2025 SNOWFLAKE PERFECT ARA-C01 RELIABLE TEST GUIDE

2025 Snowflake Perfect ARA-C01 Reliable Test Guide

2025 Snowflake Perfect ARA-C01 Reliable Test Guide

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To qualify for the Snowflake ARA-C01 exam, candidates must first obtain the SnowPro Advanced Architect certification. SnowPro Advanced Architect Certification certification requires passing the SnowPro Core exam, as well as demonstrating proficiency in data modeling and performance tuning. Once candidates have achieved this certification, they can then take the ARA-C01 Exam to further validate their expertise.

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q17-Q22):

NEW QUESTION # 17
The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:
1) Finance and Vendor Management team members who require reporting and visualization
2) Data Science team members who require access to raw data for ML model development
3) Sales team members who require engineered and protected data for data monetization What Snowflake data modeling approaches will meet these requirements? (Choose two.)

  • A. Create a set of profile-specific databases that aligns data with usage patterns.
  • B. Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.
  • C. Create a single star schema in a single database to support all consumers' requirements.
  • D. Create a raw database for landing and persisting raw data entering the data pipelines.
  • E. Consolidate data in the company's data lake and use EXTERNAL TABLES.

Answer: A,D

Explanation:
To accommodate the diverse needs of different teams and use cases within a company, a flexible and multi- faceted approach to data modeling is required.
Option B:By creating a raw database for landing and persisting raw data, you ensure that the Data Science team has access to unprocessed data for machine learning model development. This aligns with the best practices of having a staging area or raw data zone in a modern data architecture where raw data is ingested before being transformed or processed for different use cases.
Option C:Having profile-specific databases means creating targeted databases that are designed to meet the specific requirements of each user profile or team within the company. For the Finance and Vendor Management teams, the data can be structured and optimized for reporting and visualization. For the Sales team, the database can include engineered and protected data that is suitable for data monetization efforts.
This strategy not only aligns data with usage patterns but also helps in managing data access and security policies effectively.


NEW QUESTION # 18
A company is following the Data Mesh principles, including domain separation, and chose one Snowflake account for its data platform.
An Architect created two data domains to produce two data products. The Architect needs a third data domain that will use both of the data products to create an aggregate data product. The read access to the data products will be granted through a separate role.
Based on the Data Mesh principles, how should the third domain be configured to create the aggregate product if it has been granted the two read roles?

  • A. Use secondary roles for all users.
  • B. Request that the two data domains share data using the Data Exchange.
  • C. Request a technical ETL user with the sysadmin role.
  • D. Create a hierarchy between the two read roles.

Answer: B

Explanation:
In the scenario described, where a third data domain needs access to two existing data products in a Snowflake account structured according to Data Mesh principles, the best approach is to utilize Snowflake's Data Exchange functionality. Option D is correct as it facilitates the sharing and governance of data across different domains efficiently and securely. Data Exchange allows domains to publish and subscribe to live data products, enabling real-time data collaboration and access management in a governed manner. This approach is in line with Data Mesh principles, which advocate for decentralized data ownership and architecture, enhancing agility and scalability across the organization.References:
* Snowflake Documentation on Data Exchange
* Articles on Data Mesh Principles in Data Management


NEW QUESTION # 19
Materialized views based on external tables can improve query performance

  • A. TRUE
  • B. FALSE

Answer: A


NEW QUESTION # 20
The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:
1) Finance and Vendor Management team members who require reporting and visualization
2) Data Science team members who require access to raw data for ML model development
3) Sales team members who require engineered and protected data for data monetization What Snowflake data modeling approaches will meet these requirements? (Choose two.)

  • A. Create a set of profile-specific databases that aligns data with usage patterns.
  • B. Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.
  • C. Create a single star schema in a single database to support all consumers' requirements.
  • D. Create a raw database for landing and persisting raw data entering the data pipelines.
  • E. Consolidate data in the company's data lake and use EXTERNAL TABLES.

Answer: A,D

Explanation:
These two approaches are recommended by Snowflake for data modeling in a data lake scenario. Creating a raw database allows the data engineering team to ingest data from various sources without any transformation or cleansing, preserving the original data quality and format. This enables the data science team to access the raw data for ML model development. Creating a set of profile-specific databases allows the data engineering team to apply different transformations and optimizations for different use cases and data consumer requirements. For example, the finance and vendor management team can access a dimensional database that supports reporting and visualization, while the sales team can access a secure database that supports data monetization.
Reference:
Snowflake Data Lake Architecture | Snowflake Documentation
Snowflake Data Lake Best Practices | Snowflake Documentation


NEW QUESTION # 21
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 a warning stating that the file has unmatched columns.
  • 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 an error.
  • 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 # 22
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