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| Section | Objectives |
|---|---|
| Topic 1: Snowflake Architecture and Overview | - Snowflake architecture concepts (cloud data platform, separation of storage and compute) - Virtual warehouses and resource scaling concepts |
| Topic 2: Data Protection, Security, and Performance | - Role-based access control (RBAC) - Query performance optimization basics - Time Travel and Fail-safe concepts |
| Topic 3: Data Transformations and Querying | - SQL querying in Snowflake - Semi-structured data handling (VARIANT, JSON, Parquet) |
| Topic 4: Data Loading and Unloading | - Data unloading and external stages - Bulk loading using stages and COPY INTO |
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NEW QUESTION # 401
The following JSON is stored in a VARIANT column called src of the CAR_SALES table:
A user needs to extract the dealership information from the JSON.
How can this be accomplished?
Answer: D
Explanation:
In Snowflake, to extract a specific element from a JSON stored in a VARIANT column, the correct syntax is to use the dot notation. Therefore, the query select src.dealership from car_sales; will return the dealership information contained within each JSON object in the src column.
NEW QUESTION # 402
How many resource monitors can be assigned at the account level?
Answer: C
NEW QUESTION # 403
What will happen when the data retention period for an object is set to 0 days?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
* Why B (Time Travel will be deactivated for the object) is correct:In Snowflake, the data retention period determines how long historical data (including deleted or modified data) is retained for Time Travel. When this period is set to 0 days, Time Travel is effectively disabled for that object (such as a table, schema, or database).
From the official Snowflake documentation:
"The DATA_RETENTION_TIME_IN_DAYS parameter specifies the number of days for which historical data is retained for Time Travel. A value of 0 disables Time Travel for the object." This means:
* When DATA_RETENTION_TIME_IN_DAYS = 0, no historical versions of the object's data are retained.
* As a result, Time Travel queries (using AT or BEFORE clauses) and undrop/recovery operations are not available for that object.
* Why A (Streams containing the object will become stale) is incorrect:Streams become stale when the data change tracking window exceeds the retention period of the source table's Time Travel, but only if the retention period expires after some time. Setting retention to 0 disables Time Travel immediately, but doesn't by itself make existing streams "stale." Instead, new streams cannot be used effectively because there is no historical data to track.
* Why C (Data replication will be disabled) is incorrect:Data replication is independent of the Time Travel retention period. A retention period of 0 does not affect replication; it only impacts the Time Travel feature.
* Why D (Data sharing will be disabled) is incorrect:Data sharing is a separate Snowflake feature (using Secure Data Sharing). It is not impacted by Time Travel retention settings. Shared data can still be accessible regardless of the retention period.
References (Snowflake SnowPro Core / Study Guide / Official Documentation):
* Snowflake Documentation - Time Travel:
"Setting DATA_RETENTION_TIME_IN_DAYS = 0 disables Time Travel for the object."
* Snowflake Documentation - Object Parameters:Describes
DATA_RETENTION_TIME_IN_DAYS behavior at the database, schema, and table levels.
* Snowflake Study Guide - Data Protection and Governance:Clarifies that setting retention to zero disables historical data storage and deactivates Time Travel functionality.
NEW QUESTION # 404
When unloading to a stage, which of the following is a recommended practice or approach?
Answer: B
NEW QUESTION # 405
Assume there is a table consisting of five micro-partitions with values ranging from A to Z.
Which diagram indicates a well-clustered table?
Answer: B
Explanation:
A well-clustered table in Snowflake means that the data is organized in such a way that related data points are stored close to each other within the micro-partitions. This optimizes query performance by reducing the amount of scanned data. The diagram indicated by option C shows a well-clustered table, as it likely represents a more evenly distributed range of values across the micro-partitions1.
References = Snowflake Micro-partitions & Table Clustering
NEW QUESTION # 406
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