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| Section | Weight | Objectives |
|---|---|---|
| Snowflake Architecture | 30% | - Data sharing architecture design - Data protection and recovery strategies - Development lifecycle and workload support - Object hierarchy and architectural impact - Data modeling and use cases |
| Accounts and Security | 25% | - Security, compliance and governance architecture - Security principles and implementation scenarios - Account and database strategy design |
| Data Engineering | 25% | - Data loading and unloading solutions - Data pipeline and integration design - Transformation and processing frameworks |
| Performance Optimization | 20% | - Clustering and partitioning strategies - Cost optimization and resource management - Query performance tuning - Virtual warehouse configuration and sizing |
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NEW QUESTION # 222
A team needs to recover data after pipeline failures or business rule violations.
Requirements:
* Database recoverable for 48 hours
* Analytics schema recoverable for 5 days
What is the correct approach?
Answer: C
Explanation:
Snowflake Time Travel enables point-in-time recovery without manual backups. Setting MIN_DATA_RETENTION_TIME_IN_DAYS = 2 ensures that all database objects retain at least 48 hours of recoverable history. Extending DATA_RETENTION_TIME_IN_DAYS = 5 at the schema level allows longer recovery specifically for the analytics schema (Answer C).
This approach meets both recovery requirements with minimal operational overhead and aligns with Snowflake best practices. Cloning via tasks introduces unnecessary complexity and storage management.
Custom backups are not required due to built-in Time Travel capabilities.
SnowPro Architect exams emphasize using native Snowflake features such as Time Travel for data recovery scenarios.
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NEW QUESTION # 223
A retail company has over 3000 stores all using the same Point of Sale (POS) system. The company wants to deliver near real-time sales results to category managers. The stores operate in a variety of time zones and exhibit a dynamic range of transactions each minute, with some stores having higher sales volumes than others.
Sales results are provided in a uniform fashion using data engineered fields that will be calculated in a complex data pipeline. Calculations include exceptions, aggregations, and scoring using external functions interfaced to scoring algorithms. The source data for aggregations has over 100M rows.
Every minute, the POS sends all sales transactions files to a cloud storage location with a naming convention that includes store numbers and timestamps to identify the set of transactions contained in the files. The files are typically less than 10MB in size.
How can the near real-time results be provided to the category managers? (Select TWO).
Answer: C,E
Explanation:
To provide near real-time sales results to category managers, the Architect can use the following steps:
Create an external stage that references the cloud storage location where the POS sends the sales transactions files. The external stage should use the file format and encryption settings that match the source files2 Create a Snowpipe that loads the files from the external stage into a target table in Snowflake. The Snowpipe should be configured with AUTO_INGEST = true, which means that it will automatically detect and ingest new files as they arrive in the external stage. The Snowpipe should also use a copy option to purge the files from the external stage after loading, to avoid duplicate ingestion3 Create a stream on the target table that captures the INSERTS made by the Snowpipe. The stream should include the metadata columns that provide information about the file name, path, size, and last modified time. The stream should also have a retention period that matches the real-time analytics needs4 Create a task that runs a query on the stream to process the near real-time data. The query should use the stream metadata to extract the store number and timestamps from the file name and path, and perform the calculations for exceptions, aggregations, and scoring using external functions. The query should also output the results to another table or view that can be accessed by the category managers. The task should be scheduled to run at a frequency that matches the real-time analytics needs, such as every minute or every 5 minutes.
The other options are not optimal or feasible for providing near real-time results:
All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion. This option is not recommended because it would introduce additional latency and complexity in the data pipeline. Concatenating files would require an external process or service that monitors the cloud storage location and performs the file merging operation. This would delay the ingestion of new files into Snowflake and increase the risk of data loss or corruption. Moreover, concatenating files would not avoid micro-ingestion, as Snowpipe would still ingest each concatenated file as a separate load.
An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs. This option is not necessary because Snowpipe can automatically ingest new files from the external stage without requiring an external trigger or scheduler. Using an external scheduler would add more overhead and dependency to the data pipeline, and it would not guarantee near real-time ingestion, as it would depend on the polling interval and the availability of the external scheduler.
The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement. This option is not feasible because tasks cannot be scheduled to run every second in Snowflake. The minimum interval for tasks is one minute, and even that is not guaranteed, as tasks are subject to scheduling delays and concurrency limits. Moreover, using the copy into command with a task would not leverage the benefits of Snowpipe, such as automatic file detection, load balancing, and micro-partition optimization. Reference:
1: SnowPro Advanced: Architect | Study Guide
2: Snowflake Documentation | Creating Stages
3: Snowflake Documentation | Loading Data Using Snowpipe
4: Snowflake Documentation | Using Streams and Tasks for ELT
: Snowflake Documentation | Creating Tasks
: Snowflake Documentation | Best Practices for Loading Data
: Snowflake Documentation | Using the Snowpipe REST API
: Snowflake Documentation | Scheduling Tasks
: SnowPro Advanced: Architect | Study Guide
: Creating Stages
: Loading Data Using Snowpipe
: Using Streams and Tasks for ELT
: [Creating Tasks]
: [Best Practices for Loading Data]
: [Using the Snowpipe REST API]
: [Scheduling Tasks]
NEW QUESTION # 224
Which of the following are characteristics of how row access policies can be applied to external tables?
(Choose three.)
Answer: A,C,F
Explanation:
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
NEW QUESTION # 225
Which Snowflake objects can be used in a data share? (Select TWO).
Answer: A,C
Explanation:
Data sharing is a feature that allows you to share selected objects in a database in your account with other Snowflake accounts. You can share the following Snowflake database objects: external tables, dynamic tables, secure views, secure materialized views, secure UDFs, and tables. However, not all of these objects can be used in a data share. A data share is a named object that encapsulates the information required to share a database. You can grant privileges on objects to a share either via a database role or directly to a share. The objects that can be granted privileges directly to a share are: standard views, secure views, secure UDFs, and tables.
Therefore, the correct answer is A and B. The other options are incorrect because they cannot be granted privileges directly to a share. External tables, dynamic tables, and streams can only be shared via a database role. Stored procedures cannot be shared at all. Reference:
[Introduction to Secure Data Sharing] 1
[Working with Shares] 2
[Choosing How to Share Database Objects] 3
NEW QUESTION # 226
Which of the following are characteristics of Snowflake's parameter hierarchy?
Answer: C
Explanation:
Explanation
This is the correct answer because it reflects the characteristics of Snowflake's parameter hierarchy.
Snowflake provides three types of parameters that can be set for an account: account parameters, session parameters, and object parameters. All parameters have default values, which can be set and then overridden at different levels depending on the parameter type. The following diagram illustrates the hierarchical relationship between the different parameter types and how individual parameters can be overridden at each level1:
As shown in the diagram, schema parameters are a type of object parameters that can be set for schemas.
Schema parameters can override the account parameters that are set at the account level. For example, the LOG_LEVEL parameter can be set at the account level to control the logging level for all objects in the account, but it can also be overridden at the schema level to control the logging level for specific stored procedures and UDFs in that schema2.
The other options listed are not correct because they do not reflect the characteristics of Snowflake's parameter hierarchy. Session parameters do not override virtual warehouse parameters, because virtual warehouse parameters are a type of session parameters that can be set for virtual warehouses. Virtual warehouse parameters do not override user parameters, because user parameters are a type of session parameters that can be set for users. Table parameters do not override virtual warehouse parameters, because table parameters are a type of object parameters that can be set for tables, and object parameters do not affect session parameters1.
References:
* Snowflake Documentation: Parameters
* Snowflake Documentation: Setting Log Level
NEW QUESTION # 227
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