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| Section | Weight | Objectives |
|---|---|---|
| Snowflake Architecture | 30% | - Data modeling and use cases - Data sharing architecture design - Data protection and recovery strategies - Object hierarchy and architectural impact - Development lifecycle and workload support |
| Performance Optimization | 20% | - Virtual warehouse configuration and sizing - Query performance tuning - Clustering and partitioning strategies - Cost optimization and resource management |
| Data Engineering | 25% | - Data pipeline and integration design - Data loading and unloading solutions - Transformation and processing frameworks |
| Accounts and Security | 25% | - Security, compliance and governance architecture - Security principles and implementation scenarios - Account and database strategy design |
>> Snowflake ARA-C01日本語版受験参考書 <<
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質問 # 43
Which Snowflake data modeling approach is designed for BI queries?
正解：D
解説：
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.
質問 # 44
A table contains five columns and it has millions of records. The cardinality distribution of the columns is shown below:
Column C4 and C5 are mostly used by SELECT queries in the GROUP BY and ORDER BY clauses.
Whereas columns C1, C2 and C3 are heavily used in filter and join conditions of SELECT queries.
The Architect must design a clustering key for this table to improve the query performance.
Based on Snowflake recommendations, how should the clustering key columns be ordered while defining the multi-column clustering key?
正解：D
解説：
Explanation
According to the Snowflake documentation, the following are some considerations for choosing clustering for a table1:
* Clustering is optimal when either:
* You require the fastest possible response times, regardless of cost.
* Your improved query performance offsets the credits required to cluster and maintain the table.
* Clustering is most effective when the clustering key is used in the following types of query predicates:
* Filter predicates (e.g. WHERE clauses)
* Join predicates (e.g. ON clauses)
* Grouping predicates (e.g. GROUP BY clauses)
* Sorting predicates (e.g. ORDER BY clauses)
* Clustering is less effective when the clustering key is not used in any of the above query predicates, or when the clustering key is used in a predicate that requires a function or expression to be applied to the key (e.g. DATE_TRUNC, TO_CHAR, etc.).
* For most tables, Snowflake recommends a maximum of 3 or 4 columns (or expressions) per key.
Adding more than 3-4 columns tends to increase costs more than benefits.
Based on these considerations, the best option for the clustering key columns is C. C1, C3, C2, because:
* These columns are heavily used in filter and join conditions of SELECT queries, which are the most effective types of predicates for clustering.
* These columns have high cardinality, which means they have many distinct values and can help reduce the clustering skew and improve the compression ratio.
* These columns are likely to be correlated with each other, which means they can help co-locate similar rows in the same micro-partitions and improve the scan efficiency.
* These columns do not require any functions or expressions to be applied to them, which means they can be directly used in the predicates without affecting the clustering.
References: 1: Considerations for Choosing Clustering for a Table | Snowflake Documentation
質問 # 45
A company has a table with that has corrupted data, named Data. The company wants to recover the data as it was 5 minutes ago using cloning and Time Travel.
What command will accomplish this?
正解：B
解説：
Explanation
This is the correct command to create a clone of the table Data as it was 5 minutes ago using cloning and Time Travel. Cloning is a feature that allows creating a copy of a database, schema, table, or view without duplicating the data or metadata. Time Travel is a feature that enables accessing historical data (i.e. data that has been changed or deleted) at any point within a defined period. To create a clone of a table at a point in time in the past, the syntax is:
CREATE TABLE <clone_name> CLONE <source_table> AT (OFFSET => <offset_in_seconds>); The OFFSET parameter specifies the time difference in seconds from the present time. A negative value indicates a point in the past. For example, -60*5 means 5 minutes ago. Alternatively, the TIMESTAMP parameter can be used to specify an exact timestamp in the past. The clone will contain the data as it existed in the source table at the specified point in time12.
References:
* Snowflake Documentation: Cloning Objects
* Snowflake Documentation: Cloning Objects at a Point in Time in the Past
質問 # 46
A company's daily Snowflake workload consists of a huge number of concurrent queries triggered between
9pm and 11pm. At the individual level, these queries are smaller statements that get completed within a short time period.
What configuration can the company's Architect implement to enhance the performance of this workload?
(Choose two.)
正解：C、D
解説：
These two configuration options can enhance the performance of the workload that consists of a huge number of concurrent queries that are smaller and faster.
* Enabling a multi-clustered virtual warehouse in maximized mode allows the warehouse to scale out automatically by adding more clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This can improve the concurrency and throughput of the workload by minimizing or preventing queuing. The maximized mode is suitable for workloads that require high performance and low latency, and are less sensitive to credit consumption1.
* Setting the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level allows the warehouse to run more queries concurrently on each cluster. This can
* improve the utilization and efficiency of the warehouse resources, especially for smaller and faster queries that do not require a lot of processing power. The MAX_CONCURRENCY_LEVEL parameter can be set when creating or modifying a warehouse, and it can be changed at any time2.
References:
* Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses
* Snowflake Documentation: MAX_CONCURRENCY_LEVEL
質問 # 47
A group of Data Analysts have been granted the role analyst role. They need a Snowflake database where they can create and modify tables, views, and other objects to load with their own data. The Analysts should not have the ability to give other Snowflake users outside of their role access to this data.
How should these requirements be met?
正解：A
解説：
Granting ANALYST_ROLE OWNERSHIP on the database allows the analysts to create and modify tables, views, and other objects within the database. However, to prevent the analysts from giving other Snowflake users outside of their role access to this data, the ANALYST_ROLE should not have the MANAGE GRANTS privilege on the account. The MANAGE GRANTS privilege enables a role to grant or revoke privileges on any object in the account, regardless of the ownership of the object1. Therefore, by removing this privilege from the ANALYST_ROLE, the analysts can only grant or revoke privileges on the objects that they own within the database, and not on any other objects in the account2.
The other options are not correct because:
* B. Granting SYSADMIN ownership of the database and granting the create schema privilege on the database to the ANALYST_ROLE would allow the analysts to create schemas within the database, but not to create or modify tables, views, or other objects within those schemas. The analysts would need to have the create [object type] privilege on each schema to create or modify objects within the schema3.
* C. Making every schema in the database a managed access schema, owned by SYSADMIN, and granting create privileges on each schema to the ANALYST_ROLE for each type of object that needs to be created would allow the analysts to create and modify objects within the schemas, but not to grant or revoke privileges on those objects. A managed access schema is a schema that requires explicit grants for any access to the objects within the schema, regardless of the ownership of the objects4. Therefore, the analysts would need to have the grant privilege on each schema to grant or revoke privileges on the objects within the schema.
* D. Granting ANALYST_ROLE ownership on the database and granting the ownership on future [object type] s in database privilege to SYSADMIN would allow the analysts to create and modify objects within the database, but also to grant or revoke privileges on those objects. The ownership on future
* [object type] s in database privilege enables a role to automatically become the owner of any new object of the specified type that is created in the database. Therefore, by granting this privilege to SYSADMIN, the analysts would not be able to prevent SYSADMIN from accessing or modifying the objects that they create within the database.
References:
* 1: MANAGE GRANTS Privilege | Snowflake Documentation
* 2: Access Control Privileges | Snowflake Documentation
* 3: CREATE SCHEMA | Snowflake Documentation
* 4: Managed Access | Snowflake Documentation
* : GRANT | Snowflake Documentation
* : Ownership on Future Objects | Snowflake Documentation
* : Ownership and Revoking Privileges | Snowflake Documentation
質問 # 48
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