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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is designed to test the ability of experienced Snowflake architects to design and implement complex Snowflake solutions. SnowPro Advanced Architect Certification certification exam is intended for professionals who have extensive experience in architecting Snowflake solutions and want to demonstrate their expertise and proficiency. ARA-C01 exam assesses the candidate's ability to design, plan, and implement Snowflake solutions in a variety of scenarios.
To take the Snowflake ARA-C01 Exam, candidates must have previously passed the SnowPro Core Certification and have significant experience working with Snowflake's cloud data warehouse platform. ARA-C01 exam consists of 60 multiple-choice questions and must be completed within 90 minutes. A passing score of 80% or higher is required to earn the SnowPro Advanced Architect Certification, which is recognized by organizations worldwide as a validation of advanced skill and knowledge in Snowflake data warehousing.
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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a rigorous certification exam that is designed to test an individual's proficiency in the technical aspects of the Snowflake platform. SnowPro Advanced Architect Certification certification exam is targeted at experienced architects and engineers who are looking to validate their knowledge and skills in designing and implementing advanced Snowflake solutions. The Snowflake ARA-C01 Exam covers a wide range of topics, including Snowflake architecture, security, performance tuning, data warehousing, and data integration.
NEW QUESTION # 76
What are characteristics of Dynamic Data Masking? (Select TWO).
Answer: C,E
Explanation:
Dynamic Data Masking is a feature that allows masking sensitive data in query results based on the role of the user who executes the query. A masking policy is a user-defined function that specifies the masking logic and can be applied to one or more columns in one or more tables. A masking policy that is currently set on a table can be dropped using the ALTER TABLE command. A single masking policy can be applied to columns in different tables using the ALTER TABLE command with the SET MASKING POLICY clause. The other options are either incorrect or not supported by Snowflake. A masking policy cannot be applied to the value column of an external table, as external tables do not support column-level security. The role that creates the masking policy will not always see unmasked data in query results, as the masking policy can be applied to the owner role as well. A masking policy cannot be applied to a column with the GEOGRAPHY data type, as Snowflake only supports masking policies for scalar data types. References: Snowflake Documentation:
Dynamic Data Masking, Snowflake Documentation: ALTER TABLE
NEW QUESTION # 77
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: A
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. References: 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 # 78
A company is storing large numbers of small JSON files (ranging from 1-4 bytes) that are received from IoT devices and sent to a cloud provider. In any given hour, 100,000 files are added to the cloud provider.
What is the MOST cost-effective way to bring this data into a Snowflake table?
Answer: A
Explanation:
A pipe is a Snowflake object that continuously loads data from files in a stage (internal or external) into a table. A pipe can be configured to use auto-ingest, which means that Snowflake automatically detects new or modified files in the stage and loads them into the table without any manual intervention1.
A pipe is the most cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it minimizes the number of COPY commands executed and the number of micro-partitions created. A pipe can use file aggregation, which means that it can combine multiple small files into a single larger file before loading them into the table. This reduces the load time and the storage cost of the data2.
An external table is a Snowflake object that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table does not store the data in Snowflake, but only provides a view of the data for querying. An external table is not a cost-effective way to bring data into a Snowflake table, because it does not support file aggregation, and it requires additional network bandwidth and compute resources to query the external data3.
A stream is a Snowflake object that records the history of changes (inserts, updates, and deletes) made to a table. A stream can be used to consume the changes from a table and apply them to another table or a task. A stream is not a way to bring data into a Snowflake table, but a way to process the data after it is loaded into a table4.
A copy command is a Snowflake command that loads data from files in a stage into a table. A copy command can be executed manually or scheduled using a task. A copy command is not a cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it does not support file aggregation, and it may create many micro-partitions that increase the storage cost of the data5.
NEW QUESTION # 79
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: D
NEW QUESTION # 80
You have a table named JSON_TBL which has a variant column JSON_VAR. The json stored in that table looks as below
{
"COURSE_DESC": "SNOWFLAKE CERTIFICATION",
"COURSE_ID": 1000,
"DURATION": 2
}
if you run a query SELECT JSON_VAR:Course_id FROM JSON_TBL; what will it return
Answer: C
NEW QUESTION # 81
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