ARA-C01 SnowPro Advanced: Architect Certification Exam Questions and Answers

In Snowflake, when a user activates a secondary role during a session, certain privileges associated with DDL (Data Definition Language) operations are restricted. TheCREATEstatement, which falls under DDL operations, cannot be executed using a secondary role. This limitation is designed to enforce role-based access control and ensure that schema modifications are managed carefully, typically reserved for primary roles that have explicit permissions to modify database structures.

To change the edition of a Snowflake account, an organization administrator (ORGADMIN) cannot directly alter the account settings through SQL commands or the Snowflake interface. The proper procedure is to contact Snowflake Support to request an edition change for the account. This ensures that the change is managed correctly and aligns with Snowflake’s operational protocols.

 This is the correct answer because it allows the company to ingest data from different regions using a storage integration for the external stage. A storage integration is a feature that enables secure and easy access to files in external cloud storage from Snowflake. A storage integration can be used to create an external stage, which is a named location that references the files in the external storage. An external stage can be used to load data into Snowflake tables using the COPY INTO command, or to unload data from Snowflake tables using the COPY INTO LOCATION command. A storage integration can support multiple regions and cloud platforms, as long as the external storage service is compatible with Snowflake12.

Snowflake Documentation: Storage Integrations

Snowflake Documentation: External Stages

Dynamic Data Masking is a feature that allows masking sensitive data in query results based on the role of the user who executes the query. A masking policy is a user-defined function that specifies the masking logic and can be applied to one or more columns in one or more tables. A masking policy that is currently set on a table can be dropped using the ALTER TABLE command. A single masking policy can be applied to columns in different tables using the ALTER TABLE command with the SET MASKING POLICY clause. The other options are either incorrect or not supported by Snowflake. A masking policy cannot be applied to the value column of an external table, as external tables do not support column-level security. The role that creates the masking policy will not always see unmasked data in query results, as the masking policy can be applied to the owner role as well. A masking policy cannot be applied to a column with the GEOGRAPHY data type, as Snowflake only supports masking policies for scalar data types. References: Snowflake Documentation: Dynamic Data Masking, Snowflake Documentation: ALTER TABLE

Comprehensive and Detailed Explanation From Exact Extract:

The MATCH_BY_COLUMN_NAME parameter in the COPY INTO

command is used to load semi-structured or structured data, such as CSV, into columns of the target table by matching column names in the data file with those in the table. For CSV files, this parameter requires specific conditions to be met, particularly the presence of a header row in the file, which is used to map columns to the target table.

According to the official Snowflake documentation, when the MATCH_BY_COLUMN_NAME parameter is used with CSV files, it is only supported in specific scenarios and requires the PARSE_HEADER file format option to be set to TRUE. This option indicates that the first row of the CSV file contains column headers, which Snowflake uses to match with the target table's column names. The matching behavior can be configured as CASE_SENSITIVE or CASE_INSENSITIVE, but the default behavior is case-sensitive unless specified otherwise.

However, there is a critical limitation when using MATCH_BY_COLUMN_NAME with CSV files: as of the latest Snowflake documentation, this feature is in Open Private Preview for CSV files and is not generally available for all accounts. When the MATCH_BY_COLUMN_NAME parameter is specified for a CSV file in an environment where this feature is not enabled, or if the PARSE_HEADER option is not set to TRUE, the COPY INTO command will return an error. This is because Snowflake cannot process the column name matching without the header parsing capability, which is not fully supported for CSV files in general availability.

The exact extract from the Snowflake documentation states:

"For loading CSV files, the MATCH_BY_COLUMN_NAME copy option is available in preview. It requires the use of the above-mentioned CSV file format option PARSE_HEADER = TRUE."

Additionally, the documentation clarifies:

"Boolean that specifies whether to use the first row headers in the data files to determine column names. This file format option is applied to the following actions only: Automatically detecting column definitions by using the INFER_SCHEMA function. Loading CSV data into separate columns by using the INFER_SCHEMA function and MATCH_BY_COLUMN_NAME copy option."

Furthermore, a known issue is noted:

"For CSV only, there is a known issue when the INCLUDE_METADATA copy option is used with MATCH_BY_COLUMN_NAME. Do not use this copy option when loading CSV files until the known issue is resolved."

Given that the MATCH_BY_COLUMN_NAME parameter is not fully supported for CSV files in general availability and requires specific preview conditions, attempting to use it without meeting those conditions, such as PARSE_HEADER = TRUE or enabling the preview feature, results in an error. Therefore, option C is correct: The command will return an error.

Option A is incorrect because, while MATCH_BY_COLUMN_NAME expects a header in the CSV file for matching when the feature is enabled, the case-sensitive matching is only true when explicitly set to CASE_SENSITIVE. Additionally, the feature's limited availability means it is not guaranteed to work without causing an error. Option B is incorrect because the parameter is not simply ignored; it triggers an error if the conditions are not met. Option D is incorrect because Snowflake does not issue a warning for unmatched columns in this context; it fails with an error when the parameter is unsupported or misconfigured.

In the context of business intelligence (BI) queries, which are typically focused on data analysis and reporting, the star schema is the most suitable data modeling approach.

Option B: Star Schema- The star schema is a type of relational database schema that is widely used for developing data warehouses and data marts for BI purposes. It consists of a central fact table surrounded by dimension tables. The fact table contains the core data metrics, and the dimension tables contain descriptive attributes related to the fact data. The simplicity of the star schema allows for efficient querying and aggregation, which are common operations in BI reporting.

 Compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. It also involves some pruning of partition files, making the query execution efficient2

If the compilation time is greater than the execution time, it means that the optimizer spent more time analyzing the query than actually running it. This could indicate that the query has overly complex logic, such as multiple joins, subqueries, aggregations, or expressions. The complexity of the query could also affect the size and quality of the query plan, which could impact the performance of the query3

To reduce the compilation time, the Architect can try to simplify the query logic, use views or common table expressions (CTEs) to break down the query into smaller parts, or use hints to guide the optimizer. The Architect can also use the EXPLAIN command to examine the query plan and identify potential bottlenecks or inefficiencies4 References:

1: SnowPro Advanced: Architect | Study Guide 5

2: Snowflake Documentation | Query Profile Overview 6

3: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time 7

4: Snowflake Documentation | Optimizing Query Performance 8

: SnowPro Advanced: Architect | Study Guide

: Query Profile Overview

: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time

: Optimizing Query Performance

Comprehensive and Detailed Explanation From Exact Extract:

To understand this, we must differentiate between:

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