RELIABLE ARA-C01 DUMPS FREE, VALID ARA-C01 TEST REGISTRATION

Reliable ARA-C01 Dumps Free, Valid ARA-C01 Test Registration

Reliable ARA-C01 Dumps Free, Valid ARA-C01 Test Registration

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q21-Q26):

NEW QUESTION # 21
What step will im the performance of queries executed against an external table?

  • A. Shorten the names of the source files.
  • B. Partition the external table.
  • 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: B

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 # 22
create or replace table result_scan_table as
select * from table(result_scan(last_query_id()));
Will the above query cost you any compute credits?

  • A. It will not cost any compute credit because this query is a metadata operation
  • B. It will cost compute credit because processing is required to insert the data into result_scan_table
  • C. It will depend on the warehouse chosen

Answer: B


NEW QUESTION # 23
Consider the following COPY command which is loading data with CSV format into a Snowflake table from an internal stage through a data transformation query.

This command results in the following error:
SQL compilation error: invalid parameter 'validation_mode'
Assuming the syntax is correct, what is the cause of this error?

  • A. The VALIDATION_MODE parameter does not support COPY statements that transform data during a load.
  • B. The VALIDATION_MODE parameter supports COPY statements that load data from external stages only.
  • C. The VALIDATION_MODE parameter does not support COPY statements with CSV file formats.
  • D. The value return_all_errors of the option VALIDATION_MODE is causing a compilation error.

Answer: A

Explanation:
* The VALIDATION_MODE parameter is used to specify the behavior of the COPY statement when loading data into a table. It is used to specify whether the COPY statement should return an error if any of the rows in the file are invalid or if it should continue loading the valid rows. The VALIDATION_MODE parameter is only supported for COPY statements that load data from external stages1.
* The query in the question uses a data transformation query to load data from an internal stage. A data transformation query is a query that transforms the data during the load process, such as parsing JSON or XML data, applying functions, or joining with other tables2.
* According to the documentation, VALIDATION_MODE does not support COPY statements that transform data during a load. If the parameter is specified, the COPY statement returns an error1.
Therefore, option C is the correct answer.
References: : COPY INTO <table> : Transforming Data During a Load


NEW QUESTION # 24
What considerations need to be taken when using database cloning as a tool for data lifecycle management in a development environment? (Select TWO).

  • A. The clone inherits all granted privileges of all child objects in the source object, excluding the database.
  • B. The clone inherits all granted privileges of all child objects in the source object, including the database.
  • C. Any pipes in the source are not cloned.
  • D. Any pipes in the source referring to internal stages are not cloned.
  • E. Any pipes in the source referring to external stages are not cloned.

Answer: C,E


NEW QUESTION # 25
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).

  • A. 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.
  • B. A Snowpipe should be created and configured with AUTO_INGEST = true. A stream should be created to process INSERTS into a single target table using the stream metadata to inform the store number and timestamps.
  • C. A stream should be created to accumulate the near real-time data and a task should be created that runs at a frequency that matches the real-time analytics needs.
  • D. All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion.
  • E. The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement.

Answer: B,E


NEW QUESTION # 26
......

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