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| Section | Weight | Objectives |
|---|---|---|
| Data Collaboration & Protection | 10% | - Data Protection, Retention, Time Travel, Fail-safe - Secure Data Sharing, Listings, Marketplace - Data Clean Rooms, Collaboration Features |
| Performance Optimization, Querying & Transformation | 21% | - Query Processing, Profiling, Optimization - SQL Transformations, Semi-structured/Unstructured Data - Cortex AI, Notebooks, Apache Iceberg Tables - Materialized Views, Streams, Tasks, Pipelines |
| Account Management & Data Governance | 20% | - Governance: Resource Monitors, Data Masking, Row/Column Security - Access Control: RBAC, DAC, Roles, Privileges - Trust Center, Data Lineage, Compliance - Security: Network Policies, MFA, Encryption |
| Data Loading, Unloading & Connectivity | 18% | - Bulk Load, Streaming, Dynamic Tables - External Integrations, Connectors, SnowSQL - Unloading Data, Export Options - Stages, File Formats, COPY INTO, Snowpipe |
| Snowflake Architecture & Core Concepts | 31% | - Databases, Schemas, Objects, Data Types - Cloud Services Layer, Compute Layer, Storage Layer - Snowsight Interface, Core Platform Features - Virtual Warehouses, Clustering, Caching |
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NEW QUESTION # 1061
What MINIMUM permissions are required to create a pipe in Snowflake? (Select TWO).
Answer: C,E
NEW QUESTION # 1062
When unloading data from a query into a location, which VALIDATION_MODE parameter returns the query results?
Answer: D
Explanation:
The correct answer is B. RETURN_ROWS .
Corrected typo:
Option D contains a typing error. RETURN_ALL_ERRORSs should be RETURN_ALL_ERRORS .
When using COPY INTO < location > to unload data, VALIDATION_MODE = RETURN_ROWS returns the query results instead of unloading them to files. This is useful for validating the result set that would be unloaded.
Why B is correct:
RETURN_ROWS is the validation mode used with unload operations to return rows generated by the query.
Example:
COPY INTO @my_stage/output/
FROM (
SELECT *
FROM customers
)
VALIDATION_MODE = RETURN_ROWS;
Why the other options are incorrect:
A). RETURN_ < n > _ROWS is associated with validation behavior for loading data, not the unload query result validation mode.
C). RETURN_ERRORS returns errors and is used for validating data load errors. It does not return query results.
D). RETURN_ALL_ERRORS returns all errors in validation mode for loading scenarios. It does not return the unload query result set.
Official Snowflake documentation reference:
Snowflake documentation for COPY INTO < location > describes VALIDATION_MODE = RETURN_ROWS as returning the rows that would be unloaded.
Reference: Snowflake Documentation - COPY INTO < location > ; Snowflake Documentation - VALIDATION_MODE; SnowPro Core Study Guide - Data Loading and Unloading.
NEW QUESTION # 1063
Which SNOWFLAKE.ACCOUNT_USAGE schema view allows a user to identify opportunities to optimize a slow-running query?
Answer: D
Explanation:
The QUERY_INSIGHTS view contains insights generated for completed queries when Snowflake detects conditions that may have affected performance. Each insight can include an insight type, descriptive message, topic, and suggested actions for improving the query. Examples include inefficient table scans, missing filters, join-related issues, and opportunities involving clustering keys.
QUERY_ATTRIBUTION_HISTORY focuses on attributing compute costs to queries, while ACCESS_HISTORY records which objects and columns were accessed.
QUERY_ACCELERATION_HISTORY is not the Account Usage view used to provide general query- optimization recommendations. Users can also review query insights interactively in Snowsight, but querying SNOWFLAKE.ACCOUNT_USAGE.QUERY_INSIGHTS supports historical analysis across queries and warehouses. Therefore, QUERY_INSIGHTS is the appropriate view for finding documented opportunities to improve slow-running SQL statements.
NEW QUESTION # 1064
What are the results of the NULL_COUNT Data Metric Function, or DMF, used to verify?
Answer: B
Explanation:
The correct answer is D. The volume of the data .
The NULL_COUNT Data Metric Function returns the number of NULL values in a column. This type of metric is used to verify data quality by measuring the amount, or volume, of missing values.
Why D is correct:
NULL_COUNT counts how many rows contain NULL in a specified column. Since it produces a count, it is used to evaluate the volume of missing data.
Example concept:
SELECT SNOWFLAKE.CORE.NULL_COUNT(
SELECT column_name FROM my_table
);
This returns the number of NULL values found in the selected column.
Why the other options are incorrect:
A). Accuracy verifies whether data values are correct, but NULL_COUNT only counts missing values.
B). Uniqueness is verified with metrics that check duplicate or distinct values, not NULL_COUNT.
C). Freshness measures how current or recent the data is, not how many nulls exist.
Official Snowflake documentation reference:
Snowflake documentation describes NULL_COUNT as a system Data Metric Function that returns the number of NULL values in a column.
Reference: Snowflake Documentation - Data Metric Functions; Snowflake Documentation - SNOWFLAKE.CORE.NULL_COUNT; SnowPro Core Study Guide - Data Protection and Governance.
==
NEW QUESTION # 1065
What happens when an external or an internal stage is dropped? (Select TWO).
Answer: D,E
Explanation:
When an external stage is dropped in Snowflake, the reference to the external storage location is removed, but the actual files within the external storage (like Amazon S3, Google Cloud Storage, or Microsoft Azure) are not deleted. This means that the data remains intact in the external storage location, and only the stage object in Snowflake is removed.
On the other hand, when an internal stage is dropped, any files that were uploaded to the stage are deleted along with the stage itself. These files are not recoverable once the internal stage is dropped, as they are permanently removed from Snowflake's storage.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Stages
NEW QUESTION # 1066
......
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