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| Section | Weight | Objectives |
|---|---|---|
| Accounts and Security | 25% | - Account and database strategy design - Security, compliance and governance architecture - Security principles and implementation scenarios |
| Performance Optimization | 20% | - Cost optimization and resource management - Clustering and partitioning strategies - Virtual warehouse configuration and sizing - Query performance tuning |
| Data Engineering | 25% | - Transformation and processing frameworks - Data loading and unloading solutions - Data pipeline and integration design |
| Snowflake Architecture | 30% | - Data protection and recovery strategies - Development lifecycle and workload support - Data sharing architecture design - Object hierarchy and architectural impact - Data modeling and use cases |
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151. Frage
You have a need to make external file data available to your users with the lowest latency. The files are on an external stage in AWS.
What feature of Snowflake is the most appropriate to use
Antwort: A
152. Frage
Below are the rest APIs provided by Snowpipe
Antwort: A,C
153. Frage
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported.
What could be causing this?
Antwort: A,B
Begründung:
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:
* The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column-like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.
* There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column.
Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the
* JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.
The other options are not valid causes for poor query performance:
* There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi-structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.
* The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.
References:
* Considerations for Semi-structured Data Stored in VARIANT
* Snowflake Architect Training
* Snowflake query performance on unique element in variant column
* Snowflake variant performance
154. Frage
Company A has recently acquired company B. The Snowflake deployment for company B is located in the Azure West Europe region.
As part of the integration process, an Architect has been asked to consolidate company B's sales data into company A's Snowflake account which is located in the AWS us-east-1 region.
How can this requirement be met?
Antwort: B
155. Frage
Based on the Snowflake object hierarchy, what securable objects belong directly to a Snowflake account?
(Select THREE).
Antwort: C,E,F
Begründung:
* A securable object is an entity to which access can be granted in Snowflake. Securable objects include databases, schemas, tables, views, stages, pipes, functions, procedures, sequences, tasks, streams, roles, warehouses, and shares1.
* The Snowflake object hierarchy is a logical structure that organizes the securable objects in a nested manner. The top-most container is the account, which contains all the databases, roles, and warehouses for the customer organization. Each database contains schemas, which in turn contain tables, views, stages, pipes, functions, procedures, sequences, tasks, and streams. Each role can be granted privileges on other roles or securable objects. Each warehouse can be used to execute queries on securable objects2.
* Based on the Snowflake object hierarchy, the securable objects that belong directly to a Snowflake account are databases, roles, and warehouses. These objects are created and managed at the account level, and do not depend on any other securable object. The other options are not correct because:
* Schemas belong to databases, not to accounts. A schema must be created within an existing database3.
* Tables belong to schemas, not to accounts. A table must be created within an existing schema4.
* Stages belong to schemas or tables, not to accounts. A stage must be created within an existing
* schema or table.
References:
* 1: Overview of Access Control | Snowflake Documentation
* 2: Securable Objects | Snowflake Documentation
* 3: CREATE SCHEMA | Snowflake Documentation
* 4: CREATE TABLE | Snowflake Documentation
* [5]: CREATE STAGE | Snowflake Documentation
156. Frage
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P.S. Kostenlose 2026 Snowflake ARA-C01 Prüfungsfragen sind auf Google Drive freigegeben von ExamFragen verfügbar: https://drive.google.com/open?id=1I3iWkTU76-szAn12CyvhHo6X1IhoIuZ5