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| Section | Objectives |
|---|---|
| Topic 1: Performance and Optimization | - Cost optimization
|
| Topic 2: Data Architecture and Design | - Data modeling and schema design
|
| Topic 3: Data Sharing and Collaboration | - Secure data sharing
|
| Topic 4: Security, Governance, and Compliance | - Data governance
|
| Topic 5: Data Pipelines and Data Engineering | - Data ingestion and transformation
|
>> ARA-C01 Certification Questions <<
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NEW QUESTION # 107 
Based on the architecture in the image, how can the data from DB1 be copied into TBL2? (Select TWO).
Answer: B,D
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;
createstage 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.
copyintoTBL2
from@EXT_STAGE1
file format=(format name=DB1.SH1.FF PIPE1);
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;
insertintoTBL2
select*fromDB1.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.
1: CREATE STAGE | Snowflake Documentation
2: COPY INTO table | Snowflake Documentation
3: Cross-database Queries | Snowflake Documentation
NEW QUESTION # 108
Which of the below commands will use warehouse credits?
Answer: A,B,C
Explanation:
* Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake.
A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.
* Among the commands listed in the question, the following ones will use warehouse credits:
* SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.
* SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows in the table3. Therefore, option C is correct.
* SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.
* The command that will not use warehouse credits is:
* SHOW TABLES LIKE 'SNOWFL%': This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern 'SNOWFL%' in the current database and schema5. Therefore, option A is incorrect.
References: : Understanding Compute Cost : MAX Function : COUNT Function : GROUP BY Clause : SHOW TABLES
NEW QUESTION # 109
A company has an external vendor who puts data into Google Cloud Storage. The company's Snowflake account is set up in Azure.
What would be the MOST efficient way to load data from the vendor into Snowflake?
Answer: D
Explanation:
The most efficient way to load data from the vendor into Snowflake is to create an external stage on Google Cloud Storage and use the external table to load the data into Snowflake (Option B). This way, you can avoid copying or moving the data across different cloud platforms, which can incur additional costs and latency. You can also leverage the external table feature to query the data directly from Google Cloud Storage without loading it into Snowflake tables, which can save storage space and improve performance. Option A is not efficient because it requires the vendor to create a Snowflake account and a data share, which can be complicated and costly. Option C is not efficient because it involves copying the data from Google Cloud Storage to Azure Blob storage using external tools, which can be slow and expensive. Option D is not efficient because it requires creating a Snowflake account in the Google Cloud Platform (GCP), ingesting data into this account, and using data replication to move the data from GCP to Azure, which can be complex and time-consuming. Reference: The answer can be verified from Snowflake's official documentation on external stages and external tables available on their website. Here are some relevant links:
Using External Stages | Snowflake Documentation
Using External Tables | Snowflake Documentation
Loading Data from a Stage | Snowflake Documentation
NEW QUESTION # 110
How can the Snowpipe REST API be used to keep a log of data load history?
Answer: B
NEW QUESTION # 111
A call center processes sensitive PII and PHI data. Requirements:
* Object owners and privileged roles must see sensitive columns.
* Pre-tokenized data must be de-tokenized at query time.What should be used? (Select TWO).
Answer: D,E
Explanation:
Dynamic Data Masking is a Snowflake security feature that conditionally masks or reveals column values at query time based on the executing role (Answer D). This allows privileged users (such as object owners or compliance roles) to see clear-text PII/PHI, while other users see masked or tokenized values. Because masking is evaluated at query time, it supports de-tokenization logic when the role is authorized.
Role-Based Access Control (RBAC) underpins this behavior by defining which roles are privileged and allowed to view unmasked data (Answer E). Masking policies are evaluated in the context of the active role, making RBAC essential for enforcing correct access.
Object tagging alone does not enforce access control. Materialized views duplicate data and do not support dynamic de-tokenization logic. External tokenization is not required when Snowflake masking policies can handle conditional reveal. This question tests SnowPro Architect knowledge of advanced column-level security and privacy controls.
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NEW QUESTION # 112
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