Question # 1
| A retail company has 2000+ stores spread across the country. Store Managers report that they are having trouble running key reports related to inventory management, sales targets, payroll, and staffing during business hours. The Managers report that performance is poor and time-outs occur frequently.
Currently all reports share the same Snowflake virtual warehouse.
How should this situation be addressed? (Select TWO). | | A. Use a Business Intelligence tool for in-memory computation to improve performance.
| | B. Configure a dedicated virtual warehouse for the Store Manager team.
| | C. Configure the virtual warehouse to be multi-clustered.
| | D. Configure the virtual warehouse to size 4-XL
| | E. Advise the Store Manager team to defer report execution to off-business hours.
|
B. Configure a dedicated virtual warehouse for the Store Manager team.
C. Configure the virtual warehouse to be multi-clustered.
Explanation:
The best way to address the performance issues and time-outs faced by the Store Manager team is to configure a dedicated virtual warehouse for them and make it multi-clustered. This will allow them to run their reports independently from other workloads and scale up or down the compute resources as needed. A dedicated virtual warehouse will also enable them to apply specific security and access policies for their data. A multi-clustered virtual warehouse will provide high availability and concurrency for their queries and avoid queuing or throttling.
Using a Business Intelligence tool for in-memory computation may improve performance, but it will not solve the underlying issue of insufficient compute resources in the shared virtual warehouse. It will also introduce additional costs and complexity for the data architecture.
Configuring the virtual warehouse to size 4-XL may increase the performance, but it will also increase the cost and may not be optimal for the workload. It will also not address the concurrency and availability issues that may arise from sharing the virtual warehouse with other workloads.
Advising the Store Manager team to defer report execution to off-business hours may reduce the load on the shared virtual warehouse, but it will also reduce the timeliness and usefulness of the reports for the business. It will also not guarantee that the performance issues and time-outs will not occur at other times.
Question # 2
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) 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 | 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) 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 | 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. |
C. 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
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.
Question # 3
| How can the Snowpipe REST API be used to keep a log of data load history?
| | A. Call insertReport every 20 minutes, fetching the last 10,000 entries.
| | B. Call loadHistoryScan every minute for the maximum time range.
| | C. Call insertReport every 8 minutes for a 10-minute time range.
| | D. Call loadHistoryScan every 10 minutes for a 15-minutes range.
|
D. Call loadHistoryScan every 10 minutes for a 15-minutes range.
Explanation:
The Snowpipe REST API provides two endpoints for retrieving the data load history: insertReport and loadHistoryScan. The insertReport endpoint returns the status of the files that were submitted to the insertFiles endpoint, while the loadHistoryScan endpoint returns the history of the files that were actually loaded into the table by Snowpipe. To keep a log of data load history, it is recommended to use the loadHistoryScan endpoint, which provides more accurate and complete information about the data ingestion process.
The loadHistoryScan endpoint accepts a start time and an end time as parameters, and returns the files that were loaded within that time range. The maximum time range that can be specified is 15 minutes, and the maximum number of files that can be returned is 10,000. Therefore, to keep a log of data load history, the best option is to call the loadHistoryScan endpoint every 10 minutes for a 15-minute time range, and store the results in a log file or a table. This way, the log will capture all the files that were loaded by Snowpipe, and avoid any gaps or overlaps in the time range. The other options are incorrect because:
Calling insertReport every 20 minutes, fetching the last 10,000 entries, will not provide a complete log of data load history, as some files may be missed or duplicated due to the asynchronous nature of Snowpipe. Moreover, insertReport only returns the status of the files that were submitted, not the files that were loaded.
Calling loadHistoryScan every minute for the maximum time range will result in too many API calls and unnecessary overhead, as the same files will be returned multiple times. Moreover, the maximum time range is 15 minutes, not 1 minute.
Calling insertReport every 8 minutes for a 10-minute time range will suffer from the same problems as option A, and also create gaps or overlaps in the time range.
Question # 4
| An Architect is designing a file ingestion recovery solution. The project will use an internal named stage for file storage. Currently, in the case of an ingestion failure, the Operations team must manually download the failed file and check for errors.
