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[General] New ARA-C01 Test Topics, ARA-C01 Latest Test Cost

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【General】 New ARA-C01 Test Topics, ARA-C01 Latest Test Cost

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To be eligible to take the SnowPro Advanced Architect Certification exam, candidates must have a solid understanding of Snowflake data warehousing and data management concepts, as well as practical experience in designing and implementing Snowflake solutions. Candidates are also required to have completed the SnowPro Core Certification exam, which tests their foundational knowledge of Snowflake data warehousing and data management.
Snowflake SnowPro Advanced Architect Certification Sample Questions (Q157-Q162):NEW QUESTION # 157
If a multi-cluster warehouse is resized, the new size applies to
  • A. Clusters that are currently running
  • B. Clusters that are started after the warehouse is resized
  • C. All of the above
Answer: C

NEW QUESTION # 158
An Architect needs to design a data unloading strategy for Snowflake, that will be used with the COPY INTO
<location> command.
Which configuration is valid?
  • A. Location of files: Google Cloud Storage
    . File formats: Parquet
    . File encoding: UTF-8
      Compression: gzip
  • B. Location of files: Azure ADLS
    . File formats: JSON, XML, Avro, Parquet, ORC
    . Compression: bzip2
    . Encryption: User-supplied key
  • C. Location of files: Amazon S3
    . File formats: CSV, JSON
    . File encoding: Latin-1 (ISO-8859)
    . Encryption: 128-bit
  • D. Location of files: Snowflake internal location
    . File formats: CSV, XML
    . File encoding: UTF-8
    . Encryption: 128-bit
Answer: A
Explanation:
For the configuration of data unloading in Snowflake, the valid option among the provided choices is "C." This is because Snowflake supports unloading data into Google Cloud Storage using the COPY INTO
<location> command with specific configurations. The configurations listed in option C, such as Parquet file format with UTF-8 encoding and gzip compression, are all supported by Snowflake. Notably, Parquet is a columnar storage file format, which is optimal for high-performance data processing tasks in Snowflake. The UTF-8 file encoding and gzip compression are both standard and widely used settings that are compatible with Snowflake's capabilities for data unloading to cloud storage platforms.References:
* Snowflake Documentation on COPY INTO command
* Snowflake Documentation on Supported File Formats
* Snowflake Documentation on Compression and Encoding Options

NEW QUESTION # 159
A table, EMP_ TBL has three records as shown:

The following variables are set for the session:

Which SELECT statements will retrieve all three records? (Select TWO).
  • A. SELECT * FROM identifier<Stbl_ref> WHERE NAME IN ($var1, $var2, $var3);
  • B. SELECT * FROM $tb1_ref WHERE $col_ref IN ($var1, Svar2, Svar3);
  • C. SELECT * FROM identifier($tbl_ref) WHERE ID IN Cvarl','var2','var3');
  • D. SELECT * FROM EMP_TBL WHERE identifier(Scol_ref) IN ('Namel','Name2', 'Name3');
  • E. Select * FROM Stbl_ref WHERE Scol_ref IN ('Name1','Nam2','Name3');
Answer: B,D
Explanation:
* The correct answer is B and E because they use the correct syntax and values for the identifier function and the session variables.
* The identifier function allows you to use a variable or expression as an identifier (such as a table name or column name) in a SQL statement. It takes a single argument and returns it as an identifier. For example, identifier($tbl_ref) returns EMP_TBL as an identifier.
* The session variables are set using the SET command and can be referenced using the $ sign. For example, $var1 returns Name1 as a value.
* Option A is incorrect because it uses Stbl_ref and Scol_ref, which are not valid session variables or identifiers. They should be $tbl_ref and $col_ref instead.
* Option C is incorrect because it uses identifier<Stbl_ref>, which is not a valid syntax for the identifier function. It should be identifier($tbl_ref) instead.
* Option D is incorrect because it uses Cvarl, var2, and var3, which are not valid session variables or values. They should be $var1, $var2, and $var3 instead. References:
* Snowflake Documentation: Identifier Function
* Snowflake Documentation: Session Variables
* Snowflake Learning: SnowPro Advanced: Architect Exam Study Guide

NEW QUESTION # 160
When using the Snowflake Connector for Kafka, what data formats are supported for the messages? (Choose two.)
  • A. XML
  • B. Parquet
  • C. Avro
  • D. CSV
  • E. JSON
Answer: C,E
Explanation:
Explanation
The data formats that are supported for the messages when using the Snowflake Connector for Kafka are Avro and JSON. These are the two formats that the connector can parse and convert into Snowflake table rows. The connector supports both schemaless and schematized JSON, as well as Avro with or without a schema registry1. The other options are incorrect because they are not supported data formats for the messages. CSV, XML, and Parquet are not formats that the connector can parse and convert into Snowflake table rows. If the messages are in these formats, the connector will load them as VARIANT data type and store them as raw strings in the table2. References: Snowflake Connector for Kafka | Snowflake Documentation, Loading Protobuf Data using the Snowflake Connector for Kafka | Snowflake Documentation

NEW QUESTION # 161
While choosing a cluster key, what is recommended by snowflake?
  • A. Choose a key with high cardinality
  • B. If there is room for additional cluster keys, then consider columns frequently used in join predicates
  • C. Cluster columns that are most actively used in selective filters
Answer: B,C

NEW QUESTION # 162
......
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