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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Architect Certification Exam |
| Exam Number: | ARA-C01 |
| Related Certifications: | SnowPro Advanced: Data Analyst SnowPro Core SnowPro Advanced: Data Engineer |
| Real Exam Qty: | 65 |
| Exam Price: | USD 375 |
| Available Languages: | English |
| Passing Score: | 750 / 1000 |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 115 minutes |
| Exam Format: | Multiple Select, Multiple Choice |
| Recommended Training: | Snowflake Advanced Architect Learning Path Snowflake Documentation |
| Exam Registration: | Snowflake Certification Portal Pearson VUE Registration |
| Sample Questions: | Snowflake ARA-C01 Sample Questions |
| Exam Way: | Online proctored or Onsite testing center |
| Pre Condition: | Recommended: SnowPro Core certification + 2+ years hands-on experience as Snowflake Architect in production environment |
| Official Syllabus URL: | https://learn.snowflake.com/en/certifications/snowpro-advanced-architect/ |
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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a certification program designed for IT professionals who want to demonstrate their expertise in architecting data solutions using Snowflake. ARA-C01 exam is intended for individuals who have already obtained the SnowPro Core or SnowPro Advanced certification and have experience working with Snowflake as a data warehousing platform.
The SnowPro Advanced Architect Certification exam is a comprehensive test that covers various topics, including data modeling, security, performance optimization, and data integration. ARA-C01 Exam is designed to assess an individual's ability to design, implement, and optimize advanced Snowflake solutions that meet complex business requirements.
Snowflake ARA-C01 (SnowPro Advanced Architect Certification) is a professional certification exam designed to validate the skills and knowledge of data architects and engineers who specialize in designing and implementing complex Snowflake cloud data platforms. SnowPro Advanced Architect Certification certification exam is recognized globally by companies that use Snowflake for their data management, warehousing, and analytics needs.
NEW QUESTION # 171
An Architect needs to define a table structure for an unfamiliar semi-structured data set. The Architect wants to identify a list of distinct key names present in the semi-structured objects.
What function should be used?
Answer: C
Explanation:
When working with unfamiliar semi-structured data such as JSON, a common first step is to explore its structure and identify all possible keys. Snowflake's FLATTEN function is specifically designed to explode VARIANT, OBJECT, or ARRAY data into relational form. Using the RECURSIVE option allows FLATTEN to traverse nested objects and arrays, returning all nested keys regardless of depth (Answer A).
This approach enables architects to query and aggregate distinct key names, making it ideal for schema discovery and exploratory analysis. INFER_SCHEMA, by contrast, is used primarily with staged files to infer column definitions for external tables or COPY operations, not for exploring existing VARIANT data already stored in tables. PARSE_JSON simply converts a string into a VARIANT type and does not help identify keys. RESULT_SCAN is used to query the results of a previously executed query and is unrelated to schema discovery.
For SnowPro Architect candidates, this highlights an important semi-structured data design pattern: using FLATTEN (often with RECURSIVE) to explore, profile, and understand evolving data structures before committing to a relational schema or transformation pipeline.
=========
NEW QUESTION # 172
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.)
Answer: B,E
Explanation:
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
NEW QUESTION # 173
When loading data from stage using COPY INTO, what options can you specify for the ON_ERROR clause?
Answer: A,B,D
Explanation:
* The ON_ERROR clause is an optional parameter for the COPY INTO command that specifies the behavior of the command when it encounters errors in the files. The ON_ERROR clause can have one of the following values1:
* CONTINUE: This value instructs the command to continue loading the file and return an error message for a maximum of one error encountered per data file. The difference between the ROWS_PARSED and ROWS_LOADED column values represents the number of rows that include detected errors. To view all errors in the data files, use the VALIDATION_MODE parameter or query the VALIDATE function1.
* SKIP_FILE: This value instructs the command to skip the file when it encounters a data error on any of the records in the file. The command moves on to the next file in the stage and continues loading. The skipped file is not loaded and no error message is returned for the file1.
* ABORT_STATEMENT: This value instructs the command to stop loading data when the first error is encountered. The command returns an error message for the file and aborts the load operation. This is the default value for the ON_ERROR clause1.
* Therefore, options A, B, and C are correct.
References: : COPY INTO <table>
NEW QUESTION # 174
What transformations are supported in the below SQL statement? (Select THREE).
CREATE PIPE ... AS COPY ... FROM (...)
Answer: C,E,F
Explanation:
* The SQL statement is a command for creating a pipe in Snowflake, which is an object that defines the COPY INTO <table> statement used by Snowpipe to load data from an ingestion queue into tables1. The statement uses a subquery in the FROM clause to transform the data from the staged files before loading it into the table2.
* The transformations supported in the subquery are as follows2:
* Data can be filtered by an optional WHERE clause, which specifies a condition that must be satisfied by the rows returned by the subquery. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable
from(
select*from@mystage
wherecol1='A'andcol2>10
);
* Columns can be reordered, which means changing the order of the columns in the subquery to match the order of the columns in the target table. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable (col1, col2, col3)
from(
selectcol3, col1, col2from@mystage
);
* Columns can be omitted, which means excluding some columns from the subquery that are not needed in the target table. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable (col1, col2)
from(
selectcol1, col2from@mystage
);
* The other options are not supported in the subquery because2:
* Type casts are not supported, which means changing the data type of a column in the subquery.
For example, the following statement will cause an error:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable (col1, col2)
from(
selectcol1::date, col2from@mystage
);
* Incoming data can not be joined with other tables, which means combining the data from the staged files with the data from another table in the subquery. For example, the following statement will cause an error:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable (col1, col2, col3)
from(
selects.col1, s.col2, t.col3from@mystages
joinothertable tons.col1=t.col1
);
* The ON ERROR - ABORT statement command can not be used, which means aborting the entire load operation if any error occurs. This command can only be used in the COPY INTO <table> statement, not in the subquery. For example, the following statement will cause an error:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable
from(
select*from@mystage
onerror abort
);
1: CREATE PIPE | Snowflake Documentation
2: Transforming Data During a Load | Snowflake Documentation
NEW QUESTION # 175
Refreshing a secondary database is not allowed in the following circumstances
Answer: A,B
NEW QUESTION # 176
......
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