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Snowflake ARA-C01 Exam Syllabus Topics:

SectionObjectives
Data Sharing and Collaboration- Secure data sharing
  • 1. Cross-account data sharing
    • 2. Data marketplace concepts
      Security, Governance, and Compliance- Data governance
      • 1. Data classification and masking policies
        • 2. Audit and compliance features
          - Access control and security model
          • 1. Role-based access control (RBAC)
            • 2. Network security and authentication
              Performance and Optimization- Cost optimization
              • 1. Resource monitoring and cost control
                • 2. Storage and compute efficiency
                  - Query performance tuning
                  • 1. Warehouse sizing and scaling
                    • 2. Query profiling and optimization techniques
                      Data Pipelines and Data Engineering- Data orchestration
                      • 1. Pipeline design patterns
                        • 2. Tasks and Streams usage
                          - Data ingestion and transformation
                          • 1. Snowpipe and continuous data loading
                            • 2. Batch and streaming ingestion
                              Data Architecture and Design- Design scalable Snowflake architectures
                              • 1. Multi-cluster virtual warehouse design
                                • 2. Account and environment strategy
                                  - Data modeling and schema design
                                  • 1. Semi-structured data modeling
                                    • 2. Star and snowflake schema concepts

                                      >> Snowflake ARA-C01 Exam Cram Questions <<

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                                      Snowflake SnowPro Advanced Architect Certification Sample Questions (Q61-Q66):

                                      NEW QUESTION # 61
                                      The data share exists between a data provider account and a data consumer account. Five tables from the provider account are being shared with the consumer account. The consumer role has been granted the imported privileges privilege.
                                      What will happen to the consumer account if a new table (table_6) is added to the provider schema?

                                      Answer: C

                                      Explanation:
                                      grant usage on schema EDW.ACCOUNTING to share PSHARE_EDW_4TEST ;
                                      Grant select on table EDW.ACCOUNTING.Table_6 to database PSHARE_EDW_4TEST_DB ; Explanation:
                                      When a new table (table_6) is added to a schema in the provider's account that is part of a data share, the consumer will not automatically see the new table. The consumer will only be able to access the new table once the appropriate privileges are granted by the provider. The correct process, as outlined in option D, involves using the provider's ACCOUNTADMIN role to grant USAGE privileges on the database and schema, followed by SELECT privileges on the new table, specifically to the share that includes the consumer's database. This ensures that the consumer account can access the new table under the established data sharing setup.
                                      Reference:
                                      Snowflake Documentation on Managing Access Control
                                      Snowflake Documentation on Data Sharing


                                      NEW QUESTION # 62
                                      Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported.
                                      What could be causing this?

                                      Answer: B

                                      Explanation:
                                      Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:
                                      * The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column-like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.
                                      * There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column.
                                      Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.
                                      The other options are not valid causes for poor query performance:
                                      * There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi- structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.
                                      * The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.
                                      Considerations for Semi-structured Data Stored in VARIANT
                                      Snowflake Architect Training
                                      Snowflake query performance on unique element in variant column
                                      Snowflake variant performance


                                      NEW QUESTION # 63
                                      How can the Snowpipe REST API be used to keep a log of data load history?

                                      Answer: A

                                      Explanation:
                                      The Snowpipe REST API provides two endpoints for retrieving the data load history: insertReport and loadHistoryScan. The insertReport endpoint returns the status of the files that were submitted to the insertFiles endpoint, while the loadHistoryScan endpoint returns the history of the files that were actually loaded into the table by Snowpipe. To keep a log of data load history, it is recommended to use the loadHistoryScan endpoint, which provides more accurate and complete information about the data ingestion process. The loadHistoryScan endpoint accepts a start time and an end time as parameters, and returns the files that were loaded within that time range. The maximum time range that can be specified is 15 minutes, and the maximum number of files that can be returned is 10,000. Therefore, to keep a log of data load history, the best option is to call the loadHistoryScan endpoint every 10 minutes for a 15-minute time range, and store the results in a log file or a table. This way, the log will capture all the files that were loaded by Snowpipe, and avoid any gaps or overlaps in the time range. The other options are incorrect because:
                                      * Calling insertReport every 20 minutes, fetching the last 10,000 entries, will not provide a complete log of data load history, as some files may be missed or duplicated due to the asynchronous nature of Snowpipe. Moreover, insertReport only returns the status of the files that were submitted, not the files that were loaded.
                                      * Calling loadHistoryScan every minute for the maximum time range will result in too many API calls
                                      * and unnecessary overhead, as the same files will be returned multiple times. Moreover, the maximum time range is 15 minutes, not 1 minute.
                                      * Calling insertReport every 8 minutes for a 10-minute time range will suffer from the same problems as option A, and also create gaps or overlaps in the time range.
                                      References:
                                      * Snowpipe REST API
                                      * Option 1: Loading Data Using the Snowpipe REST API
                                      * PIPE_USAGE_HISTORY


                                      NEW QUESTION # 64
                                      A company is designing high availability and disaster recovery plans and needs to maximize redundancy and minimize recovery time objectives for their critical application processes. Cost is not a concern as long as the solution is the best available. The plan so far consists of the following steps:
                                      1. Deployment of Snowflake accounts on two different cloud providers.
                                      2. Selection of cloud provider regions that are geographically far apart.
                                      3. The Snowflake deployment will replicate the databases and account data between both cloud provider accounts.
                                      4. Implementation of Snowflake client redirect.
                                      What is the MOST cost-effective way to provide the HIGHEST uptime and LEAST application disruption if there is a service event?

                                      Answer: C

                                      Explanation:
                                      To provide the highest uptime and least application disruption in case of a service event, the best option is to use the Business Critical Snowflake edition and connect the applications using the <organization_name>-
                                      <accountLocator> URL. The Business Critical Snowflake edition offers the highest level of security, performance, and availability for Snowflake accounts. It includes features such as customer-managed encryption keys, HIPAA compliance, and 4-hour RPO and RTO SLAs. It also supports account replication and failover across regions and cloud platforms, which enables business continuity and disaster recovery. By using the <organization_name>-<accountLocator> URL, the applications can leverage the Snowflake Client Redirect feature, which automatically redirects the client connections to the secondary account in case of a failover. This way, the applications can seamlessly switch to the backup account without any manual intervention or configuration changes. The other options are less cost-effective or less reliable because they either use a lower edition of Snowflake, which does not support account replication and failover, or they use the <organization_name>-<connection_name> URL, which does not support client redirect and requires manual updates to the connection string in case of a failover. References:
                                      * [Snowflake Editions] 1
                                      * [Replication and Failover/Failback] 2
                                      * [Client Redirect] 3
                                      * [Snowflake Account Identifiers] 4


                                      NEW QUESTION # 65
                                      You will be using a multi cluster warehouse. You will statically control the available resources (i.e. servers) and you have large numbers of concurrent user sessions and/or queries and the numbers do not fluctuate significantly.
                                      Which mode will you use for the warehouse?

                                      Answer: B


                                      NEW QUESTION # 66
                                      ......

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