PASS GUARANTEED QUIZ PERFECT SNOWFLAKE - ARA-C01 - SNOWPRO ADVANCED ARCHITECT CERTIFICATION BRAINDUMPS PDF

Pass Guaranteed Quiz Perfect Snowflake - ARA-C01 - SnowPro Advanced Architect Certification Braindumps Pdf

Pass Guaranteed Quiz Perfect Snowflake - ARA-C01 - SnowPro Advanced Architect Certification Braindumps Pdf

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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a certification program designed for IT professionals who want to demonstrate their expertise in architecting data solutions using Snowflake. ARA-C01 Exam is intended for individuals who have already obtained the SnowPro Core or SnowPro Advanced certification and have experience working with Snowflake as a data warehousing platform.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q80-Q85):

NEW QUESTION # 80
Which semi structured data function interprets an input string as a JSON document, producing a VARIANT value.

  • A. PARSE_JSON
  • B. STRIP_JSON
  • C. PARSE_XML

Answer: A


NEW QUESTION # 81
Which organization-related tasks can be performed by the ORGADMIN role? (Choose three.)

  • A. Deleting an account
  • B. Changing the name of the organization
  • C. Creating an account
  • D. Enabling the replication of a database
  • E. Changing the name of an account
  • F. Viewing a list of organization accounts

Answer: C,D,F

Explanation:
Explanation
According to the SnowPro Advanced: Architect documents and learning resources, the organization-related tasks that can be performed by the ORGADMIN role are:
* Creating an account in the organization. A user with the ORGADMIN role can use the CREATE ACCOUNT command to create a new account that belongs to the same organization as the current account1.
* Viewing a list of organization accounts. A user with the ORGADMIN role can use the SHOW ORGANIZATION ACCOUNTS command to view the names and properties of all accounts in the organization2. Alternatively, the user can use the Admin a Accounts page in the web interface to view the organization name and account names3.
* Enabling the replication of a database. A user with the ORGADMIN role can use the SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER function to enable database replication for an account in the organization. This allows the user to replicate databases across accounts in different regions and cloud platforms for data availability and durability4.
The other options are incorrect because they are not organization-related tasks that can be performed by the ORGADMIN role. Option A is incorrect because changing the name of the organization is not a task that can be performed by the ORGADMIN role. To change the name of an organization, the user must contact Snowflake Support3. Option D is incorrect because changing the name of an account is not a task that can be performed by the ORGADMIN role. To change the name of an account, the user must contact Snowflake Support5. Option E is incorrect because deleting an account is not a task that can be performed by the ORGADMIN role. To delete an account, the user must contact Snowflake Support. References: CREATE ACCOUNT | Snowflake Documentation, SHOW ORGANIZATION ACCOUNTS | Snowflake Documentation, Getting Started with Organizations | Snowflake Documentation, SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER | Snowflake Documentation, ALTER ACCOUNT | Snowflake Documentation, [DROP ACCOUNT | Snowflake Documentation]


NEW QUESTION # 82
A user has the appropriate privilege to see unmasked data in a column.
If the user loads this column data into another column that does not have a masking policy, what will occur?

  • A. Unmasked data will be loaded into the new column but only users with the appropriate privileges will be able to see the unmasked data.
  • B. Unmasked data will be loaded in the new column.
  • C. Unmasked data will be loaded into the new column and no users will be able to see the unmasked data.
  • D. Masked data will be loaded into the new column.

Answer: B

Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, column masking policies are applied at query time based on the privileges of the user who runs the query. Therefore, if a user has the privilege to see unmasked data in a column, they will see the original data when they query that column. If they load this column data into another column that does not have a masking policy, the unmasked data will be loaded in the new column, and any user who can query the new column will see the unmasked data as well.
The masking policy does not affect the underlying data in the column, only the query results.
References:
* Snowflake Documentation: Column Masking
* Snowflake Learning: Column Masking


NEW QUESTION # 83
When using the copy into <table> command with the CSV file format, how does the match_by_column_name parameter behave?

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

Answer: A

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.
Reference:
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.
1: COPY INTO <table> | Snowflake Documentation
2: MATCH_BY_COLUMN_NAME | Snowflake Documentation


NEW QUESTION # 84
Files arrive in an external stage every 10 seconds from a proprietary system. The files range in size from 500 K to 3 MB. The data must be accessible by dashboards as soon as it arrives.
How can a Snowflake Architect meet this requirement with the LEAST amount of coding? (Choose two.)

  • A. Use a materialized view on an external table.
  • B. Use a combination of a task and a stream.
  • C. Use a COPY command with a task.
  • D. Use the COPY INTO command.
  • E. Use Snowpipe with auto-ingest.

Answer: B,E

Explanation:
The requirement is for the data to be accessible as quickly as possible after it arrives in the external stage with minimal coding effort.
Option A: Snowpipe with auto-ingest is a service that continuously loads data as it arrives in the stage. With auto-ingest, Snowpipe automatically detects new files as they arrive in a cloud stage and loads the data into the specified Snowflake table with minimal delay and no intervention required. This is an ideal low-maintenance solution for the given scenario where files are arriving at a very high frequency.
Option E: Using a combination of a task and a stream allows for real-time change data capture in Snowflake. A stream records changes (inserts, updates, and deletes) made to a table, and a task can be scheduled to trigger on a very short interval, ensuring that changes are processed into the dashboard tables as they occur.


NEW QUESTION # 85
......

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