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| Section | Weight | Objectives |
|---|---|---|
| Account Management & Data Governance | 20% | - Security: Network Policies, MFA, Encryption - Governance: Resource Monitors, Data Masking, Row/Column Security - Access Control: RBAC, DAC, Roles, Privileges - Trust Center, Data Lineage, Compliance |
| Performance Optimization, Querying & Transformation | 21% | - Materialized Views, Streams, Tasks, Pipelines - Cortex AI, Notebooks, Apache Iceberg Tables - SQL Transformations, Semi-structured/Unstructured Data - Query Processing, Profiling, Optimization |
| Snowflake Architecture & Core Concepts | 31% | - Cloud Services Layer, Compute Layer, Storage Layer - Databases, Schemas, Objects, Data Types - Snowsight Interface, Core Platform Features - Virtual Warehouses, Clustering, Caching |
| Data Collaboration & Protection | 10% | - Data Protection, Retention, Time Travel, Fail-safe - Data Clean Rooms, Collaboration Features - Secure Data Sharing, Listings, Marketplace |
| Data Loading, Unloading & Connectivity | 18% | - External Integrations, Connectors, SnowSQL - Stages, File Formats, COPY INTO, Snowpipe - Unloading Data, Export Options - Bulk Load, Streaming, Dynamic Tables |
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NEW QUESTION # 68
A user manages a 2 TB sales table clustered by order_date. Multiple teams query this data, but one specific team repeatedly filters on the region column, retrieving only a few thousand rows per query across hundreds of daily requests. The table is refreshed weekly, and no other teams report performance problems. Which action should be taken to optimize query performance for this specific team?
Answer: D
Explanation:
The Search Optimization Service is useful for selective point lookup or highly selective filter queries. Since one team frequently filters on region and retrieves a small number of rows from a very large table, search optimization is the best targeted solution.
Reference: Snowflake Documentation - Search Optimization Service; Search access path; Selective query optimization.
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NEW QUESTION # 69
Which Snowflake tasks are managed in the cloud services layer? Select TWO.
Answer: A,C
Explanation:
The correct answers are C. Metadata management and E. Query parsing .
Snowflake's architecture has three main layers:
Cloud Services Layer
Query Processing Layer / Compute Layer
Database Storage Layer
The cloud services layer coordinates Snowflake activities and handles services such as authentication, infrastructure management, metadata management, query parsing and optimization, access control, and transaction management.
Why C is correct:
Metadata management is handled by the cloud services layer. This includes metadata about tables, micro- partitions, query optimization statistics, and access-related metadata.
Why E is correct:
Query parsing and optimization are handled by the cloud services layer before execution is assigned to compute resources.
Why the other options are incorrect:
A). Performance tuning is not usually described as a discrete cloud services task. Snowflake automatically optimizes many operations, but this option is too broad.
B). Query acceleration uses additional compute resources and is a performance feature, not a core task of the cloud services layer.
D). Query processing is performed by virtual warehouses in the compute layer, not by the cloud services layer.
Official Snowflake documentation reference:
Snowflake documentation explains that the cloud services layer manages authentication, infrastructure management, metadata, query parsing and optimization, access control, and transaction management.
Reference: Snowflake Documentation - Snowflake architecture; Snowflake Documentation - Cloud services layer; SnowPro Core Study Guide - Snowflake Architecture.
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NEW QUESTION # 70
A user needs to create a materialized view in the schema MYDB.MYSCHEMA. Which statements will provide this access?
Answer: A
Explanation:
To provide a user with the necessary access to create a materialized view in a schema, the user must be granted a role that has the CREATE MATERIALIZED VIEW privilege on that schema.First, the role is granted to the user, and then the privilege is granted to the role
NEW QUESTION # 71
What does Snowflake recommend as a best practice for using secure views?
Answer: A
Explanation:
Snowflake recommends not exposing sequence-generated columns in secure views. Secure views are used to protect sensitive data by ensuring that users can only access data for which they have permissions. Exposing sequence-generated columns can potentially reveal information about the underlying data structure or the number of rows, which might be sensitive.
Create Secure Views: Define secure views using the SECURE keyword to ensure they comply with Snowflake's security policies.
Exclude Sensitive Columns: When creating secure views, exclude columns that might expose sensitive information, such as sequence-generated columns.
CREATE SECURE VIEW secure_view AS
SELECT col1, col2
FROM sensitive_table
WHERE sensitive_column IS NOT NULL;
References:
Snowflake Documentation: Secure Views
Snowflake Documentation: Creating Secure Views
These answers and explanations should provide comprehensive guidance on the specified Snowflake topics.
NEW QUESTION # 72
Which commands extract the value for a key, key_1, from an object column? Select TWO.
Answer: B,E
Explanation:
The correct answers are A. SELECT column:key_1 FROM my_table; and C. SELECT column[ ' key_1 '
] FROM my_table; .
Snowflake supports path notation for querying semi-structured data stored in columns such as VARIANT, OBJECT, and ARRAY. For a JSON object key, Snowflake supports colon path notation and bracket notation.
Why A is correct:
The colon path notation accesses a field inside a semi-structured column.
Example:
SELECT column:key_1
FROM my_table;
This extracts the value associated with the key key_1.
Why C is correct:
Bracket notation also accesses an object element by key.
Example:
SELECT column[ ' key_1 ' ]
FROM my_table;
This is especially useful when key names require quoting or contain special characters.
Why the other options are incorrect:
B). Dot notation is supported for nested traversal after the initial colon path, such as column:key_1.subkey, but column.key_1 by itself is interpreted as regular SQL object/column qualification, not as extracting a key from a semi-structured column.
D). Parentheses are not valid JSON object key extraction syntax in Snowflake.
E). Casting a column to string and aliasing it does not extract the value for the key key_1.
Official Snowflake documentation reference:
Snowflake documentation explains that elements in semi-structured data can be accessed using colon path notation and bracket notation. Dot notation may also be used for nested paths after the semi-structured column reference.
Reference: Snowflake Documentation - Querying semi-structured data; Snowflake Documentation - Traversing semi-structured data; SnowPro Core Study Guide - Working with Semi-Structured Data.
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NEW QUESTION # 73
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