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| Section | Weight | Objectives |
|---|---|---|
| Time Travel & Cloning | 10% | - Fail-safe concepts - UNDROP and historical data access - Time travel concepts and retention - Zero-copy cloning |
| Databases, Tables & Views | 18% | - Column types and sequences - Data manipulation (DML operations) - Views (standard, materialized, secure) - Stored procedures and UDFs - Clustering and micro-partitions - Table types (permanent, transient, temporary) |
| Account Objects | 8% | - Resource monitors - Warehouses, databases, and schemas hierarchy - Organizations and accounts structure |
| Performance Optimization & Monitoring | 4% | - Query history and monitoring - Query profiling and performance tuning - Resource optimization techniques |
| Virtual Warehouses | 15% | - Warehouse sizing and scaling - Multi-cluster warehouses - Warehouse creation and configuration - Warehouse monitoring and troubleshooting - Cache concepts (warehouse, result, metadata) |
| Semi-Structured Data | 10% | - Parsing and flattening semi-structured data - VARIANT data type - Array and object operations |
| Data Sharing | 12% | - Private sharing vs. Data Exchange - Data listing marketplace - Reader account management - Shares and reader accounts |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Virtual warehouse concepts and credit usage - Snowflake architecture layers (storage, compute, services) - Snowflake editions and features |
| Security & Access Control | 10% | - Authentication (SSO, MFA) - Governance best practices - Row-level and column-level security - Masking policies - Authorization (roles, privileges) |
| Data Loading & Unloading | 15% | - Data unloading and staging - File formats (JSON, CSV, Parquet, AVRO) - Continuous loading with Snowpipe - Data ingestion best practices - Bulk loading with COPY command |
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NEW QUESTION # 585
Why would a Snowflake user load JSON data into a VARIANT column instead of a string column?
Answer: B
Explanation:
A VARIANT column in Snowflake is specifically designed to store semi-structured data, such as JSON, and allows for the creation of a data hierarchy. Unlike string columns, VARIANT columns can natively handle JSON data structures, enabling complex querying and manipulation of hierarchical data using functions designed for semi-structured data.
References:
Snowflake Documentation: VARIANT Data
NEW QUESTION # 586
In which hierarchy is tag inheritance possible?
Answer: B
Explanation:
In Snowflake, tag inheritance is a feature that allows tags, which are key-value pairs assigned to objects for the purpose of data governance and metadata management, to be inherited within a hierarchy. The hierarchy in which tag inheritance is possible is from Schema to Table to Column. This means that a tag applied to a schema can be inherited by the tables within that schema, and a tag applied to a table can be inherited by the columns within that table.References: Snowflake Documentation on Tagging and Object Hierarchy
NEW QUESTION # 587
What is a key difference between JSON and CSV file formats when loading data? Select TWO.
Answer: A,C
Explanation:
The correct answers are A. CSV stores flat, tabular data and E. JSON can be loaded into a VARIANT column without predefined schema .
Important correction:
The provided answer A, B is not correct. CSV does not natively support complex semi-structured data types such as arrays and objects. JSON does.
Why A is correct:
CSV is a delimited, flat file format commonly used for structured, tabular data. Each row usually represents a record, and each field is separated by a delimiter such as a comma.
Why E is correct:
JSON is semi-structured data and can be loaded into a Snowflake VARIANT column. This allows Snowflake to store JSON objects and arrays without requiring every nested field to be predefined as a relational column.
Example:
CREATE TABLE json_data (
src VARIANT
);
COPY INTO json_data
FROM @my_stage
FILE_FORMAT = (TYPE = JSON);
Why the other options are incorrect:
B). CSV does not natively support arrays and objects. JSON supports nested structures such as arrays and objects.
C). JSON does not need to be converted to CSV before it can be queried in Snowflake. Snowflake can query JSON data stored in VARIANT, OBJECT, or ARRAY columns.
D). CSV files do not automatically detect and apply Snowflake table data types during loading. The target table schema and file format options control how data is interpreted.
Official Snowflake documentation reference:
Snowflake documentation explains that CSV is used for structured data, while semi-structured formats such as JSON can be loaded into VARIANT columns and queried using semi-structured data syntax.
Reference: Snowflake Documentation - Semi-structured data; Snowflake Documentation - Loading JSON data; Snowflake Documentation - CSV file format options; SnowPro Core Study Guide - Data Loading and Unloading.
NEW QUESTION # 588
Which virtual warehouse consideration can help lower compute resource credit consumption?
Answer: A
Explanation:
One key strategy to lower compute resource credit consumption in Snowflake is by automating the suspension and resumption of virtual warehouses. Virtual warehouses consume credits when they are running, and managing their operational times effectively can lead to significant cost savings.
A . Setting up a multi-cluster virtual warehouse increases parallelism and throughput but does not directly lower credit consumption. It is more about performance scaling than cost efficiency.
B . Resizing the virtual warehouse to a larger size increases the compute resources available for processing queries, which increases the credit consumption rate. This option does not help in lowering costs.
C . Automating the virtual warehouse suspension and resumption settings: This is a direct method to manage credit consumption efficiently. By automatically suspending a warehouse when it is not in use and resuming it when needed, you can avoid consuming credits during periods of inactivity. Snowflake allows warehouses to be configured to automatically suspend after a specified period of inactivity and to automatically resume when a query is submitted that requires the warehouse.
D . Increasing the maximum cluster count parameter for a multi-cluster virtual warehouse would potentially increase credit consumption by allowing more clusters to run simultaneously. It is used to scale up resources for performance, not to reduce costs.
Automating the operational times of virtual warehouses ensures that you only consume compute credits when the warehouse is actively being used for queries, thereby optimizing your Snowflake credit usage.
NEW QUESTION # 589
A Snowflake user has been granted the create data EXCHANGE listing privilege with their role.
Which tasks can this user now perform on the Data Exchange? (Select TWO).
Answer: A,C
Explanation:
With the create data EXCHANGE listing privilege, a Snowflake user can modify the properties of listings and submit them for approval or publishing on the Data Exchange. This allows them to manage and share data sets with consumers effectively.References:Based on general data exchange practices in cloud services as of 2021.
NEW QUESTION # 590
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