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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a comprehensive certification program designed for professionals who want to demonstrate their expertise in advanced Snowflake architecture. SnowPro Advanced Architect Certification certification program is designed to validate the skills and knowledge of individuals who have a deep understanding of Snowflake's architecture and design principles, and who can translate business requirements into scalable, secure, and high-performing Snowflake solutions.
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Snowflake ARA-C01: SnowPro Advanced Architect Certification Exam is a highly respected and sought-after certification in the field of data warehousing and cloud computing. It is designed to test the advanced knowledge and skills of architects who are responsible for designing and implementing complex data warehousing solutions using Snowflake's cloud data platform. Passing ARA-C01 Exam demonstrates the candidate's expertise in designing and implementing scalable and high-performance data warehouses, data lakes, and data pipelines using Snowflake's cloud data platform.
NEW QUESTION # 73
A Snowflake Architect is designing a multi-tenant application strategy for an organization in the Snowflake Data Cloud and is considering using an Account Per Tenant strategy.
Which requirements will be addressed with this approach? (Choose two.)
Answer: B,E
Explanation:
Explanation
* An Account Per Tenant strategy means creating a separate Snowflake account for each tenant (customer or business unit) of the multi-tenant application.
* This approach has some advantages and disadvantages compared to other strategies, such as Database Per Tenant or Schema Per Tenant.
* One advantage is that each tenant can have a unique data shape, meaning they can define their own tables, views, and other objects without affecting other tenants. This allows for more flexibility and customization for each tenant. Therefore, option D is correct.
* Another advantage is that storage costs can be optimized, because each tenant can use their own storage credits and manage their own data retention policies. This also reduces the risk of data spillover or cross-tenant access. Therefore, option E is correct.
* However, this approach also has some drawbacks, such as:
* It requires more administrative overhead and complexity to manage multiple accounts and their resources.
* It may not optimize compute costs, because each tenant has to provision their own warehouses and pay for their own compute credits. This may result in underutilization or overprovisioning of compute resources. Therefore, option C is incorrect.
* It may not simplify security and RBAC policies, because each account has to define its own roles, users, and privileges. This may increase the risk of human errors or inconsistencies in security configurations. Therefore, option B is incorrect.
* It may not reduce the number of objects per tenant, because each tenant still has to create their own databases, schemas, and other objects within their account. This mayaffect the performance and scalability of the application. Therefore, option A is incorrect.
References: : Multi-Tenant Application Strategies
NEW QUESTION # 74
An Architect is integrating an application that needs to read and write data to Snowflake without installing any additional software on the application server.
How can this requirement be met?
Answer: B
Explanation:
The Snowflake SQL REST API is a REST API that you can use to access and update data in a Snowflake database. You can use this API to execute standard queries and most DDL and DML statements. This API can be used to develop custom applications and integrations that can read and write data to Snowflake without installing any additional software on the application server. Option A is not correct because SnowSQL is a command-line client that requires installation and configuration on the application server. Option B is not correct because the Snowpipe REST API is used to load data from cloud storage into Snowflake tables, not to read or write data to Snowflake. Option D is not correct because the Snowflake ODBC driver is a software component that enables applications to connect to Snowflake using the ODBC protocol, which also requires installation and configuration on the application server. Reference: The answer can be verified from Snowflake's official documentation on the Snowflake SQL REST API available on their website. Here are some relevant links:
Snowflake SQL REST API | Snowflake Documentation
Introduction to the SQL API | Snowflake Documentation
Submitting a Request to Execute SQL Statements | Snowflake Documentation
NEW QUESTION # 75
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?
Answer: A
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
NEW QUESTION # 76
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?
Answer: B
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
NEW QUESTION # 77
A table for IOT devices that measures water usage is created. The table quickly becomes large and contains more than 2 billion rows.
The general query patterns for the table are:
1. DeviceId, lOT_timestamp and Customerld are frequently used in the filter predicate for the select statement
2. The columns City and DeviceManuf acturer are often retrieved
3. There is often a count on Uniqueld
Which field(s) should be used for the clustering key?
Answer: B
NEW QUESTION # 78
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