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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Engineering | 25% | - Data loading and unloading solutions - Data pipeline and integration design - Transformation and processing frameworks |
| Topic 2: Accounts and Security | 25% | - Security principles and implementation scenarios - Account and database strategy design - Security, compliance and governance architecture |
| Topic 3: Performance Optimization | 20% | - Clustering and partitioning strategies - Query performance tuning - Virtual warehouse configuration and sizing - Cost optimization and resource management |
| Topic 4: Snowflake Architecture | 30% | - Data protection and recovery strategies - Object hierarchy and architectural impact - Data sharing architecture design - Data modeling and use cases - Development lifecycle and workload support |
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NEW QUESTION # 38
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?
Answer: D
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 a masking 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.
Reference:
Snowflake Documentation: Column Masking
Snowflake Learning: Column Masking
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?
Answer: A
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. Reference:
[Snowflake Editions] 1
[Replication and Failover/Failback] 2
[Client Redirect] 3
[Snowflake Account Identifiers] 4
NEW QUESTION # 40
How can the Snowpipe REST API be used to keep a log of data load history?
Answer: B
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.
Snowpipe REST API
Option 1: Loading Data Using the Snowpipe REST API
PIPE_USAGE_HISTORY
NEW QUESTION # 41
You are running a large join on snowflake. You ran it on a medium warehouse and it took almost an hour to run. You then tried to run the join on a large warehouse but still the performance did not improve.
What may be the most possible cause of this.
Answer: A
NEW QUESTION # 42
Database DB1 has schema S1 which has one table, T1.
DB1 --> S1 --> T1
The retention period of EG1 is set to 10 days.
The retention period of s: is set to 20 days.
The retention period of t: Is set to 30 days.
The user runs the following command:
Drop Database DB1;
What will the Time Travel retention period be for T1?
Answer: A
Explanation:
The Time Travel retention period for T1 will be 30 days, which is the retention period set at the table level. The Time Travel retention period determines how long the historical data is preserved and accessible for an object after it is modified or dropped. The Time Travel retention period can be set at the account level, the database level, the schema level, or the table level. The retention period set at the lowest level of the hierarchy takes precedence over the higher levels. Therefore, the retention period set at the table level overrides the retention periods set at the schema level, the database level, or the account level. When the user drops the database DB1, the table T1 is also dropped, but the historical data is still preserved for 30 days, which is the retention period set at the table level. The user can use the UNDROP command to restore the table T1 within the 30-day period. The other options are incorrect because:
10 days is the retention period set at the database level, which is overridden by the table level.
20 days is the retention period set at the schema level, which is also overridden by the table level.
37 days is not a valid option, as it is not the retention period set at any level.
Reference:
Understanding & Using Time Travel
AT | BEFORE
Snowflake Time Travel & Fail-safe
NEW QUESTION # 43
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