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| Section | Objectives |
|---|---|
| Data Architecture and Design | - Data modeling and schema design
|
| Data Pipelines and Data Engineering | - Data ingestion and transformation
|
| Performance and Optimization | - Query performance tuning
|
| Security, Governance, and Compliance | - Data governance
|
| Data Sharing and Collaboration | - Secure data sharing
|
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NEW QUESTION # 61
A company's daily Snowflake workload consists of a huge number of concurrent queries triggered between
9pm and 11pm. At the individual level, these queries are smaller statements that get completed within a short time period.
What configuration can the company's Architect implement to enhance the performance of this workload?
(Choose two.)
Answer: B,D
Explanation:
Explanation
These two configuration options can enhance the performance of the workload that consists of a huge number of concurrent queries that are smaller and faster.
* Enabling a multi-clustered virtual warehouse in maximized mode allows the warehouse to scale out automatically by adding more clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This can improve the concurrency and throughput of the workload by minimizing or preventing queuing. The maximized mode is suitable for workloads that require high performance and low latency, and are less sensitive to credit consumption1.
* Setting the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level allows the warehouse to run more queries concurrently on each cluster. This can improve the utilization and efficiency of the warehouse resources, especially for smaller and faster queries that do not require a lot of processing power. The MAX_CONCURRENCY_LEVEL parameter can be set when creating or modifying a warehouse, and it can be changed at any time2.
References:
* Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses
* Snowflake Documentation: MAX_CONCURRENCY_LEVEL
NEW QUESTION # 62
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?
Answer: B
Explanation:
Explanation
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
NEW QUESTION # 63
A company is using Snowflake in Azure in the Netherlands. The company analyst team also has data in JSON format that is stored in an Amazon S3 bucket in the AWS Singapore region that the team wants to analyze.
The Architect has been given the following requirements:
1. Provide access to frequently changing data
2. Keep egress costs to a minimum
3. Maintain low latency
How can these requirements be met with the LEAST amount of operational overhead?
Answer: D
Explanation:
Option A is the best design to meet the requirements because it uses a materialized view on top of an external table against the S3 bucket in AWS Singapore. A materialized view is a database object that contains the results of a query and can be refreshed periodically to reflect changes in the underlying data1. An external table is a table that references data files stored in a cloud storage service, such as Amazon S32. By using a materialized view on top of an external table, the company can provide access to frequently changing data, keep egress costs to a minimum, and maintain low latency. This is because the materialized view will cache the query results in Snowflake, reducing the need to access the external data files and incur network charges.
The materialized view will also improve the query performance by avoiding scanning the external data files every time. The materialized view can be refreshed on a schedule or on demand to capture the changes in the external data files1.
Option B is not the best design because it uses an external table against the S3 bucket in AWS Singapore and copies the data into transient tables. A transient table is a tablethat is not subject to the Time Travel and Fail- safe features of Snowflake, and is automatically purged after a period of time3. By using an external table and copying the data into transient tables, the company will incur more egress costs and operational overhead than using a materialized view. This is because the external table will access the external data files every time a query is executed, and the copy operation will also transfer data from S3 to Snowflake. The transient tables will also consume more storage space in Snowflake and require manual maintenance to ensure they are up to date.
Option C is not the best design because it copies the data between providers from S3 to Azure Blob storage to collocate, then uses Snowpipe for data ingestion. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables4. By copying the data between providers, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure.
Snowpipe will also add another layer of processing and storage in Snowflake, which may not be necessary if the external data files are already in a queryable format.
Option D is not the best design because it uses AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and an Azure Netherlands Blob storage, then uses an external table against the Blob storage. AWS Transfer Family is a service that enables secure and seamless transfer of files over SFTP, FTPS, and FTP to and from Amazon S3 or Amazon EFS5. By using AWS Transfer Family, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure.
The external table will also access the external data files every time a query is executed, which may affect the query performance.
References: 1: Materialized Views 2: External Tables 3: Transient Tables 4: Snowpipe Overview 5: AWS Transfer Family
NEW QUESTION # 64
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?
Answer: A
NEW QUESTION # 65
An Architect is designing Snowflake architecture to support fast Data Analyst reporting. To optimize costs, the virtual warehouse is configured to auto-suspend after 2 minutes of idle time. Queries are run once in the morning after refresh, but later queries run slowly.
Why is this occurring?
Answer: A
Explanation:
Snowflake virtual warehouses maintain a local result and data cache only while the warehouse is running.
When a warehouse is suspended-whether manually or via auto-suspend-the local cache is cleared. As a result, subsequent queries cannot benefit from cached data and must re-scan data from remote storage, leading to slower execution (Answer D).
Snowflake does maintain a global result cache at the cloud services layer, but it is only used when the exact same query text is re-executed and the underlying data has not changed. In many analytical workloads, queries vary slightly, preventing reuse of the result cache.
Warehouse size and multi-cluster configuration impact concurrency and throughput, not cache persistence.
There is no USE_CACHE parameter in Snowflake. This question tests an architect's understanding of Snowflake caching behavior and the tradeoff between aggressive auto-suspend for cost control and cache reuse for performance.
=========
NEW QUESTION # 66
......
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