Neueste SnowPro Advanced Administrator ADA-C02 Prüfung pdf & ADA-C02 Prüfung Torrent

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Snowflake ADA-C02 Exam Syllabus Topics:

SectionObjectives
Topic 1: Performance and Monitoring- Performance tuning and optimization
  • 1. Query performance analysis
    • 2. Warehouse sizing and scaling strategies
      Topic 2: Snowflake Architecture and Virtual Warehouses- Snowflake architecture concepts
      • 1. Virtual warehouse usage and scaling
        • 2. Separation of storage and compute
          Topic 3: Data Loading and Unloading- Bulk and continuous data ingestion
          • 1. Stages and file formats
            • 2. COPY INTO operations
              Topic 4: Security and Access Control- Authentication and authorization
              • 1. Role-based access control (RBAC)
                • 2. Network policies and secure access
                  Topic 5: Data Protection and Recovery- Backup and recovery mechanisms
                  • 1. Cloning and data recovery strategies
                    • 2. Time Travel and Fail-safe concepts
                      Topic 6: Account and Resource Administration- Account configuration and management
                      • 1. Users, roles, and permissions
                        • 2. Resource monitors and governance basics

                          >> ADA-C02 Exam <<

                          ADA-C02 neuester Studienführer & ADA-C02 Training Torrent prep

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                          Snowflake SnowPro Advanced Administrator ADA-C02 ADA-C02 Prüfungsfragen mit Lösungen (Q23-Q28):

                          23. Frage
                          An Administrator has been asked to support the company's application team need to build a loyalty program for its customers. The customer table contains Personal Identifiable Information (PII), and the application team's role is DEVELOPER.
                          CREATE TABLE customer_data (
                          customer_first_name string,
                          customer_last_name string,
                          customer_address string,
                          customer_email string,
                          ... some other columns,
                          );
                          The application team would like to access the customer data, but the email field must be obfuscated.
                          How can the Administrator protect the sensitive information, while maintaining the usability of the data?

                          Antwort: D

                          Begründung:
                          The correct approach is to apply a dynamic data masking policy on the sensitive customer_email column. Using the CURRENT_ROLE context function in the masking policy allows the Administrator to obfuscate the email for the DEVELOPER role while maintaining usability of the data for other authorized roles.


                          24. Frage
                          A Snowflake account is configured with SCIM provisioning for user accounts and has bi-directional synchronization for user identities. An Administrator with access to SECURITYADMIN uses the Snowflake UI to create a user by issuing the following commands:
                          use role USERADMIN;
                          create or replace role DEVELOPER_ROLE;
                          create user PTORRES PASSWORD = 'hello world!' MUST_CHANGE_PASSWORD = FALSE
                          default_role = DEVELOPER_ROLE;
                          The new user named PTORRES successfully logs in, but sees a default role of PUBLIC in the web UI. When attempted, the following command fails:
                          use DEVELOPER_ROLE;
                          Why does this command fail?

                          Antwort: B

                          Begründung:
                          Creating a user in Snowflake does not automatically grant them roles beyond PUBLIC. To use DEVELOPER_ROLE, the USERADMIN (or another role with MANAGE GRANTS privilege) must explicitly grant DEVELOPER_ROLE to PTORRES. Without this grant, the user cannot assume the role.


                          25. Frage
                          A company has many users in the role ANALYST who routinely query Snowflake through a reporting tool. The Administrator has noticed that the ANALYST users keep two small clusters busy all of the time, and occasionally they need three or four clusters of that size.
                          Based on this scenario, how should the Administrator set up a virtual warehouse to MOST efficiently support this group of users?

                          Antwort: C

                          Begründung:
                          A multi-cluster warehouse with MIN_CLUSTERS = 2 matches typical usage, while auto-suspend and auto-resume ensure resources are only used when needed. Allowing auto-scaling handles peak periods efficiently, and giving USAGE (not MANAGE) to ANALYST users is secure and appropriate.


                          26. Frage
                          Which type of listing in the Snowflake Marketplace can be added and queried immediately?

                          Antwort: B

                          Begründung:
                          A standard listing in the Snowflake Marketplace can be immediately added to an account and queried without requiring provider approval or additional setup.


                          27. Frage
                          A retailer uses a TRANSACTIONS table (100M rows, 1.2 TB) that has been clustered by the STORE_ID column (varchar(50)). The vast majority of analyses on this table are grouped by STORE_ID to look at store performance.
                          There are 1000 stores operated by the retailer but most sales come from only 20 stores. The Administrator notes that most queries are currently experiencing poor pruning, with large amounts of bytes processed by even simple queries.
                          Why is this occurring?

                          Antwort: A

                          Begründung:
                          Clustering is effective when data distribution across the clustering key is relatively uniform. In this case, most transactions are concentrated in only 20 of the 1000 stores, leading to skewed data distribution. This imbalance causes poor pruning, since large portions of micro-partitions still need to be scanned, even for queries targeting specific stores.


                          28. Frage
                          ......

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