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ARA-C01 Questions and Answers

Question # 1

A Snowflake Architect created a new data share and would like to verify that only specific records in secure views are visible within the data share by the consumers.

What is the recommended way to validate data accessibility by the consumers?

Create reader accounts as shown below and impersonate the consumers by logging in with their credentials.

create managed account reader_acctl admin_name = userl , adroin_password â– 'Sdfed43da!44T , type = reader;

Create a row access policy as shown below and assign it to the data share.

create or replace row access policy rap_acct as (acct_id varchar) returns boolean -> case when 'acctl_role' = current_role() then true else false end;

Set the session parameter called SIMULATED_DATA_SHARING_C0NSUMER as shown below in order to impersonate the consumer accounts.

alter session set simulated_data_sharing_consumer - 'Consumer Acctl*

Alter the share settings as shown below, in order to impersonate a specific consumer account.

alter share sales share set accounts = 'Consumerlâ€™ share restrictions = true

Question # 2

An Architect is implementing a CI/CD process. When attempting to clone a table from a production to a development environment, the cloning operation fails.

What could be causing this to happen?

The table is transient.

The table has a masking policy.

The retention time for the table is set to zero.

Tables cannot be cloned from a higher environment to a lower environment.

Question # 3

When using the copy into

command with the CSV file format, how does the match_by_column_name parameter behave?

It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name.

The parameter will be ignored.

The command will return an error.

The command will return a warning stating that the file has unmatched columns.

Answer:

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.Â 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.Â 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.Â 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.Â 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:Â 1:Â Snowpipe OverviewÂ 2:Â Using Streams and Tasks to Automate Data PipelinesÂ 3:Â External Functions OverviewÂ 4:Â Snowflake Data Marketplace OverviewÂ : [Loading Data Using COPY INTO] : [What is Amazon EMR?] : [PySpark Overview]

The copy into

command is used to load data from staged files into an existing table in Snowflake.Â 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.Â The parameter can have one of the following values2:

The match_by_column_name parameter only applies to semi-structured data, such as JSON, AVRO, ORC, PARQUET, and XML.Â It does not apply to CSV data, which is considered structured data2.

When using the copy into

command with the CSV file format, the match_by_column_name parameter behaves as follows2:

References:

1: COPY INTO

| Snowflake Documentation

2: MATCH_BY_COLUMN_NAME | Snowflake Documentation

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