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| Section | Weight | Objectives |
|---|---|---|
| Security & Access Control | 10% | - Row-level and column-level security - Masking policies - Authorization (roles, privileges) - Governance best practices - Authentication (SSO, MFA) |
| Performance Optimization & Monitoring | 4% | - Query profiling and performance tuning - Query history and monitoring - Resource optimization techniques |
| Time Travel & Cloning | 10% | - Time travel concepts and retention - UNDROP and historical data access - Fail-safe concepts - Zero-copy cloning |
| Databases, Tables & Views | 18% | - Column types and sequences - Views (standard, materialized, secure) - Data manipulation (DML operations) - Table types (permanent, transient, temporary) - Clustering and micro-partitions - Stored procedures and UDFs |
| Semi-Structured Data | 10% | - Array and object operations - Parsing and flattening semi-structured data - VARIANT data type |
| Account Objects | 8% | - Organizations and accounts structure - Resource monitors - Warehouses, databases, and schemas hierarchy |
| Virtual Warehouses | 15% | - Multi-cluster warehouses - Cache concepts (warehouse, result, metadata) - Warehouse monitoring and troubleshooting - Warehouse creation and configuration - Warehouse sizing and scaling |
| Data Loading & Unloading | 15% | - Bulk loading with COPY command - Continuous loading with Snowpipe - Data unloading and staging - Data ingestion best practices - File formats (JSON, CSV, Parquet, AVRO) |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake editions and features - Snowflake architecture layers (storage, compute, services) - Virtual warehouse concepts and credit usage |
| Data Sharing | 12% | - Private sharing vs. Data Exchange - Data listing marketplace - Reader account management - Shares and reader accounts |
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NEW QUESTION # 1021
Using variables in Snowflake is denoted by using which SQL character?
Answer: D
Explanation:
VeryComprehensiveExplanation=InSnowflake,variablesaredenotedbyadollarsign(). Variables can be used in SQL statements where a literal constant is allowed, and they must be prefixed with a $ sign to distinguish them from bind values and column names.
NEW QUESTION # 1022
A dashboard query is running much slower than expected. The Query Profile shows a join operator consuming 70% of the total execution time and producing 1,000 times more rows than its input tables. What is causing this issue?
Answer: D
Explanation:
A join operator producing dramatically more rows than its input tables usually indicates a join explosion, commonly caused by a missing, incomplete, or incorrect join condition. This can result in a Cartesian product where rows from one table are matched with many unrelated rows from another table.
Reference: Snowflake Documentation - Query Profile; Join operators; Query optimization.
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NEW QUESTION # 1023
If a column contains float values that are greater than (15,9) and the values need to be preserved when unloading data, what data file format should be used?
Answer: B
Explanation:
The correct answer is A. JSON .
When unloading floating-point or high-precision numeric values, users must consider whether the target file format can preserve the values as needed. JSON stores values in a textual semi-structured representation and can preserve numeric values without forcing them into a fixed Parquet numeric precision representation.
Why A is correct:
For values that exceed a precision/scale such as (15,9) and must be preserved during unloading, JSON is the safest option among the listed choices because it can represent the values textually in the output.
Why the other options are incorrect:
B). PARQUET has limitations for certain numeric precision/scale representations during unload. Snowflake documentation notes precision-related considerations for Parquet unloads.
C). AVRO is a semi-structured file format, but it is not the best documented answer for preserving float values beyond this precision scenario.
D). ORC is not the best answer for this Snowflake unload precision scenario.
Official Snowflake documentation reference:
Snowflake documentation for unloading data explains that file format choice can affect how numeric values are represented and preserved. JSON is commonly used when preserving semi-structured or text-based representations is required.
Reference: Snowflake Documentation - Unloading data; Snowflake Documentation - File format considerations; SnowPro Core Study Guide - Data Loading and Unloading.
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NEW QUESTION # 1024
Which Snowflake objects can be restored using Time Travel? (Select VNO).
Answer: B,E
Explanation:
Snowflake's Time Travel feature allows users to access historical data within a specific period. This feature supports the restoration of various objects, including databases and schemas, to their previous states. Time Travel can be used for recovering dropped objects, undoing accidental changes, or analyzing data changes over time. However, it does not support user or role objects like Users and Roles, or compute resources like Virtual Warehouses.
References: Snowflake Documentation on Time Travel
NEW QUESTION # 1025
Which solution improves the performance of point lookup queries that return a small number of rows from large tables using highly selective filters?
Answer: C
Explanation:
The search optimization service improves the performance of point lookup queries on large tables by using selective filters to quickly return a small number of rows.It creates an optimized data structure that helps in pruning the micro-partitions that do not contain the queried values3. References: [COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 1026
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