Which downloading method should the Architect recommend that requires the LEAST amount of operational overhead? | | A. Use the Snowflake Connector for Python, connect to remote storage and download the file. | | B. Use the get command in SnowSQL to retrieve the file. | | C. Use the get command in Snowsight to retrieve the file. | | D. Use the Snowflake API endpoint and download the file. |
B. Use the get command in SnowSQL to retrieve the file.
Explanation:
The get command in SnowSQL is a convenient way to download files from an internal stage to a local directory. The get command can be used in interactive mode or in a script, and it supports wildcards and parallel downloads. The get command also allows specifying the overwrite option, which determines how to handle existing files with the same name.
The Snowflake Connector for Python, the Snowflake API endpoint, and the get command in Snowsight are not recommended methods for downloading files from an internal stage, because they require more operational overhead than the get command in SnowSQL. The Snowflake Connector for Python and the Snowflake API endpoint require writing and maintaining code to handle the connection, authentication, and file transfer. The get command in Snowsight requires using the web interface and manually selecting the files to download.
Question # 5
| Which statements describe characteristics of the use of materialized views in Snowflake? (Choose two.) | | A. They can include ORDER BY clauses.
| | B. They cannot include nested subqueries.
| | C. They can include context functions, such as CURRENT_TIME().
| | D. They can support MIN and MAX aggregates.
| | E. They can support inner joins, but not outer joins.
|
B. They cannot include nested subqueries.
D. They can support MIN and MAX aggregates.
Explanation:
According to the Snowflake documentation, materialized views have some limitations on the query specification that defines them. One of these limitations is that they cannot include nested subqueries, such as subqueries in the FROM clause or scalar subqueries in the SELECT list. Another limitation is that they cannot include ORDER BY clauses, context functions (such as CURRENT_TIME()), or outer joins. However, materialized views can support MIN and MAX aggregates, as well as other aggregate functions, such as SUM, COUNT, and AVG.
Question # 6
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 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; | B. 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; | C. 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* | D. 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 |
C. 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*
Explanation:
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.
Question # 7
| 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. Ask the partner to create a share and add the company's account.
| | B. 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). | | C. Keep the current structure but request that the partner stop changing files, instead only appending new files.
| | D. Ask the partner to set up a Snowflake reader account and use that account to get the data for ingestion.
|
A. Ask the partner to create a share and add the company's account.
Explanation:
The most efficient solution is to ask the partner to create a share and add the company’s account (Option A). This way, the company can access the live data from the partner without any data movement or manual intervention. Snowflake’s secure data sharing feature allows data providers to share selected objects in a database with other Snowflake accounts. The shared data is read-only and does not incur any storage or compute costs for the data consumers. The data consumers can query the shared data directly or create local copies of the shared objects in their own databases.
Option B is not efficient because it involves using the data lake export feature, which is intended for exporting data from Snowflake to an external data lake, not for importing data from another Snowflake account. The data lake export feature also requires the data provider to create an external stage on cloud storage and use the COPY INTO command to export the data into parquet files. The data consumer then needs to create an external table or a file format to load the data from the cloud storage into Snowflake. This process can be complex and costly, especially if the data changes frequently.
Option C is not efficient because it does not solve the problem of manual data ingestion and adaptation. Keeping the current structure of daily JSON extracts on an FTP server and requesting the partner to stop changing files, instead only appending new files, does not improve the efficiency or reliability of the data ingestion process. The company still needs to upload the data to Snowflake manually and deal with any schema changes or data quality issues.
Option D is not efficient because it requires the partner to set up a Snowflake reader account and use that account to get the data for ingestion. A reader account is a special type of account that can only consume data from the provider account that created it. It is intended for data consumers who are not Snowflake customers and do not have a licensing agreement with Snowflake. A reader account is not suitable for data ingestion from another Snowflake account, as it does not allow uploading, modifying, or unloading data. The company would need to use external tools or interfaces to access the data from the reader account and load it into their own account, which can be slow and expensive.
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