of credit card numbers.
* To apply a masking policy on a column in Snowflake, you need to use the ALTER TABLE ... ALTER COLUMN command or the ALTER VIEW command and specify the policy name. For example, to apply the creditcardno_mask policy on the CREDITCARDNO column of the CREDITCARDINFO table, you can use the following command:
ALTER TABLE CREDITCARDINFO ALTER COLUMN CREDITCARDNO SET MASKING POLICY creditcardno_mask;
* For more information on how to create and use masking policies in Snowflake, you can refer to the following resources:
CREATE MASKING POLICY: This document explains the syntax and usage of the CREATE MASKING POLICY command, which allows you to create a new masking policy or replace an existing one.
Using Dynamic Data Masking: This guide provides instructions on how to configure and use dynamic data masking in Snowflake, which is a feature that allows you to mask sensitive data based on the execution context of the user.
ALTER MASKING POLICY: This document explains the syntax and usage of the ALTER MASKING POLICY command, which allows you to modify the properties of an existing masking policy.
References: 1: https://docs.snowflake.com/en/sql-reference/sql/create-masking-policy 2: https://docs.
snowflake.com/en/user-guide/security-column-ddm-use 3: https://docs.snowflake.com/en/sql-reference/sql
/alter-masking-policy
NEW QUESTION # 43
While using joins, non-equality join predicates might result in significantly slower processing speeds and should be avoided if possible.
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 44
There are two databases in an account, named fin_db and hr_db which contain payroll and employee data, respectively. Accountants and Analysts in the company require different permissions on the objects in these databases to perform their jobs. Accountants need read-write access to fin_db but only require read-only access to hr_db because the database is maintained by human resources personnel.
An Architect needs to create a read-only role for certain employees working in the human resources department.
Which permission sets must be granted to this role?
- A. MODIFY on database hr_db, USAGE on all schemas in database hr_db, USAGE on all tables in database hr_db
- B. USAGE on database hr_db, USAGE on all schemas in database hr_db, SELECT on all tables in database hr_db
- C. USAGE on database hr_db, USAGE on all schemas in database hr_db, REFERENCES on all tables in database hr_db
- D. USAGE on database hr_db, SELECT on all schemas in database hr_db, SELECT on all tables in database hr_db
Answer: B
Explanation:
To create a read-only role for certain employees working in the human resources department, the role needs to have the following permissions on the hr_db database:
USAGE on the database: This allows the role to access the database and see its schemas and objects.
USAGE on all schemas in the database: This allows the role to access the schemas and see their objects.
SELECT on all tables in the database: This allows the role to query the data in the tables.
Option A is the correct answer because it grants the minimum permissions required for a read-only role on the hr_db database.
Option B is incorrect because SELECT on schemas is not a valid permission. Schemas only support USAGE and CREATE permissions.
Option C is incorrect because MODIFY on the database is not a valid permission. Databases only support USAGE, CREATE, MONITOR, and OWNERSHIP permissions. Moreover, USAGE on tables is not sufficient for querying the data. Tables support SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, and OWNERSHIP permissions.
Option D is incorrect because REFERENCES on tables is not relevant for querying the data. REFERENCES permission allows the role to create foreign key constraints on the tables.
Reference:
: https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#database-privileges
: https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#schema-privileges
: https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#table-privileges
NEW QUESTION # 45
A large manufacturing company runs a dozen individual Snowflake accounts across its business divisions. The company wants to increase the level of data sharing to support supply chain optimizations and increase its purchasing leverage with multiple vendors.
The company's Snowflake Architects need to design a solution that would allow the business divisions to decide what to share, while minimizing the level of effort spent on configuration and management. Most of the company divisions use Snowflake accounts in the same cloud deployments with a few exceptions for European-based divisions.
According to Snowflake recommended best practice, how should these requirements be met?
- A. Deploy a Private Data Exchange and use replication to allow European data shares in the Exchange.
- B. Migrate the European accounts in the global region and manage shares in a connected graph architecture. Deploy a Data Exchange.
- C. Deploy a Private Data Exchange in combination with data shares for the European accounts.
- D. Deploy to the Snowflake Marketplace making sure that invoker_share() is used in all secure views.
Answer: C
Explanation:
According to Snowflake recommended best practice, the requirements of the large manufacturing company should be met by deploying a Private Data Exchange in combination with data shares for the European accounts. A Private Data Exchange is a feature of the Snowflake Data Cloud platform that enables secure and governed sharing of data between organizations. It allows Snowflake customers to create their own data hub and invite other parts of their organization or external partners to access and contribute data sets. A Private Data Exchange provides centralized management, granular access control, and data usage metrics for the data shared in the exchange1. A data share is a secure and direct way of sharing data between Snowflake accounts without having to copy or move the data. A data share allows the data provider to grant privileges on selected objects in their account to one or more data consumers in other accounts2. By using a Private Data Exchange in combination with data shares, the company can achieve the following benefits:
The business divisions can decide what data to share and publish it to the Private Data Exchange, where it can be discovered and accessed by other members of the exchange. This reduces the effort and complexity of managing multiple data sharing relationships and configurations.
The company can leverage the existing Snowflake accounts in the same cloud deployments to create the Private Data Exchange and invite the members to join. This minimizes the migration and setup costs and leverages the existing Snowflake features and security.
The company can use data shares to share data with the European accounts that are in different regions or cloud platforms. This allows the company to comply with the regional and regulatory requirements for data sovereignty and privacy, while still enabling data collaboration across the organization.
The company can use the Snowflake Data Cloud platform to perform data analysis and transformation on the shared data, as well as integrate with other data sources and applications. This enables the company to optimize its supply chain and increase its purchasing leverage with multiple vendors.
The other options are incorrect because they do not meet the requirements or follow the best practices. Option A is incorrect because migrating the European accounts to the global region may violate the data sovereignty and privacy regulations, and deploying a Data Exchange may not provide the level of control and management that the company needs. Option C is incorrect because deploying to the Snowflake Marketplace may expose the company's data to unwanted consumers, and using invoker_share() in secure views may not provide the desired level of security and governance. Option D is incorrect because using replication to allow European data shares in the Exchange may incur additional costs and complexity, and may not be necessary if data shares can be used instead. Reference: Private Data Exchange | Snowflake Documentation, Introduction to Secure Data Sharing | Snowflake Documentation
NEW QUESTION # 46
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