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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Collaboration & Protection | 10% | - Data Clean Rooms, Collaboration Features - Data Protection, Retention, Time Travel, Fail-safe - Secure Data Sharing, Listings, Marketplace |
| Topic 2: Performance Optimization, Querying & Transformation | 21% | - Query Processing, Profiling, Optimization - Materialized Views, Streams, Tasks, Pipelines - SQL Transformations, Semi-structured/Unstructured Data - Cortex AI, Notebooks, Apache Iceberg Tables |
| Topic 3: Data Loading, Unloading & Connectivity | 18% | - External Integrations, Connectors, SnowSQL - Stages, File Formats, COPY INTO, Snowpipe - Bulk Load, Streaming, Dynamic Tables - Unloading Data, Export Options |
| Topic 4: Account Management & Data Governance | 20% | - Trust Center, Data Lineage, Compliance - Governance: Resource Monitors, Data Masking, Row/Column Security - Security: Network Policies, MFA, Encryption - Access Control: RBAC, DAC, Roles, Privileges |
| Topic 5: Snowflake Architecture & Core Concepts | 31% | - Virtual Warehouses, Clustering, Caching - Databases, Schemas, Objects, Data Types - Cloud Services Layer, Compute Layer, Storage Layer - Snowsight Interface, Core Platform Features |
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NEW QUESTION # 135
What are the main differences between the account usage views and the information schema views? (Select TWO).
Answer: C,E
Explanation:
The account usage views in Snowflake provide historical usage data about the Snowflake account, and they retain this data for a period of up to 1 year. These views include information about dropped objects, enabling audit and tracking activities. On the other hand, information schema views provide metadata about database objects currently in use, such as tables and views, but do not include dropped objects. The retention of data in information schema views varies, but it is generally shorter than the retention for account usage views, ranging from 7 days to a maximum of 6 months, depending on the specific view.References: Snowflake Documentation on Account Usage and Information Schema
NEW QUESTION # 136
How can a user optimize the performance of a COPY INTO < table > query that is being run on a very large file?
Answer: B
Explanation:
The correct answer is A. Split the file into smaller files .
Snowflake can load files in parallel. A single very large file limits parallelism because fewer worker threads can process the load concurrently. Splitting a very large file into multiple smaller files allows Snowflake to load the files in parallel, improving performance.
Why A is correct:
For best loading performance, Snowflake recommends splitting large data files into smaller files of a recommended size range so that the load operation can be parallelized.
Why the other options are incorrect:
B). Increasing the warehouse size can sometimes help, but if the load is constrained by a single very large file, file splitting is the better optimization.
C). SIZE_LIMIT controls the maximum amount of data loaded during a COPY command. It does not improve performance for a very large file.
D). COMPRESSION = AUTO lets Snowflake detect compression type. It does not solve the lack of parallelism caused by a single large file.
Official Snowflake documentation reference:
Snowflake documentation recommends splitting large files into smaller files to increase parallelism and improve load performance.
Reference: Snowflake Documentation - Data loading considerations; Snowflake Documentation - COPY INTO < table > ; SnowPro Core Study Guide - Data Loading and Unloading.
==
NEW QUESTION # 137
What feature specifies which roles are allowed to see a particular record in a table?
Answer: D
Explanation:
The correct answer is C. Row access policy .
A row access policy is used to implement row-level security in Snowflake. It controls whether a row is visible in query results based on conditions such as the current role, current user, or values in a mapping table.
Why C is correct:
The question asks which feature controls visibility of particular records, meaning rows. Row access policies are designed exactly for that purpose.
Example concept:
CREATE ROW ACCESS POLICY region_policy
AS (region STRING) RETURNS BOOLEAN - >
region IN (
SELECT allowed_region
FROM role_region_mapping
WHERE role_name = CURRENT_ROLE()
);
This policy can allow users with different roles to see only the rows assigned to their region.
Why the other options are incorrect:
A). Dynamic Data Masking controls how column values are displayed. It masks data in columns but does not filter entire rows.
B). Secure Data Sharing allows data to be shared between accounts, but it is not the row-level filtering feature itself.
D). Object tagging classifies or labels objects for governance, but it does not directly control row visibility.
Official Snowflake documentation reference:
Snowflake documentation describes row access policies as schema-level objects that determine whether a row is visible in query results.
Reference: Snowflake Documentation - Row access policies; Snowflake Documentation - Row-level security; SnowPro Core Study Guide - Data Protection and Governance.
==
NEW QUESTION # 138
When resizing a currently running virtual warehouse, what happens to in-progress queries?
Answer: B
Explanation:
The correct answer is C. Queries will continue running, using the current warehouse size .
When a virtual warehouse is resized while it is running, the resize does not affect queries that are already in progress. Running queries continue using the warehouse resources that were available when they started. The new warehouse size applies to queries that start after the resize operation.
Why C is correct:
Snowflake does not cancel or restart existing queries when a warehouse is resized. In-progress queries continue executing with the current warehouse size.
Why the other options are incorrect:
A). Resizing a warehouse does not automatically cancel in-progress queries.
B). In-progress queries do not switch to the new warehouse size.
D). Queries are not paused and reprocessed after resizing.
Official Snowflake documentation reference:
Snowflake documentation explains that resizing a running warehouse affects only new queries and queued queries. Currently running queries continue to use the warehouse size that was in effect when they started.
Reference: Snowflake Documentation - Resizing a warehouse; Snowflake Documentation - Virtual warehouses; SnowPro Core Study Guide - Snowflake Account and Resource Management.
NEW QUESTION # 139
When floating-point number columns are unloaded to CSV or JSON files, Snowflake truncates the values to approximately what?
Answer: D
Explanation:
When unloading floating-point number columns to CSV or JSON files, Snowflake truncates the values to approximately 15 significant digits with 9 digits following the decimal point, which can be represented as (15,9). This ensures a balance between accuracy and efficiency in representing floating-point numbers in text-based formats, which is essential for data interchange and processing applications that consume these files.
References:
Snowflake Documentation: Data Unloading Considerations
NEW QUESTION # 140
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