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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Exam is a professional certification exam offered by Snowflake for individuals who wish to demonstrate advanced knowledge and skills in designing and implementing Snowflake solutions. ARA-C01 Exam is the highest-level certification offered by Snowflake and is designed for architects, engineers, and consultants who work with Snowflake on a daily basis.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q71-Q76):

NEW QUESTION # 71
Which feature provides the capability to define an alternate cluster key for a table with an existing cluster key?

A. External table
B. Materialized view
C. Search optimization
D. Result cache

Answer: B

Explanation:
A materialized view is a feature that provides the capability to define an alternate cluster key for a table with an existing cluster key. A materialized view is a pre-computed result set that is stored in Snowflake and can be queried like a regular table. A materialized view can have a different cluster key than the base table, which can improve the performance and efficiency of queries on the materialized view. A materialized view can also support aggregations, joins, and filters on the base table data. A materialized view is automatically refreshed when the underlying data in the base table changes, as long as the AUTO_REFRESH parameter is set to true1.
References:
* Materialized Views | Snowflake Documentation

NEW QUESTION # 72
Which command below will only copy the table structure from the existing table to the new table?

A. CREATE TABLE ... LIKE
B. CREATE TABLE ... CLONE
C. CREATE TABLE ... AS SELECT

Answer: A

NEW QUESTION # 73
Cloud services can help in pruning even if the columns are variant columns.

A. FALSE
B. TRUE

Answer: B

NEW QUESTION # 74
A new user user_01 is created within Snowflake. The following two commands are executed:
Command 1# SHOW GRANTS TO USER user_01;
Command 2# SHOW GRANTS ON USER user_01;
What inferences can be made about these commands?

A. Command 1 defines all the grants which are given to user_01Command 2 defines which user owns user_01
B. Command 1 defines all the grants which are given to user_01Command 2 defines which role owns user_01
C. Command 1 defines which role owns user_01Command 2 defines all the grants which have been given to user_01
D. Command 1 defines which user owns user_01Command 2 defines all the grants which have been given to user_01

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To understand this, we must differentiate between:
* SHOW GRANTS TO <object>
* SHOW GRANTS ON <object>
1. SHOW GRANTS TO USER user_01;
This commandlists all the privileges granted to the useruser_01. These can include:
* Roles granted to the user
* Global privileges such as MONITOR, USAGE, etc.
#Official Documentation Extract:
"SHOW GRANTS TO USERdisplays the roles and other privileges that have been granted to the specified user." Source: Snowflake Docs - SHOW GRANTS TO

NEW QUESTION # 75
When using the copy into <table> command with the CSV file format, how does the match_by_column_name parameter behave?

A. The command will return an error.
B. The command will return a warning stating that the file has unmatched columns.
C. It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name.
D. The parameter will be ignored.

Answer: D

Explanation:
Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2.
Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.
Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets.
PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.
Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
References: 1: Snowpipe Overview 2: Using Streams and Tasks to Automate Data Pipelines 3: External Functions Overview 4: Snowflake Data Marketplace Overview : [Loading Data Using COPY INTO] : [What is Amazon EMR?] : [PySpark Overview]
* The copy into <table> command is used to load data from staged files into an existing table in Snowflake. The command supports various file formats, such as CSV, JSON, AVRO, ORC, PARQUET, and XML1.
* The match_by_column_name parameter is a copy option that enables loading semi-structured data into separate columns in the target table that match corresponding columns represented in the source data. The parameter can have one of the following values2:
* CASE_SENSITIVE: The column names in the source data must match the column names in the target table exactly, including the case. This is the default value.
* CASE_INSENSITIVE: The column names in the source data must match the column names in
* the target table, but the case is ignored.
* NONE: The column names in the source data are ignored, and the data is loaded based on the order of the columns in the target table.
* The match_by_column_name parameter only applies to semi-structured data, such as JSON, AVRO, ORC, PARQUET, and XML. It does not apply to CSV data, which is considered structured data2.
* When using the copy into <table> command with the CSV file format, the match_by_column_name parameter behaves as follows2:
* It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name. This means that the first row of the CSV file must contain the column names, and they must match the column names in the target table exactly, including the case. If the header is missing or does not match, the command will return an error.
* The parameter will not be ignored, even if it is set to NONE. The command will still try to match the column names in the CSV file with the column names in the target table, and will return an error if they do not match.
* The command will not return a warning stating that the file has unmatched columns. It will either load the data successfully if the column names match, or return an error if they do not match.
References:
* 1: COPY INTO <table> | Snowflake Documentation
* 2: MATCH_BY_COLUMN_NAME | Snowflake Documentation

NEW QUESTION # 76
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