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| Section | Objectives |
|---|---|
| Security, Governance, and Compliance | - Data governance
|
| Performance and Optimization | - Query performance tuning
|
| Data Architecture and Design | - Data modeling and schema design
|
| Data Sharing and Collaboration | - Secure data sharing
|
| Data Pipelines and Data Engineering | - Data ingestion and transformation
|
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NEW QUESTION # 22
What is a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka?
Answer: C
Explanation:
The Snowflake Connector for Kafka simplifies streaming ingestion by automatically managing Snowflake objects required for ingestion. It creates and manages internal stages, file formats, and Snowpipe pipes without requiring manual configuration (Answer C). This reduces operational overhead and simplifies deployment.
The connector is cloud-agnostic and works across supported Snowflake clouds, not just AWS. It supports a defined set of formats (such as JSON and Avro), not all possible formats. While the connector enables near real-time ingestion, it still uses Snowpipe under the hood and does not guarantee lower latency than Snowpipe Streaming.
For SnowPro Architect candidates, this question emphasizes understanding how Snowflake-managed connectors abstract infrastructure details while still relying on Snowflake-native ingestion services.
NEW QUESTION # 23
Which steps are recommended best practices for prioritizing cluster keys in Snowflake? (Choose two.)
Answer: C,D
Explanation:
According to the Snowflake documentation, the best practices for choosing clustering keys are:
* Choose columns that are frequently used in join predicates. This can improve the join performance by reducing the number of micro-partitions that need to be scanned and joined.
* Choose columns that are most actively used in selective filters. This can improve the scan efficiency by skipping micro-partitions that do not match the filter predicates.
* Avoid using low cardinality columns, such as gender or country, as clustering keys. This can result in poor clustering and high maintenance costs.
* Avoid using TIMESTAMP columns with nanoseconds, as they tend to have very high cardinality and low correlation with other columns. This can also result in poor clustering and high maintenance costs.
* Avoid using columns with duplicate values or NULLs, as they can cause skew in the clustering and reduce the benefits of pruning.
* Cluster on multiple columns if the queries use multiple filters or join predicates. This can increase the chances of pruning more micro-partitions and improve the compression ratio.
* Clustering is not always useful, especially for small or medium-sized tables, or tables that are not frequently queried or updated. Clustering can incur additional costs for initially clustering the data and maintaining the clustering over time.
References:
* Clustering Keys & Clustered Tables | Snowflake Documentation
* [Considerations for Choosing Clustering for a Table | Snowflake Documentation]
NEW QUESTION # 24
Which query will identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse to improve the performance of a particular workload?
Answer: C
Explanation:
A multi-cluster warehouse is a virtual warehouse that can scale compute resources by adding or removing clusters based on the workload demand. A multi-cluster warehouse can improve the performance of a particular workload by reducing the query queue time and the data spillage to local storage. To identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse, you need to analyze the query history and look for the following indicators:
* High average queued load: This metric shows the average number of queries waiting in the queue for each warehouse cluster. A high value indicates that the warehouse is overloaded and cannot handle the concurrency demand.
* High bytes spilled to local storage: This metric shows the amount of data that was spilled from memory to local disk during query processing. A high value indicates that the warehouse size is too small and cannot fit the data in memory.
* High variation in workload: This metric shows the fluctuation in the number of queries submitted to the warehouse over time. A high variation indicates that the workload is unpredictable and dynamic, and requires a flexible scaling policy.
The query in option C is the best one to identify these indicators, as it selects the date, warehouse name, bytes spilled to local storage, and sum of average queued load from the query history table, and filters the results where bytes spilled to local storage is greater than zero. This query will show the days and warehouses that experienced data spillage and high queue time, and could benefit from a multi-cluster warehouse with auto-scale mode.
The query in option A is not correct, as it only selects the date and warehouse name, and does not include any metrics to measure the performance of the workload. The query in option B is not correct, as it selects the date, warehouse name, and average execution time, which is not a good indicator of the need for a multi-cluster warehouse. The query in option D is not correct, as it selects the date, warehouse name, and average credits used, which is not a good indicator of the need for a multi-cluster warehouse either.
References: Multi-cluster Warehouses, Query History View, Reducing Queues
NEW QUESTION # 25
What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)
Answer: C,E
Explanation:
The built-in Snowflake features that make use of the change tracking metadata for a table are the CHANGES clause and a STREAM object. The CHANGES clause enables querying the change tracking metadata for a table or view within a specified interval of time without having to create a stream with an explicit transactional offset1. A STREAM object records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change, so that actions can be taken using the changed data. This process is referred to as change data capture (CDC)2. The other options are incorrect because they do not make use of the change tracking metadata for a table. The MERGE command performs insert, update, or delete operations on a target table based on the results of a join with a source table3. The UPSERT command is not a valid Snowflake command. The CHANGE_DATA_CAPTURE command is not a valid Snowflake command. Reference: CHANGES | Snowflake Documentation, Change Tracking Using Table Streams | Snowflake Documentation, MERGE | Snowflake Documentation
NEW QUESTION # 26
An Architect is designing a pipeline to stream event data into Snowflake using the Snowflake Kafka connector. The Architect's highest priority is to configure the connector to stream data in the MOST cost-effective manner.
Which of the following is recommended for optimizing the cost associated with the Snowflake Kafka connector?
Answer: B
Explanation:
The minimum value supported for the buffer.flush.time property is 1 (in seconds). For higher average data flow rates, we suggest that you decrease the default value for improved latency. If cost is a greater concern than latency, you could increase the buffer flush time. Be careful to flush the Kafka memory buffer before it becomes full to avoid out of memory exceptions. https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-kafka
NEW QUESTION # 27
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