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| Section | Weight | Objectives |
|---|---|---|
| Performance Optimization, Querying & Transformation | 21% | - SQL Transformations, Semi-structured/Unstructured Data - Query Processing, Profiling, Optimization - Cortex AI, Notebooks, Apache Iceberg Tables - Materialized Views, Streams, Tasks, Pipelines |
| Data Collaboration & Protection | 10% | - Data Protection, Retention, Time Travel, Fail-safe - Secure Data Sharing, Listings, Marketplace - Data Clean Rooms, Collaboration Features |
| Data Loading, Unloading & Connectivity | 18% | - Unloading Data, Export Options - External Integrations, Connectors, SnowSQL - Bulk Load, Streaming, Dynamic Tables - Stages, File Formats, COPY INTO, Snowpipe |
| Account Management & Data Governance | 20% | - Access Control: RBAC, DAC, Roles, Privileges - Trust Center, Data Lineage, Compliance - Security: Network Policies, MFA, Encryption - Governance: Resource Monitors, Data Masking, Row/Column Security |
| Snowflake Architecture & Core Concepts | 31% | - Databases, Schemas, Objects, Data Types - Cloud Services Layer, Compute Layer, Storage Layer - Virtual Warehouses, Clustering, Caching - Snowsight Interface, Core Platform Features |
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NEW QUESTION # 1118
What is the minimum Snowflake Edition that supports secure storage of Protected Health Information (PHI) data?
Answer: C
Explanation:
The minimum Snowflake Edition that supports secure storage of Protected Health Information (PHI) data is the Business Critical Edition.This edition offers enhanced security features necessary for compliance with regulations such as HIPAA and HITRUST CSF4.
NEW QUESTION # 1119
What is the maximum total Continuous Data Protection (CDP) charges incurred for a temporary table?
Answer: D
Explanation:
For a temporary table, the maximum total Continuous Data Protection (CDP) charges incurred are for the duration of the session in which the table was created, which does not exceed 24 hours2.
References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation2
NEW QUESTION # 1120
When reviewing a query profile, what is a symptom that a query is too large to fit into the memory?
Answer: D
Explanation:
When a query in Snowflake is too large to fit into the available memory, it will start spilling to remote storage. This is an indication that the memory allocated for the query is insufficient for its execution, and as a result, Snowflake uses remote disk storage to handle the overflow. This spill to remote storage can lead to slower query performance due to the additional I/O operations required.
References:
[ COF-C03 ] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Query Profile1
Snowpro Core Certification Exam Flashcards2
NEW QUESTION # 1121
Which copy option is used to include column headers when unloading data to Parquet files?
Answer: D
Explanation:
The correct answer is C. HEADER = TRUE .
When unloading data with the COPY INTO < location > command, the HEADER option specifies whether column names should be included in the output file.
Why C is correct:
HEADER = TRUE includes column headings in the unloaded file output.
Example:
COPY INTO @my_stage/output/
FROM my_table
FILE_FORMAT = (TYPE = PARQUET)
HEADER = TRUE;
Important note for exam review:
For CSV unloads, HEADER = TRUE includes column names in the first row. For Parquet unloads, Snowflake can use column names in the Parquet output metadata/schema when the header option is enabled.
Why the other options are incorrect:
A). PARSE_HEADER is a file format option used when loading CSV data with inferred column names. It is not the unload option for including headers.
B). SKIP_HEADER is used when loading files to skip header rows. It does not include headers during unloading.
D). This syntax is not the correct Snowflake unload option for column headers.
Official Snowflake documentation reference:
Snowflake documentation for COPY INTO < location > describes the HEADER option as specifying whether to include table column headings in the output files.
Reference: Snowflake Documentation - COPY INTO < location > ; Snowflake Documentation - Unloading data; SnowPro Core Study Guide - Data Loading and Unloading.
==
NEW QUESTION # 1122
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: A
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 srcncolumn.
References: For a detailed explanation, please refer to the Snowflake documentation on querying semi-structured data.
NEW QUESTION # 1123
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