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[Hardware] Verified Snowflake Valid ARA-C01 Cram Materials Strictly Researched by Snowflake

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【Hardware】 Verified Snowflake Valid ARA-C01 Cram Materials Strictly Researched by Snowflake

Posted at 1/8/2026 16:30:17      View：116 | Replies：5        Print      Only Author   [Copy Link] 1#
P.S. Free 2026 Snowflake ARA-C01 dumps are available on Google Drive shared by Pass4cram: https://drive.google.com/open?id=15i6gdWTSq-sUk-kZbbLLASwiwC2niVlK
ARA-C01 study material applies to all types of candidates. Buying a set of learning materials is not difficult, but it is difficult to buy one that is suitable for you. For example, some learning materials can really help students get high scores, but they usually require users to have a lot of study time, which is difficult for office workers. However, ARA-C01 Study Material is to help students improve their test scores by improving their learning efficiency. Therefore, users can pass exams with very little learning time.
Snowflake ARA-C01 certification exam is not for the faint-hearted. It is a rigorous and challenging exam that requires a deep understanding of Snowflake architecture, data modeling, performance optimization, security, and administration. ARA-C01 Exam consists of 60 multiple-choice questions that must be completed within 120 minutes. The passing score for the ARA-C01 exam is 80%, and candidates who pass the exam are awarded the SnowPro Advanced Architect Certification.
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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q40-Q45):NEW QUESTION # 40
A user needs access to create materialized view on a shema mydb.myschema.
What is the appropriate command to provide the access?
  • A. GRANT CREATE MATERIALIZED VIEW ON SCHEMA MYDB.MYSCHEMA TO USER USER1;
  • B. GRANT CREATE MATERIALIZED VIEW ON SCHEMA MYDB.MYSCHEMA TO USER1;
  • C. GRANT ROLE MYROLE TO USER USER1; GRANT CREATE MATERIALIZED VIEW ON SCHEMA MYDB.MYSCHEMA TO MYROLE;
Answer: C

NEW QUESTION # 41
How can an Architect enable optimal clustering to enhance performance for different access paths on a given table?
  • A. Create super projections that will automatically create clustering.
  • B. Create a clustering key that contains all columns used in the access paths.
  • C. Create multiple materialized views with different cluster keys.
  • D. Create multiple clustering keys for a table.
Answer: C
Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, the best way to enable optimal clustering to enhance performance for different access paths on a given table is to create multiple materialized views with different cluster keys. A materialized view is a pre-computed result set that is derived from a query on one or more base tables. A materialized view can be clustered by specifying a clustering key, which is a subset of columns or expressions that determines how the data in the materialized view is co- located in micro-partitions. By creating multiple materialized views with different cluster keys, an Architect can optimize the performance of queries that use different access paths on the same base table. For example, if a base table has columns A, B, C, and D, and there are queries that filter on A and B, or on C and D, or on A and C, the Architect can create three materialized views, each with a different cluster key: (A, B), (C, D), and (A, C). This way, each query can leverage the optimal clustering of the corresponding materialized view and achieve faster scan efficiency and better compression.
Snowflake Documentation: Materialized Views
Snowflake Learning: Materialized Views
https://www.snowflake.com/blog/u ... rformance-problems/

NEW QUESTION # 42
An Architect is designing a pipeline to stream event data into Snowflake using the Snowflake Kafka connector. The Architect's highest priority is to configure the connector to stream data in the MOST cost-effective manner.
Which of the following is recommended for optimizing the cost associated with the Snowflake Kafka connector?
  • A. Utilize a higher Buffer.size.bytes in the connector configuration.
  • B. Utilize a higher Buffer.flush.time in the connector configuration.
  • C. Utilize a lower Buffer.count.records in the connector configuration.
  • D. Utilize a lower Buffer.size.bytes in the connector configuration.
Answer: B
Explanation:
The minimum value supported for the buffer.flush.time property is 1 (in seconds). For higher average data flow rates, we suggest that you decrease the default value for improved latency. If cost is a greater concern than latency, you could increase the buffer flush time. Be careful to flush the Kafka memory buffer before it becomes full to avoid out of memory exceptions. https://docs.snowflake.com/en/us ... ipe-streaming-kafka

NEW QUESTION # 43
Data sharing is supported only between provider and consumer accounts in same region
  • A. TRUE
  • B. FALSE
Answer: B

NEW QUESTION # 44
A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes.
Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation. The external vendor is a Snowflake customer.
What solution will MINIMIZE complexity and MAXIMIZE performance?
  • A. 1. Create a Snowpipe to bring the JSON data into Snowflake.
    2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
    3. Ask the vendor to create a data share with the required data that is then imported into the Snowflake account.
    4. Join the vendor's data back to the loT data in a transformation procedure
    5. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
    6. Give the materialized views access to the dashboard tool.
  • B. 1. Create an external table over the JSON data in cloud storage.
    2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp.
    3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure.
    4. Give the transformed table access to the dashboard tool.
    5. Perform the aggregations on the dashboard tool.
  • C. 1. Create an external table over the JSON data in cloud storage.
    2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp.
    3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account.
    4. Join the vendor's data back to the loT data using a transformation procedure.
    5. Create views over the larger dataset to perform the aggregations required by the dashboard.
    6. Give the views access to the dashboard tool.
  • D. 1. Create a Snowpipe to bring the JSON data into Snowflake.
    2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
    3. Ask the vendor to expose an API so an external function call can be made to join the vendor's data back to the loT data in a transformation procedure.
    4. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
    5. Give the materialized views access to the dashboard tool.
Answer: A
Explanation:
Using Snowpipe for continuous, automated data ingestion minimizes the need for manual intervention and ensures that data is available in Snowflake promptly after it is generated. Leveraging Snowflake's data sharing capabilities allows for efficient and secure access to the vendor's data without the need for complex API integrations. Materialized views provide pre-aggregated data for fast access, which is ideal for dashboards that require high performance1234.
Reference =
*         Snowflake Documentation on Snowpipe4
*         Snowflake Documentation on Secure Data Sharing2
*         Best Practices for Data Ingestion with Snowflake1

NEW QUESTION # 45
......
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