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| Section | Objectives |
|---|---|
| Data Pipelines and Data Engineering | - Data orchestration
|
| Data Sharing and Collaboration | - Secure data sharing
|
| Data Architecture and Design | - Design scalable Snowflake architectures
|
| Security, Governance, and Compliance | - Access control and security model
|
| Performance and Optimization | - Query performance tuning
|
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NEW QUESTION # 122
An Architect plans to stream data using the Snowflake Connector for Kafka in Snowpipe.
What setting will optimize costs?
Answer: D
Explanation:
When using the Snowflake Connector for Kafka with Snowpipe, cost efficiency depends on batching data into appropriately sized files before ingestion. Snowflake charges Snowpipe costs based on the number of files ingested and compute used for loading. Very small files significantly increase overhead and cost.
Setting buffer.size.bytes = 10 MB allows the connector to batch records into reasonably sized files before flushing them to Snowflake (Answer C). This strikes a balance between ingestion latency and cost efficiency and aligns with Snowflake best practices for streaming ingestion. Extremely small buffer sizes or very frequent flush intervals (such as 1 record or 1 second) lead to excessive file creation and higher Snowpipe costs.
Maximizing micro-partitions is not configurable directly and is counterproductive for cost and performance.
For SnowPro Architect candidates, this question emphasizes the importance of batching and file sizing strategies when designing streaming ingestion pipelines with Kafka and Snowpipe.
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NEW QUESTION # 123
An Architect is integrating an application that needs to read and write data to Snowflake without installing any additional software on the application server.
How can this requirement be met?
Answer: A
Explanation:
The Snowflake SQL REST API is a REST API that you can use to access and update data in a Snowflake database. You can use this API to execute standard queries and most DDL and DML statements. This API can be used to develop custom applications and integrations that can read and write data to Snowflake without installing any additional software on the application server. Option A is not correct because SnowSQL is a command-line client that requires installation and configuration on the application server. Option B is not correct because the Snowpipe REST API is used to load data from cloud storage into Snowflake tables, not to read or write data to Snowflake. Option D is not correct because the Snowflake ODBC driver is a software component that enables applications to connect to Snowflake using the ODBC protocol, which also requires installation and configuration on the application server. Reference: The answer can be verified from Snowflake's official documentation on the Snowflake SQL REST API available on their website. Here are some relevant links:
Snowflake SQL REST API | Snowflake Documentation
Introduction to the SQL API | Snowflake Documentation
Submitting a Request to Execute SQL Statements | Snowflake Documentation
NEW QUESTION # 124
To convert JSON null value to SQL null value, you will use
Answer: A
NEW QUESTION # 125
Which command can be run to list all shares that have been created in your account or are available to consume by your account
Answer: C
NEW QUESTION # 126
The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:
1) Finance and Vendor Management team members who require reporting and visualization
2) Data Science team members who require access to raw data for ML model development
3) Sales team members who require engineered and protected data for data monetization What Snowflake data modeling approaches will meet these requirements? (Choose two.)
Answer: C,E
Explanation:
To accommodate the diverse needs of different teams and use cases within a company, a flexible and multi-faceted approach to data modeling is required.
Option B: By creating a raw database for landing and persisting raw data, you ensure that the Data Science team has access to unprocessed data for machine learning model development. This aligns with the best practices of having a staging area or raw data zone in a modern data architecture where raw data is ingested before being transformed or processed for different use cases.
Option C: Having profile-specific databases means creating targeted databases that are designed to meet the specific requirements of each user profile or team within the company. For the Finance and Vendor Management teams, the data can be structured and optimized for reporting and visualization. For the Sales team, the database can include engineered and protected data that is suitable for data monetization efforts. This strategy not only aligns data with usage patterns but also helps in managing data access and security policies effectively.
NEW QUESTION # 127
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