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| Section | Weight | Objectives |
|---|---|---|
| Databases, Tables & Views | 18% | - Data manipulation (DML operations) - Views (standard, materialized, secure) - Clustering and micro-partitions - Column types and sequences - Stored procedures and UDFs - Table types (permanent, transient, temporary) |
| Time Travel & Cloning | 10% | - UNDROP and historical data access - Zero-copy cloning - Fail-safe concepts - Time travel concepts and retention |
| Account Objects | 8% | - Resource monitors - Warehouses, databases, and schemas hierarchy - Organizations and accounts structure |
| Virtual Warehouses | 15% | - Cache concepts (warehouse, result, metadata) - Warehouse sizing and scaling - Warehouse monitoring and troubleshooting - Warehouse creation and configuration - Multi-cluster warehouses |
| Security & Access Control | 10% | - Masking policies - Governance best practices - Authorization (roles, privileges) - Authentication (SSO, MFA) - Row-level and column-level security |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Virtual warehouse concepts and credit usage - Snowflake editions and features - Snowflake architecture layers (storage, compute, services) |
| Data Loading & Unloading | 15% | - Data ingestion best practices - File formats (JSON, CSV, Parquet, AVRO) - Continuous loading with Snowpipe - Bulk loading with COPY command - Data unloading and staging |
| Semi-Structured Data | 10% | - VARIANT data type - Parsing and flattening semi-structured data - Array and object operations |
| Data Sharing | 12% | - Private sharing vs. Data Exchange - Shares and reader accounts - Data listing marketplace - Reader account management |
| Performance Optimization & Monitoring | 4% | - Query history and monitoring - Resource optimization techniques - Query profiling and performance tuning |
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NEW QUESTION # 856
Which data protection feature should only be used when all other data recovery options have been attempted?
Answer: B
Explanation:
The Fail-safe feature in Snowflake is the last-resort data recovery option that should only be used when all other options, such as Time Travel or Cloning, have been exhausted. Fail-safe provides a 7-day recovery period after the Time Travel window has expired but requires Snowflake Support intervention for data recovery. It is intended for disaster recovery and carries additional storage costs.
NEW QUESTION # 857
The following JSON is stored in a VARIANT column called src of the CAR_SALES table:
A user needs to extract the dealership information from the JSON.
How can this be accomplished?
Answer: B
Explanation:
In Snowflake, to extract a specific element from a JSON stored in a VARIANT column, the correct syntax is to use the dot notation. Therefore, the query select src.dealership from car_sales; will return the dealership information contained within each JSON object in the src column.
References: For a detailed explanation, please refer to the Snowflake documentation on querying semi- structured data.
NEW QUESTION # 858
Where would a Snowflake user find information about query activity from 90 days ago?
Answer: A
Explanation:
To find information about query activity from 90 days ago, a Snowflake user should use the account_usage.
query_history_archive view. This view is designed to provide access to historical query data beyond the default 14-day retention period found in the standard query_history view. It allows users to analyze and audit past query activities for up to 365 days after the date of execution, which includes the 90-day period mentioned.
References:
[ COF-C03 ] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Account Usage Schema1
NEW QUESTION # 859
A user frequently runs a reporting query that returns the total record count and the minimum and maximum timestamp from a 100 TB fact table.
The table is loaded once per week and remains static between loads. The reporting query runs hundreds of times per day and causes significant warehouse-credit consumption because it repeatedly scans the fact table.
Which object will improve performance, minimize repeated query-compute costs, and provide the fastest response time?
Answer: B
Explanation:
A materialized view stores precomputed query results. Snowflake maintains the materialized view when its underlying base-table data changes. Because the source table changes only once per week, maintenance occurs much less frequently than the reporting query is executed.
Repeated queries can read the much smaller precomputed result instead of scanning the 100 TB base table hundreds of times per day. Snowflake's optimizer can also automatically rewrite an eligible query to use a materialized view even when the query references the base table.
A standard view stores only its SQL definition and normally executes the underlying query whenever it is referenced. A temporary table would require manual creation, refresh, and lifecycle management. A dynamic table could materialize transformed results, but it requires refresh scheduling and refresh compute. For this repeated, stable aggregation pattern, the intended optimization object is a materialized view.
Materialized-view maintenance itself can consume credits, so it is most beneficial when query savings outweigh maintenance costs. The table's infrequent load schedule makes that condition more likely.
References:
Snowflake Documentation - Working with materialized views: https://docs.snowflake.com/en/user-guide
/views-materialized
Snowflake Documentation - Deciding when to create a materialized view: https://docs.snowflake.com/en
/user-guide/views-materialized#deciding-when-to-create-a-materialized-view
==
NEW QUESTION # 860
Which of the following significantly improves the performance of selective point lookup queries on a table?
Answer: B
Explanation:
The Search Optimization Service significantly improves the performance of selective point lookup queries on tables by creating and maintaining a persistent data structure called a search access path, which allows some micro-partitions to be skipped when scanning the table
NEW QUESTION # 861
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