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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Architect Certification |
| Exam Number: | ARA-C01 |
| Exam Format: | Multiple Choice, Multiple Select |
| Real Exam Qty: | 65 |
| Exam Duration: | 115 minutes |
| Exam Price: | $375 USD |
| Certificate Validity Period: | 2 years |
| Available Languages: | Japanese, English |
| Related Certifications: | SnowPro Core Certification |
| Passing Score: | 750/1000 |
| Sample Questions: | Snowflake ARA-C01 Sample Questions |
| Exam Way: | Online proctored or test center delivery through Pearson VUE. |
| Pre Condition: | Candidates must hold an active SnowPro Core Certification. Snowflake recommends 2+ years of hands-on Snowflake architecture experience in production environments. |
| Official Syllabus URL: | https://learn.snowflake.com/en/certifications/snowpro-advanced-architect/ |
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Snowflake ARA-C01 certification is highly respected in the industry and is recognized by many organizations as a standard of excellence in Snowflake architecture and implementation. Achieving this certification demonstrates an individual's commitment to their profession and their ability to provide high-quality and effective solutions to complex business problems. It also provides a competitive advantage in the job market, as many organizations look for certified Snowflake professionals to lead their data management initiatives.
Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a globally recognized certification program that validates an individual's expertise in designing and implementing Snowflake solutions. SnowPro Advanced Architect Certification certification is designed for experienced architects who have already achieved the SnowPro Core Certification and have a deep understanding of Snowflake's data warehousing platform. The Snowflake ARA-C01 Exam covers a wide range of advanced topics, including Snowflake architecture, query optimization, data modeling, security, and performance tuning.
NEW QUESTION # 207
To increase performance, materialized views can be created on external table without any additional cost
Answer: A
NEW QUESTION # 208
Which query will identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse to improve the performance of a particular workload?
Answer: C
Explanation:
The correct answer is option B. This query is designed to assess the need for a multi-cluster warehouse by examining the queuing time (AVG_QUEUED_LOAD) on different days and virtual warehouses. When the AVG_QUEUED_LOAD is greater than zero, it suggests that queries are waiting for resources, which can be an indicator that performance might be improved by using a multi-cluster warehouse to handle the workload more efficiently. By grouping by date and warehouse name and filtering on the sum of the average queued load being greater than zero, the query identifies specific days and warehouses where the workload exceeded the available compute resources. This information is valuable when considering scaling out warehouses to multi-cluster configurations for improved performance.
NEW QUESTION # 209
An Architect is troubleshooting a query with poor performance using the QUERY_HIST0RY function. The Architect observes that the COMPILATIONJHME is greater than the EXECUTIONJTIME.
What is the reason for this?
Answer: A
Explanation:
Compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. It also involves some pruning of partition files, making the query execution efficient2 If the compilation time is greater than the execution time, it means that the optimizer spent more time analyzing the query than actually running it. This could indicate that the query has overly complex logic, such as multiple joins, subqueries, aggregations, or expressions. The complexity of the query could also affect the size and quality of the query plan, which could impact the performance of the query3 To reduce the compilation time, the Architect can try to simplify the query logic, use views or common table expressions (CTEs) to break down the query into smaller parts, or use hints to guide the optimizer. The Architect can also use the EXPLAIN command to examine the query plan and identify potential bottlenecks or inefficiencies4 References:
* 1: SnowPro Advanced: Architect | Study Guide 5
* 2: Snowflake Documentation | Query Profile Overview 6
* 3: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time 7
* 4: Snowflake Documentation | Optimizing Query Performance 8
* : SnowPro Advanced: Architect | Study Guide
* : Query Profile Overview
* : Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time
* : Optimizing Query Performance
NEW QUESTION # 210
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: B,C
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 # 211
Assuming all Snowflake accounts are using an Enterprise edition or higher, in which development and testing scenarios would be copying of data be required, and zero-copy cloning not be suitable? (Select TWO).
Answer: B,E
NEW QUESTION # 212
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