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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Databases, Tables & Views | 18% | - Column types and sequences - Stored procedures and UDFs - Table types (permanent, transient, temporary) - Clustering and micro-partitions - Data manipulation (DML operations) - Views (standard, materialized, secure) |
| Topic 2: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake editions and features - Snowflake architecture layers (storage, compute, services) - Virtual warehouse concepts and credit usage |
| Topic 3: Semi-Structured Data | 10% | - Parsing and flattening semi-structured data - Array and object operations - VARIANT data type |
| Topic 4: Data Loading & Unloading | 15% | - File formats (JSON, CSV, Parquet, AVRO) - Data ingestion best practices - Continuous loading with Snowpipe - Bulk loading with COPY command - Data unloading and staging |
| Topic 5: Account Objects | 8% | - Organizations and accounts structure - Resource monitors - Warehouses, databases, and schemas hierarchy |
| Topic 6: Time Travel & Cloning | 10% | - Fail-safe concepts - Time travel concepts and retention - UNDROP and historical data access - Zero-copy cloning |
| Topic 7: Virtual Warehouses | 15% | - Warehouse sizing and scaling - Multi-cluster warehouses - Warehouse monitoring and troubleshooting - Cache concepts (warehouse, result, metadata) - Warehouse creation and configuration |
| Topic 8: Performance Optimization & Monitoring | 4% | - Resource optimization techniques - Query profiling and performance tuning - Query history and monitoring |
| Topic 9: Security & Access Control | 10% | - Masking policies - Row-level and column-level security - Governance best practices - Authorization (roles, privileges) - Authentication (SSO, MFA) |
| Topic 10: Data Sharing | 12% | - Data listing marketplace - Reader account management - Private sharing vs. Data Exchange - Shares and reader accounts |
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NEW QUESTION # 74
Which of the following compute resources or features are managed by Snowflake? (Select TWO).
Answer: B,E
Explanation:
Snowflake manages various compute resources and features, including Snowpipe and the ability to scale up a warehouse. Snowpipe is Snowflake's continuous data ingestion service that allows users to load data as soon as it becomes available. Scaling up a warehouse refers to increasing the compute resources allocated to a virtual warehouse to handle larger workloads or improve performance.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Snowpipe and Virtual Warehouses1
NEW QUESTION # 75
Which of the following activities consume virtual warehouse credits in the Snowflake environment? (Choose two.)
Answer: B,E
Explanation:
Running EXPLAIN and SHOW commands, as well as running a custom query, consume virtual warehouse credits in the Snowflake environment. These activities require computational resources, and therefore, credits are used to account for the usage of these resources. References: [ COF-C03 ] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 76
Which command will unload data from a table into an external stage?
Answer: D
Explanation:
In Snowflake, the COPY INTO <location> command is used to unload (export) data from a Snowflake table to an external stage, such as an S3 bucket, Azure Blob Storage, or Google Cloud Storage. This command allows users to specify the format, file size, and other options for the data being unloaded, making it a flexible solution for exporting data from Snowflake to external storage solutions for further use or analysis.References: Snowflake Documentation on Data Unloading
NEW QUESTION # 77
What effect does WAIT_FOR_COMPLETION = TRUE have when running an ALTER WAREHOUSE command and changing the warehouse size?
Answer: A
Explanation:
The WAIT_FOR_COMPLETION = TRUE parameter in an ALTER WAREHOUSE command ensures that the command does not return until the warehouse has completed resizing. This means that the command will wait until all the necessary compute resources have been provisioned and the warehouse size has been changed. References: [ COF-C03 ] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 78
Which account__usage views are used to evaluate the details of dynamic data masking? (Select TWO)
Answer: C,D
Explanation:
To evaluate the details of dynamic data masking,
the POLICY_REFERENCES and ACCESS_HISTORY views in the account_usage schema are used.
The POLICY_REFERENCES view provides information about the objects to which a masking policy is applied, and the ACCESS_HISTORY view contains details about access to the masked data, which can be used to audit and verify the application of dynamic data masking policies.
References:
[ COF-C03 ] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Dynamic Data Masking1
NEW QUESTION # 79
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