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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Architect Certification Exam |
| Exam Number: | ARA-C01 |
| Exam Price: | $375 USD |
| Passing Score: | 750 (scaled score out of 1000) |
| Exam Format: | Multiple choice, Multiple select |
| Exam Duration: | 115 minutes |
| Related Certifications: | SnowPro Core Certification |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | Approximately 65 |
| Available Languages: | English |
| Recommended Training: | Snowflake Training & Certification Learning Path |
| Exam Registration: | Snowflake Certification Portal |
| Sample Questions: | Snowflake ARA-C01 Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | SnowPro Core Certification is strongly recommended and often expected before attempting the Advanced Architect exam |
| Official Syllabus URL: | https://www.snowflake.com/certification/ |
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Snowflake ARA-C01 Exam is a challenging exam that requires a significant amount of preparation and study. Candidates should have experience working with Snowflake and should be familiar with Snowflake best practices and implementation strategies. ARA-C01 exam consists of multiple-choice questions and scenario-based questions that test the candidate's ability to design and implement Snowflake solutions. Candidates who pass the exam will earn the SnowPro Advanced Architect certification, which is a highly respected certification in the data warehousing industry.
NEW QUESTION # 155
A retail company has over 3000 stores all using the same Point of Sale (POS) system. The company wants to deliver near real-time sales results to category managers. The stores operate in a variety of time zones and exhibit a dynamic range of transactions each minute, with some stores having higher sales volumes than others.
Sales results are provided in a uniform fashion using data engineered fields that will be calculated in a complex data pipeline. Calculations include exceptions, aggregations, and scoring using external functions interfaced to scoring algorithms. The source data for aggregations has over 100M rows.
Every minute, the POS sends all sales transactions files to a cloud storage location with a naming convention that includes store numbers and timestamps to identify the set of transactions contained in the files. The files are typically less than 10MB in size.
How can the near real-time results be provided to the category managers? (Select TWO).
Answer: A,D
Explanation:
To provide near real-time sales results to category managers, the Architect can use the following steps:
Create an external stage that references the cloud storage location where the POS sends the sales transactions files. The external stage should use the file format and encryption settings that match the source files2 Create a Snowpipe that loads the files from the external stage into a target table in Snowflake. The Snowpipe should be configured with AUTO_INGEST = true, which means that it will automatically detect and ingest new files as they arrive in the external stage. The Snowpipe should also use a copy option to purge the files from the external stage after loading, to avoid duplicate ingestion3 Create a stream on the target table that captures the INSERTS made by the Snowpipe. The stream should include the metadata columns that provide information about the file name, path, size, and last modified time. The stream should also have a retention period that matches the real-time analytics needs4 Create a task that runs a query on the stream to process the near real-time data. The query should use the stream metadata to extract the store number and timestamps from the file name and path, and perform the calculations for exceptions, aggregations, and scoring using external functions. The query should also output the results to another table or view that can be accessed by the category managers. The task should be scheduled to run at a frequency that matches the real-time analytics needs, such as every minute or every 5 minutes.
The other options are not optimal or feasible for providing near real-time results:
All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion. This option is not recommended because it would introduce additional latency and complexity in the data pipeline.
Concatenating files would require an external process or service that monitors the cloud storage location and performs the file merging operation. This would delay the ingestion of new files into Snowflake and increase the risk of data loss or corruption. Moreover, concatenating files would not avoid micro-ingestion, as Snowpipe would still ingest each concatenated file as a separate load.
An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs. This option is not necessary because Snowpipe can automatically ingest new files from the external stage without requiring an external trigger or scheduler. Using an external scheduler would add more overhead and dependency to the data pipeline, and it would not guarantee near real-time ingestion, as it would depend on the polling interval and the availability of the external scheduler.
The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement. This option is not feasible because tasks cannot be scheduled to run every second in Snowflake. The minimum interval for tasks is one minute, and even that is not guaranteed, as tasks are subject to scheduling delays and concurrency limits. Moreover, using the copy into command with a task would not leverage the benefits of Snowpipe, such as automatic file detection, load balancing, and micro-partition optimization. References:
1: SnowPro Advanced: Architect | Study Guide
2: Snowflake Documentation | Creating Stages
3: Snowflake Documentation | Loading Data Using Snowpipe
4: Snowflake Documentation | Using Streams and Tasks for ELT
Snowflake Documentation | Creating Tasks
Snowflake Documentation | Best Practices for Loading Data
Snowflake Documentation | Using the Snowpipe REST API
Snowflake Documentation | Scheduling Tasks
SnowPro Advanced: Architect | Study Guide
Creating Stages
Loading Data Using Snowpipe
Using Streams and Tasks for ELT
[Creating Tasks]
[Best Practices for Loading Data]
[Using the Snowpipe REST API]
[Scheduling Tasks]
NEW QUESTION # 156
Assigning ACCOUNTADMIN as the default role for the account administrators is a best practice
Answer: A
NEW QUESTION # 157
Company A would like to share data in Snowflake with Company B. Company B is not on the same cloud platform as Company A.
What is required to allow data sharing between these two companies?
Answer: B
Explanation:
Explanation
According to the SnowPro Advanced: Architect documents and learning resources, the requirement to allow data sharing between two companies that are not on the same cloud platform is to set up data replication to the region and cloud platform where the consumer resides. Data replication is a feature of Snowflake that enables copying databases across accounts in different regions and cloud platforms. Data replication allows data providers to securely share data with data consumers across different regions and cloud platforms by creating a replica database in the consumer's account. The replica database is read-only and automatically synchronized with the primary database in the provider's account. Data replication is useful for scenarios where data sharing is not possible or desirable due to latency, compliance, or security reasons1. The other options are incorrect because they are not required or feasible to allow data sharing between two companies that are not on the same cloud platform. Option A is incorrect because creating a pipeline to write shared data to a cloud storage location in the target cloud provider is not a secure or efficient way of sharing data. It would require additional steps to load the data from the cloud storage to the consumer's account, and it would not leverage the benefits of Snowflake's data sharing features. Option B is incorrect because ensuring that all views are persisted is not relevant for data sharing across cloud platforms. Views can be shared across cloud platforms as long as they reference objects in the same database. Persisting views is an option to improve the performance of querying views, but it is not required for data sharing2. Option D is incorrect because Company A and Company B do not need to agree to use a single cloud platform. Data sharing is possible across different cloud platforms using data replication or other methods, such as listings or auto-fulfillment3. References: ReplicatingDatabases Across Multiple Accounts | Snowflake Documentation, Persisting Views | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation
NEW QUESTION # 158
A company has a Snowflake account named ACCOUNTA in AWS us-east-1 region. The company stores its marketing data in a Snowflake database named MARKET_DB. One of the company's business partners has an account named PARTNERB in Azure East US 2 region. For marketing purposes the company has agreed to share the database MARKET_DB with the partner account.
Which of the following steps MUST be performed for the account PARTNERB to consume data from the MARKET_DB database?
Answer: B
NEW QUESTION # 159
What step will improve the performance of queries executed against an external table?
Answer: B
NEW QUESTION # 160
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