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| Section | Weight | Objectives |
|---|---|---|
| Performance Optimization & Monitoring | 4% | - Query profiling and performance tuning - Resource optimization techniques - Query history and monitoring |
| Time Travel & Cloning | 10% | - UNDROP and historical data access - Fail-safe concepts - Time travel concepts and retention - Zero-copy cloning |
| Data Sharing | 12% | - Reader account management - Shares and reader accounts - Data listing marketplace - Private sharing vs. Data Exchange |
| Account Objects | 8% | - Resource monitors - Organizations and accounts structure - Warehouses, databases, and schemas hierarchy |
| Virtual Warehouses | 15% | - Warehouse creation and configuration - Multi-cluster warehouses - Warehouse monitoring and troubleshooting - Cache concepts (warehouse, result, metadata) - Warehouse sizing and scaling |
| Security & Access Control | 10% | - Authorization (roles, privileges) - Authentication (SSO, MFA) - Masking policies - Governance best practices - Row-level and column-level security |
| Data Loading & Unloading | 15% | - Data unloading and staging - Bulk loading with COPY command - Data ingestion best practices - File formats (JSON, CSV, Parquet, AVRO) - Continuous loading with Snowpipe |
| Databases, Tables & Views | 18% | - Data manipulation (DML operations) - Table types (permanent, transient, temporary) - Views (standard, materialized, secure) - Clustering and micro-partitions - Column types and sequences - Stored procedures and UDFs |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake architecture layers (storage, compute, services) - Virtual warehouse concepts and credit usage - Snowflake editions and features |
| Semi-Structured Data | 10% | - Parsing and flattening semi-structured data - Array and object operations - VARIANT data type |
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NEW QUESTION # 901
Which common query problems are identified by the Query Profile? (Select TWO.)
Answer: A,B
Explanation:
The Query Profile in Snowflake can identify common query problems, including:
B . Inefficient pruning: This refers to the inability of a query to effectively limit the amount of data being scanned, potentially leading to suboptimal performance.
D . Queries too large to fit in memory: This indicates that a query requires more memory than is available in the virtual warehouse, which can lead to spilling to disk and degraded performance.
The Query Profile helps diagnose these issues by providing detailed execution statistics and visualizations, aiding in query optimization and troubleshooting.
References:
Snowflake Documentation: Query Profile
Top of Form
NEW QUESTION # 902
Why would the REPEATABLE keyword be used in a SAMPLE clause?
Answer: B
Explanation:
The correct answer is C. To ensure the same sample is returned each time a query is run .
The REPEATABLE keyword is used with a seed value to make sampling deterministic. This means Snowflake can return the same sample when the query is rerun with the same seed and the underlying data has not changed.
Why C is correct:
SELECT *
FROM table_A
SAMPLE BERNOULLI (10) REPEATABLE (123);
This returns a repeatable sample based on the seed value 123.
Why the other options are incorrect:
A). Returning a fixed number of rows uses SAMPLE ( < n > ROWS), not REPEATABLE.
B). REPEATABLE does not remove duplicates.
D). Block sampling is controlled with BLOCK or SYSTEM, not REPEATABLE.
Official Snowflake documentation reference:
Snowflake documentation explains that REPEATABLE or SEED specifies a seed value to make sample results repeatable.
Reference: Snowflake Documentation - SAMPLE / TABLESAMPLE; SnowPro Core Study Guide - SQL and Snowflake Objects.
NEW QUESTION # 903
Which formats does Snowflake store unstructured data in? (Choose two.)
Answer: A,D
Explanation:
Snowflake supports storing unstructured data and provides native support for semi-structured file formats such as JSON, Avro, Parquet, ORC, and XML1. GeoJSON, being a type of JSON, and XML are among the formats that can be stored in Snowflake. References: [COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 904
A virtual warehouse's auto-suspend and auto-resume settings apply to which of the following?
Answer: A
Explanation:
The auto-suspend and auto-resume settings in Snowflake apply to the entire virtual warehouse. These settings allow the warehouse to automatically suspend when it's not in use, helping to save on compute costs. When queries or tasks are submitted to the warehouse, it can automatically resume operation. This functionality is designed to optimize resource usage and cost-efficiency.
References:
SnowPro Core Certification Exam Study Guide (as of 2021)
Snowflake documentation on virtual warehouses and their settings (as of 2021)
NEW QUESTION # 905
A table is created with a column set to a data type of CHARACTER(25). If the table is viewed in the database section of Snowsight, what data type will be shown for the column?
Answer: C
Explanation:
The correct answer is C. VARCHAR(25) .
In Snowflake, CHAR, CHARACTER, STRING, and TEXT are synonyms for VARCHAR. Snowflake stores and displays these character string data types as VARCHAR.
Corrected typo:
Option D was written as CHARACTERS). The corrected option is CHARACTER(25) .
Why C is correct:
A column created as CHARACTER(25) is treated as VARCHAR(25) in Snowflake metadata and display output.
Why the other options are incorrect:
A). STRING is a synonym for VARCHAR, but Snowflake commonly displays the canonical type as VARCHAR.
B). TEXT is also a synonym for VARCHAR, but it is not the canonical display type.
D). CHARACTER is accepted when defining the column, but Snowflake displays it as VARCHAR.
Official Snowflake documentation reference:
Snowflake documentation explains that string data type synonyms such as CHAR, CHARACTER, STRING, and TEXT are equivalent to VARCHAR.
Reference: Snowflake Documentation - String and binary data types; SnowPro Core Study Guide - SQL and Snowflake Objects.
NEW QUESTION # 906
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