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Snowflake ARA-C01 certification exam covers a wide range of topics including Snowflake architecture, data modeling, performance optimization, security, and governance. To pass the certification exam, candidates must demonstrate their expertise in designing and implementing Snowflake solutions that meet the business requirements of their organization. Candidates must also demonstrate their ability to optimize the performance of Snowflake solutions and ensure the security and governance of Snowflake data.
Snowflake ARA-C01 Exam covers a wide range of topics, including Snowflake architecture, data loading, performance tuning, security and access control, and data sharing. Candidates are expected to have a deep understanding of Snowflake architecture, including the various components of a Snowflake deployment and how they work together. They must also be able to design and implement Snowflake solutions that meet business requirements and performance expectations.
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Snowflake ARA-C01 Certification Exam consists of 60 multiple-choice questions and has a time limit of 120 minutes. ARA-C01 exam is administered online and can be taken from anywhere in the world. The passing score for the exam is 80%, and individuals who pass the exam will receive the SnowPro Advanced Architect Certification. SnowPro Advanced Architect Certification certification is recognized globally and demonstrates that the holder has the skills and knowledge to design and deploy complex Snowflake solutions that meet the needs of their organization. The SnowPro Advanced Architect Certification is a valuable credential for data architects, data engineers, and data analysts who work with Snowflake and want to advance their careers in the field of data management and analytics.
Snowflake SnowPro Advanced Architect Certification Sample Questions (Q149-Q154):
NEW QUESTION # 149
An Architect is designing a solution that will be used to process changed records in an orders table.
Newly-inserted orders must be loaded into the f_orders fact table, which will aggregate all the orders by multiple dimensions (time, region, channel, etc.). Existing orders can be updated by the sales department within 30 days after the order creation. In case of an order update, the solution must perform two actions:
1. Update the order in the f_0RDERS fact table.
2. Load the changed order data into the special table ORDER _REPAIRS.
This table is used by the Accounting department once a month. If the order has been changed, the Accounting team needs to know the latest details and perform the necessary actions based on the data in the order_repairs table.
What data processing logic design will be the MOST performant?
Answer: B
Explanation:
The most performant design for processing changed records, considering the need to both update records in the f_orders fact table and load changes into the order_repairs table, is to use one stream and two tasks. The stream will monitor changes in the orders table, capturing both inserts and updates. The first task would apply these changes to the f_orders fact table, ensuring all dimensions are accurately represented. The second task would use the same stream to insert relevant changes into the order_repairs table, which is critical for the Accounting department's monthly review. This method ensures efficient processing by minimizing the overhead of managing multiple streams and synchronizing between them, while also allowing specific tasks to optimize for their target operations.References: Snowflake's documentation on streams and tasks for handling data changes efficiently.
NEW QUESTION # 150
Based on the architecture in the image, how can the data from DB1 be copied into TBL2? (Select TWO).
Answer: A,C
Explanation:
* The architecture in the image shows a Snowflake data platform with two databases, DB1 and DB2, and two schemas, SH1 and SH2. DB1 contains a table TBL1 and a stage STAGE1. DB2 contains a table TBL2. The image also shows a snippet of code written in SQL language that copies data from STAGE1 to TBL2 using a file format FF PIPE 1.
* To copy data from DB1 to TBL2, there are two possible options among the choices given:
* Option B: Use a named external stage that references STAGE1. This option requires creating an external stage object in DB2.SH2 that points to the same location as STAGE1 in DB1.SH1. The external stage can be created using the CREATE STAGE command with the URL parameter specifying the location of STAGE11. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
use database DB2;
use schema SH2;
create stage EXT_STAGE1
url = @DB1.SH1.STAGE1;
* Then, the data can be copied from the external stage to TBL2 using the COPY INTO command with the FROM parameter specifying the external stage name and the FILE FORMAT parameter specifying the file format name2. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
copy into TBL2
from @EXT_STAGE1
file format = (format name = DB1.SH1.FF PIPE 1);
* Option E: Use a cross-database query to select data from TBL1 and insert into TBL2. This option requires using the INSERT INTO command with the SELECT clause to query data from TBL1 in DB1.SH1 and insert it into TBL2 in DB2.SH2. The query must use the fully-qualified names of the tables, including the database and schema names3. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
use database DB2;
use schema SH2;
insert into TBL2
select * from DB1.SH1.TBL1;
* The other options are not valid because:
* Option A: It uses an invalid syntax for the COPY INTO command. The FROM parameter cannot specify a table name, only a stage name or a file location2.
* Option C: It uses an invalid syntax for the COPY INTO command. The FILE FORMAT parameter cannot specify a stage name, only a file format name or options2.
* Option D: It uses an invalid syntax for the CREATE STAGE command. The URL parameter cannot specify a table name, only a file location1.
References:
* 1: CREATE STAGE | Snowflake Documentation
* 2: COPY INTO table | Snowflake Documentation
* 3: Cross-database Queries | Snowflake Documentation
NEW QUESTION # 151
Which statements describe characteristics of the use of materialized views in Snowflake? (Choose two.)
Answer: B,E
Explanation:
According to the Snowflake documentation, materialized views have some limitations on the query specification that defines them. One of these limitations is that they cannot include nested subqueries, such as subqueries in the FROM clause or scalar subqueries in the SELECT list. Another limitation is that they cannot include ORDER BY clauses, context functions (such as CURRENT_TIME()), or outer joins. However, materialized views can support MIN and MAX aggregates, as well as other aggregate functions, such as SUM, COUNT, and AVG.
References:
* Limitations on Creating Materialized Views | Snowflake Documentation
* Working with Materialized Views | Snowflake Documentation
NEW QUESTION # 152
You have a table named customer_table. You want to create another table as customer_table_other which will be same as customer_table with respect to schema and data.
What is the best option?
Answer: B
NEW QUESTION # 153
Which of the below privileges are required for search optimization?
Answer: A,C
NEW QUESTION # 154
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