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Snowflake ARA-C01 Exam Syllabus Topics:

SectionObjectives
Topic 1: Data Sharing and Collaboration- Secure data sharing
  • 1. Data marketplace concepts
    • 2. Cross-account data sharing
      Topic 2: Security, Governance, and Compliance- Data governance
      • 1. Audit and compliance features
        • 2. Data classification and masking policies
          - Access control and security model
          • 1. Role-based access control (RBAC)
            • 2. Network security and authentication
              Topic 3: Performance and Optimization- Query performance tuning
              • 1. Query profiling and optimization techniques
                • 2. Warehouse sizing and scaling
                  - Cost optimization
                  • 1. Storage and compute efficiency
                    • 2. Resource monitoring and cost control
                      Topic 4: 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
                              Topic 5: Data Pipelines and Data Engineering- Data ingestion and transformation
                              • 1. Snowpipe and continuous data loading
                                • 2. Batch and streaming ingestion
                                  - Data orchestration
                                  • 1. Tasks and Streams usage
                                    • 2. Pipeline design patterns

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                                      Snowflake SnowPro Advanced Architect Certification Sample Questions (Q65-Q70):

                                      NEW QUESTION # 65
                                      Assuming all Snowflake accounts are using an Enterprise edition or higher, in which development and testing scenarios would be copying of data be required, and zero-copy cloning not be suitable? (Select TWO).

                                      Answer: C,E


                                      NEW QUESTION # 66
                                      Consider the following COPY command which is loading data with CSV format into a Snowflake table from an internal stage through a data transformation query.

                                      This command results in the following error:
                                      SQL compilation error: invalid parameter 'validation_mode'
                                      Assuming the syntax is correct, what is the cause of this error?

                                      Answer: C

                                      Explanation:
                                      Explanation
                                      * The VALIDATION_MODE parameter is used to specify the behavior of the COPY statement when loading data into a table. It is used to specify whether the COPY statement should return an error if any of the rows in the file are invalid or if it should continue loading the valid rows. The VALIDATION_MODE parameter is only supported for COPY statements that load data from external stages1.
                                      * The query in the question uses a data transformation query to load data from an internal stage. A data transformation query is a query that transforms the data during the load process, such as parsing JSON or XML data, applying functions, or joining with other tables2.
                                      * According to the documentation, VALIDATION_MODE does not support COPY statements that transform data during a load. If the parameter is specified, the COPY statement returns an error1.
                                      Therefore, option C is the correct answer.
                                      References: : COPY INTO <table> : Transforming Data During a Load


                                      NEW QUESTION # 67
                                      How is the change of local time due to daylight savings time handled in Snowflake tasks? (Choose two.)

                                      Answer: B,D

                                      Explanation:
                                      According to the Snowflake documentation1 and the web search results2, these two statements are true about how the change of local time due to daylight savings time is handled in Snowflake tasks. A task is a feature that allows scheduling and executing SQL statements or stored procedures in Snowflake. A task can be scheduled using a cron expression that specifies the frequency and time zone of the task execution.
                                      A task scheduled in a UTC-based schedule will have no issues with the time changes. UTC is a universal time standard that does not observe daylight savings time. Therefore, a task that uses UTC as the time zone will run at the same time throughout the year, regardless of the local time changes1.
                                      Task schedules can be designed to follow specified or local time zones to accommodate the time changes. Snowflake supports using any valid IANA time zone identifier in the cron expression for a task. This allows the task to run according to the local time of the specified time zone, which may include daylight savings time adjustments. For example, a task that uses Europe/London as the time zone will run one hour earlier or later when the local time switches between GMT and BST12.
                                      Reference:
                                      Snowflake Documentation: Scheduling Tasks
                                      Snowflake Community: Do the timezones used in scheduling tasks in Snowflake adhere to daylight savings?


                                      NEW QUESTION # 68
                                      An Architect entered the following commands in sequence:

                                      USER1 cannot find the table.
                                      Which of the following commands does the Architect need to run for USER1 to find the tables using the Principle of Least Privilege? (Choose two.)

                                      Answer: B,C

                                      Explanation:
                                      * According to the Principle of Least Privilege, the Architect should grant the minimum privileges necessary for the USER1 to find the tables in the SANDBOX database.
                                      * The USER1 needs to have USAGE privilege on the SANDBOX database and the SANDBOX.PUBLIC schema to be able to access the tables in the PUBLIC schema. Therefore, the commands B and C are the correct ones to run.
                                      * The command A is not correct because the PUBLIC role is automatically granted to every user and role in the account, and it does not have any privileges on the SANDBOX database by default.
                                      * The command D is not correct because it would transfer the ownership of the SANDBOX database from the Architect to the USER1, which is not necessary and violates the Principle of Least Privilege.
                                      * The command E is not correct because it would grant all the possible privileges on the SANDBOX database to the USER1, which is also not necessary and violates the Principle of Least Privilege.
                                      Snowflake - Principle of Least Privilege : Snowflake - Access Control Privileges : Snowflake - Public Role :
                                      Snowflake - Ownership and Grants


                                      NEW QUESTION # 69
                                      A user has the appropriate privilege to see unmasked data in a column.
                                      If the user loads this column data into another column that does not have a masking policy, what will occur?

                                      Answer: B

                                      Explanation:
                                      According to the SnowPro Advanced: Architect documents and learning resources, column masking policies are applied at query time based on the privileges of the user who runs the query. Therefore, if a user has the privilege to see unmasked data in a column, they will see the original data when they query that column. If they load this column data into another column that does not have a masking policy, the unmasked data will be loaded in the new column, and any user who can query the new column will see the unmasked data as well.
                                      The masking policy does not affect the underlying data in the column, only the query results.
                                      Snowflake Documentation: Column Masking
                                      Snowflake Learning: Column Masking


                                      NEW QUESTION # 70
                                      ......

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