An Architect uses COPY INTO with the ON_ERROR=SKIP_FILE option to bulk load CSV files into a table called TABLEA, using its table stage. One file named file5.csv fails to load. The Architect fixes the file and re-loads it to the stage with the exact same file name it had previously. Which commands should the Architect use to load only file5.csv file from the stage? (Choose two.)
Correct Answer: B,C
* Option A (RETURN_FAILED_ONLY) will only load files that previously failed to load. Since file5. csv already exists in the stage with the same name, it will not be considered a new file and will not be loaded. * Option D (FORCE) will overwrite any existing data in the table. This is not desired as we only want to load the data from file5.csv. * Option E (NEW_FILES_ONLY) will only load files that have been added to the stage since the last COPY command. This will not work because file5.csv was already in the stage before it was fixed. * Option F (MERGE) is used to merge data from a stage into an existing table, creating new rows for any data not already present. This is not needed in this case as we simply want to load the data from file5. csv. Therefore, the architect can use either COPY INTO tablea FROM @%tablea or COPY INTO tablea FROM @%tablea FILES = ('file5.csv') to load only file5.csv from the stage. Both options will load the data from the specified file without overwriting any existing data or requiring additional configuration
Question 27
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.)
Correct Answer: A,B
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
Question 28
What is the recommended strategy to choose the right sized warehouse to achieve best performance based on query processing?
Correct Answer: B
Question 29
A large manufacturing company runs a dozen individual Snowflake accounts across its business divisions. The company wants to increase the level of data sharing to support supply chain optimizations and increase its purchasing leverage with multiple vendors. The company's Snowflake Architects need to design a solution that would allow the business divisions to decide what to share, while minimizing the level of effort spent on configuration and management. Most of the company divisions use Snowflake accounts in the same cloud deployments with a few exceptions for European-based divisions. According to Snowflake recommended best practice, how should these requirements be met?
Correct Answer: B
According to Snowflake recommended best practice, the requirements of the large manufacturing company should be met by deploying a Private Data Exchange in combination with data shares for the European accounts. A Private Data Exchange is a feature of the Snowflake Data Cloud platform that enables secure and governed sharing of data between organizations. It allows Snowflake customers to create their own data hub and invite other parts of their organization or external partners to access and contribute data sets. A Private Data Exchange provides centralized management, granular access control, and data usage metrics for the data shared in the exchange1. A data share is a secure and direct way of sharing data between Snowflake accounts without having to copy or move the data. A data share allows the data provider to grant privileges on selected objects in their account to one or more data consumers in other accounts2. By using a Private Data Exchange in combination with data shares, the company can achieve the following benefits: * The business divisions can decide what data to share and publish it to the Private Data Exchange, where it can be discovered and accessed by other members of the exchange. This reduces the effort and complexity of managing multiple data sharing relationships and configurations. * The company can leverage the existing Snowflake accounts in the same cloud deployments to create the Private Data Exchange and invite the members to join. This minimizes the migration and setup costs and leverages the existing Snowflake features and security. * The company can use data shares to share data with the European accounts that are in different regions or cloud platforms. This allows the company to comply with the regional and regulatory requirements for data sovereignty and privacy, while still enabling data collaboration across the organization. * The company can use the Snowflake Data Cloud platform to perform data analysis and transformation on the shared data, as well as integrate with other data sources and applications. This enables the company to optimize its supply chain and increase its purchasing leverage with multiple vendors. The other options are incorrect because they do not meet the requirements or follow the best practices. Option A is incorrect because migrating the European accounts to the global region may violate the data sovereignty and privacy regulations, and deploying a Data Exchange may not provide the level of control and management that the company needs. Option C is incorrect because deploying to the Snowflake Marketplace may expose the company's data to unwanted consumers, and using invoker_share() in secure views may not provide the desired level of security and governance. Option D is incorrect because using replication to allow European data shares in the Exchange may incur additional costs and complexity, and may not be necessary if data shares can be used instead. References: Private Data Exchange | Snowflake Documentation, Introduction to Secure Data Sharing | Snowflake Documentation
Question 30
An Architect wants to stream website logs near real time to Snowflake using the Snowflake Connector for Kafka. What characteristics should the Architect consider regarding the different ingestion methods? (Select TWO).
Correct Answer: D,E
When using the Snowflake Connector for Kafka, architects must understand the behavior differences between Snowpipe (file-based) and Snowpipe Streaming. Snowpipe Streaming is optimized for low-latency ingestion and works by continuously sending records directly into Snowflake-managed channels rather than staging files. One important characteristic is that Snowpipe Streaming automatically flushes buffered records at short, fixed intervals (approximately every second), ensuring near real-time data availability (Answer D). Another key consideration is offset handling. The Snowflake Connector for Kafka is designed to tolerate Kafka offset jumps or resets, such as those caused by topic reprocessing or consumer group changes. Snowflake can safely ingest records without corrupting state, relying on Kafka semantics and connector metadata to maintain consistency (Answer E). Snowpipe Streaming is not always the default ingestion method; configuration determines whether file-based Snowpipe or Streaming is used. Schema detection is not supported in Snowpipe Streaming. Traditional Snowpipe does not offer lower latency than Snowpipe Streaming. For the SnowPro Architect exam, understanding ingestion latency, buffering behavior, and fault tolerance is essential when designing streaming architectures. =========
