ARA-C01 Exam Dumps : SnowPro Advanced: Architect Certification Exam

In Snowflake's parameter hierarchy, virtual warehouse parameters take precedence over user parameters. This hierarchy is designed to ensure that settings at the virtual warehouse level, which typically reflect the requirements of a specific workload or set of queries, override the preferences set at the individual user level. This helps maintain consistent performance and resource utilization as specified by the administrators managing the virtual warehouses.

 According to the SnowPro Advanced: Architect documents and learning resources, a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka is that the Connector creates and manages its own stage, file format, and pipe objects. The stage is an internal stage that is used to store the data files from the Kafka topics. The file format is a JSON or Avro file format that is used to parse the data files. The pipe is a Snowpipe object that is used to load the data files into the Snowflake table. The Connector automatically creates and configures these objects based on the Kafka configuration properties, and handles the cleanup and maintenance of these objects1.

The other options are incorrect because they are not characteristics of loading data into Snowflake using the Snowflake Connector for Kafka. Option A is incorrect because the Connector works in Snowflake regions that use any cloud infrastructure, not just AWS. The Connector supports AWS, Azure, and Google Cloud platforms, and can load data across different regions and cloud platforms using data replication2. Option B is incorrect because the Connector does not work with all file formats, only JSON and Avro. The Connector expects the data in the Kafka topics to be in JSON or Avro format, and parses the data accordingly. Other file formats, such as text, ORC, Parquet, or XML, are not supported by the Connector3. Option D is incorrect because loads using the Connector do not have lower latency than Snowpipe, and do not ingest data in real time. The Connector uses Snowpipe to load data into Snowflake, and inherits the same latency and performance characteristics of Snowpipe. The Connector does not provide real-time ingestion, but near real-time ingestion, depending on the frequency and size of the data files4. References: Installing and Configuring the Kafka Connector | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation, Overview of the Kafka Connector | Snowflake Documentation, Using Snowflake Connector for Kafka With Snowpipe Streaming | Snowflake Documentation

The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for an object (database, schema, or table) in Snowflake. This parameter specifies the number of days for which historical data is preserved and can be accessed using Time Travel operations (SELECT, CREATE … CLONE, UNDROP)1.

The requirement for recovery of staging tables on a rolling 7-day basis means that the DATA_RETENTION_TIME_IN_DAYS parameter should be set to 7 at the database level. However, this parameter can be overridden at the lower levels (schema or table) if they have a different value1.

Therefore, one possible cause for certain tables to remain unrecoverable past 1 day is that the DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day. This would override the database level setting and limit the Time Travel retention period for all the tables in the schema to 1 day. To fix this, the parameter should be unset or set to 7 at the schema level1. Therefore, option B is correct.

Another possible cause for certain tables to remain unrecoverable past 1 day is that the staging tables are of the TRANSIENT type. Transient tables are tables that do not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. Transient tables are suitable for temporary or intermediate data that can be easily reproduced or replicated2. Tofix this, the tables should be created as permanent tables, which can have a Time Travel retention period of up to 90 days1. Therefore, option D is correct.

Option A is incorrect because the MANAGED ACCESS feature is not related to the data recovery requirement. MANAGED ACCESS is a feature that allows granting access privileges to objects without explicitly granting the privileges to roles. It does not affect the Time Travel retention period or the data availability3.

Option C is incorrect because there is no 1 TB limit for data recovery in Snowflake. The data storage size does not affect the Time Travel retention period or the data availability4.

Option E is incorrect because there is no ALLOW_RECOVERY privilege in Snowflake. The privilege required to perform Time Travel operations is SELECT, which allows querying historical data in tables5.

 Understanding & Using Time Travel : Transient Tables : Managed Access : Understanding Storage Cost : Table Privileges