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Snowflake ARA-C01 Examcollection Questions Answers & ARA-C01 Valid Braindumps

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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is an industry-recognized certification that is designed to validate the advanced technical skills of Snowflake architects. SnowPro Advanced Architect Certification certification is intended for architects with extensive experience in designing Snowflake solutions and is a demonstration of their expertise in designing and implementing complex Snowflake-based solutions. SnowPro Advanced Architect Certification certification is highly sought after by organizations that utilize Snowflake as their data warehousing and analytics platform.

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Snowflake ARA-C01: SnowPro Advanced Architect Certification is a highly sought-after certification exam that tests the advanced architectural knowledge of Snowflake Data Cloud architects. ARA-C01 Exam is designed for individuals who have a deep understanding of Snowflake's architecture, best practices, and design principles. SnowPro Advanced Architect Certification certification is a testament to the candidate's expertise in designing and implementing complex Snowflake architectures that meet the needs of modern data-driven organizations.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q30-Q35):

NEW QUESTION # 30
Loading data using snowpipe REST API is supported for external stage only

A. FALSE
B. TRUE

Answer: A

NEW QUESTION # 31
A new table and streams are created with the following commands:
CREATE OR REPLACE TABLE LETTERS (ID INT, LETTER STRING) ;
CREATE OR REPLACE STREAM STREAM_1 ON TABLE LETTERS;
CREATE OR REPLACE STREAM STREAM_2 ON TABLE LETTERS APPEND_ONLY = TRUE;
The following operations are processed on the newly created table:
INSERT INTO LETTERS VALUES (1, 'A');
INSERT INTO LETTERS VALUES (2, 'B');
INSERT INTO LETTERS VALUES (3, 'C');
TRUNCATE TABLE LETTERS;
INSERT INTO LETTERS VALUES (4, 'D');
INSERT INTO LETTERS VALUES (5, 'E');
INSERT INTO LETTERS VALUES (6, 'F');
DELETE FROM LETTERS WHERE ID = 6;
What would be the output of the following SQL commands, in order?
SELECT COUNT (*) FROM STREAM_1;
SELECT COUNT (*) FROM STREAM_2;

A. 2 & 6
B. 4 & 3
C. 2 & 3
D. 4 & 6

Answer: B

Explanation:
In Snowflake, a stream records data manipulation language (DML) changes to its base table since the stream was created or last consumed. STREAM_1 will show all changes including the TRUNCATE operation, while STREAM_2, being APPEND_ONLY, will not show deletions like TRUNCATE. Therefore, STREAM_1 will count the three inserts, the TRUNCATE (counted as a single operation), and the subsequent two inserts before the delete, totaling 4. STREAM_2 will only count the three initial inserts and the two after the TRUNCATE, totaling 3, as it does not count the TRUNCATE or the delete operation.
References: The explanation is based on the Snowflake documentation on streams, which details how streams track changes and the difference between standard and APPEND_ONLY streams12.

NEW QUESTION # 32
What step will im the performance of queries executed against an external table?

A. Partition the external table.
B. Shorten the names of the source files.
C. Convert the source files' character encoding to UTF-8.
D. Use an internal stage instead of an external stage to store the source files.

Answer: A

Explanation:
Partitioning an external table is a technique that improves the performance of queries executed against the table by reducing the amount of data scanned. Partitioning an external table involves creating one or more partition columns that define how the table is logically divided into subsets of data based on the values in those columns. The partition columns can be derived from the file metadata (such as file name, path, size, or modification time) or from the file content (such as a column value or a JSON attribute). Partitioning an external table allows the query optimizer to prune the files that do not match the query predicates, thus avoiding unnecessary data scanning and processing2 The other options are not effective steps for improving the performance of queries executed against an external table:
* Shorten the names of the source files. This option does not have any impact on the query performance, as the file names are not used for query processing. The file names are only used for creating the external table and displaying the query results3
* Convert the source files' character encoding to UTF-8. This option does not affect the query performance, as Snowflake supports various character encodings for external table files, such as UTF-8, UTF-16, UTF-32, ISO-8859-1, and Windows-1252. Snowflake automatically detects the character encoding of the files and converts them to UTF-8 internally for query processing4
* Use an internal stage instead of an external stage to store the source files. This option is not applicable, as external tables can only reference files stored in external stages, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage. Internal stages are used for loading data into internal tables, not external tables5 References:
* 1: SnowPro Advanced: Architect | Study Guide
* 2: Snowflake Documentation | Partitioning External Tables
* 3: Snowflake Documentation | Creating External Tables
* 4: Snowflake Documentation | Supported File Formats and Compression for Staged Data Files
* 5: Snowflake Documentation | Overview of Stages
* : SnowPro Advanced: Architect | Study Guide
* : Partitioning External Tables
* : Creating External Tables
* : Supported File Formats and Compression for Staged Data Files
* : Overview of Stages

NEW QUESTION # 33
Which alter command below may affect the availability of column with respect to time travel?

A. ALTER TABLE...SET DATA TYPE
B. ALTER TABLE...DROP COLUMN
C. ALTER TABLE...SET DEFAULT

Answer: A

NEW QUESTION # 34
An Architect needs to improve the performance of reports that pull data from multiple Snowflake tables, join, and then aggregate the data. Users access the reports using several dashboards. There are performance issues on Monday mornings between 9:00am-11:00am when many users check the sales reports.
The size of the group has increased from 4 to 8 users. Waiting times to refresh the dashboards has increased significantly. Currently this workload is being served by a virtual warehouse with the following parameters:
AUTO-RESUME = TRUE AUTO_SUSPEND = 60 SIZE = Medium
What is the MOST cost-effective way to increase the availability of the reports?

A. Use a multi-cluster warehouse in maximized mode with 2 size Medium clusters.
B. Increase the warehouse to size Large and set auto_suspend = 600.
C. Use a multi-cluster warehouse in auto-scale mode with 1 size Medium cluster, and set min_cluster_count = 1 and max_cluster_count = 4.
D. Use materialized views and pre-calculate the data.

Answer: C

Explanation:
The most cost-effective way to increase the availability and performance of the reports during peak usage times, while keeping costs under control, is to use a multi-cluster warehouse in auto-scale mode. Option D suggests using a multi-cluster warehouse with 1 size Medium cluster and allowing it to auto-scale between 1 and 4 clusters based on demand. This setup ensures that additional computing resources are available when needed (e.g., during Monday morning peaks) and are scaled down to minimize costs when the demand decreases. This approach optimizes resource utilization and cost by adjusting the compute capacity dynamically, rather than maintaining a larger fixed size or multiple clusters continuously.
References:Snowflake's official documentation on managing warehouses and using auto-scaling features.

NEW QUESTION # 35
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