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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security & Access Control | 10% | - Authentication (SSO, MFA) - Row-level and column-level security - Governance best practices - Masking policies - Authorization (roles, privileges) |
| Topic 2: Data Loading & Unloading | 15% | - File formats (JSON, CSV, Parquet, AVRO) - Continuous loading with Snowpipe - Data ingestion best practices - Bulk loading with COPY command - Data unloading and staging |
| Topic 3: Time Travel & Cloning | 10% | - Time travel concepts and retention - UNDROP and historical data access - Fail-safe concepts - Zero-copy cloning |
| Topic 4: Data Sharing | 12% | - Private sharing vs. Data Exchange - Data listing marketplace - Reader account management - Shares and reader accounts |
| Topic 5: Databases, Tables & Views | 18% | - Table types (permanent, transient, temporary) - Views (standard, materialized, secure) - Clustering and micro-partitions - Data manipulation (DML operations) - Column types and sequences - Stored procedures and UDFs |
| Topic 6: Virtual Warehouses | 15% | - Multi-cluster warehouses - Warehouse monitoring and troubleshooting - Cache concepts (warehouse, result, metadata) - Warehouse creation and configuration - Warehouse sizing and scaling |
| Topic 7: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake architecture layers (storage, compute, services) - Virtual warehouse concepts and credit usage - Snowflake editions and features |
| Topic 8: Performance Optimization & Monitoring | 4% | - Query profiling and performance tuning - Resource optimization techniques - Query history and monitoring |
| Topic 9: Account Objects | 8% | - Warehouses, databases, and schemas hierarchy - Resource monitors - Organizations and accounts structure |
| Topic 10: Semi-Structured Data | 10% | - Parsing and flattening semi-structured data - Array and object operations - VARIANT data type |
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NEW QUESTION # 398
Which components make up the Snowflake Cortex AI suite? Select TWO.
Answer: A,D
Explanation:
The correct answers are A. Document AI and E. Cortex Analyst .
Snowflake Cortex is Snowflake's integrated AI and machine learning capability set. It includes features for working with language models, document extraction, natural language analytics, and AI-powered application development.
Why A is correct:
Document AI is part of Snowflake Cortex. It is used to extract information from unstructured documents using AI.
Why E is correct:
Cortex Analyst is part of Snowflake Cortex. It enables natural language questions over structured data by using semantic models and Cortex capabilities.
Why the other options are incorrect:
B). Snowpipe is a data ingestion service, not part of the Cortex AI suite.
C). Streams are change data capture objects used to track table changes, not Cortex AI components.
D). Secure views are security/governance objects used to protect view definitions and support secure data sharing, not Cortex AI components.
Official Snowflake documentation reference:
Snowflake documentation describes Snowflake Cortex as a set of AI and ML capabilities and includes features such as Document AI and Cortex Analyst. Snowpipe, streams, and secure views belong to other Snowflake feature areas.
Reference: Snowflake Documentation - Snowflake Cortex; Snowflake Documentation - Document AI; Snowflake Documentation - Cortex Analyst; SnowPro Core Study Guide - Snowflake Architecture.
NEW QUESTION # 399
What technique does Snowflake use to limit the number of micro-partitions scanned by each query?
Answer: B
Explanation:
Snowflake uses a technique called pruning to limit the number of micro-partitions scanned by each query.Pruning effectively filters out unnecessary micro-partitions based on the query's filter conditions, which can significantly improve query performance by reducing the amount of data scanned1.
NEW QUESTION # 400
Which function is used to convert rows in a relational table to a single VARIANT column?
Answer: D
Explanation:
The OBJECT_CONSTRUCT function in Snowflake is used to convert rows in a relational table into a single VARIANT column that represents each row as a JSON object. This function dynamically creates a JSON object from a list of key-value pairs, where each key is a column name and each value is the corresponding column value for a row. This is particularly useful for aggregating and transforming structured data into semi-structured JSON format for further processing or analysis.
References:
Snowflake Documentation: Semi-structured Data Functions
NEW QUESTION # 401
How can network and private connectivity security be managed in Snowflake?
Answer: D
NEW QUESTION # 402
A user needs to connect a private GitHub repository to Snowflake for stored-procedure version control. The user's organization requires secure authentication.
Which authentication method should be configured for access to the private GitHub repository?
Answer: D
Explanation:
For a private GitHub repository accessed over HTTPS, Snowflake can authenticate using a GitHub personal access token. The token is stored securely in a Snowflake secret rather than written directly into the Git repository object.
The secret is referenced by the API integration or Git repository configuration. For GitHub, a personal access token is generally supplied in the password field of a password-type Snowflake secret.
A normal GitHub account password is not the recommended authentication mechanism for Git operations.
Interactive MFA cannot be performed by Snowflake when it synchronizes a repository. An arbitrary OAuth token is not the standard answer for the documented private-GitHub setup represented by this question.
References:
Snowflake Documentation - Setting up Snowflake to use Git: https://docs.snowflake.com/en/developer-guide
/git/git-setting-up
Snowflake Documentation - Using a Git repository in Snowflake: https://docs.snowflake.com/en/developer- guide/git/git-overview
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NEW QUESTION # 403
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