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To become certified in Snowflake ARA-C01, candidates must first meet the eligibility requirements, which include having experience in data warehousing, data modeling, and data integration. Candidates must also complete the SnowPro Core Certification Exam and pass the SnowPro Advanced Architect Certification Exam, which is a rigorous and comprehensive exam that tests advanced knowledge and skills in Snowflake architecture.

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The Best ARA-C01 Relevant Answers offer you accurate Reliable Test Answers | Snowflake SnowPro Advanced Architect Certification

To pass the Snowflake ARA-C01 exam on the first try, candidates need SnowPro Advanced Architect Certification updated practice material. Preparing with real ARA-C01 exam questions is one of the finest strategies for cracking the exam in one go. Students who study with ARA-C01 Real Questions are more prepared for the exam, increasing their chances of succeeding. The ARA-C01 exam preparation calls for a strong preparation and precise Snowflake ARA-C01 practice material.

Snowflake ARA-C01 Certification Exam covers a wide range of topics, including Snowflake architecture design, Snowflake security, performance tuning, data integration, and data governance. ARA-C01 exam is intended to test the candidate's deep understanding of these topics and their ability to apply the best practices to design and implement Snowflake solutions. ARA-C01 exam is conducted online and consists of multiple-choice questions that are designed to test the candidate's knowledge and practical skills.

Snowflake ARA-C01 Certification Exam is composed of two parts, the first being a multiple-choice exam that tests fundamental knowledge of Snowflake architecture and features. The second part of the exam is a hands-on lab where candidates are given a set of requirements and need to implement a Snowflake solution to meet those requirements. The lab is designed to test the candidate's ability to apply their knowledge in real-world scenarios.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q159-Q164):

NEW QUESTION # 159
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 the COPY INTO command.
C. Use a COPY command with a task.
D. Use Snowpipe with auto-ingest.
E. Use a combination of a task and a stream.

Answer: A,D

Explanation:
These two options are the best ways to meet the requirement of loading data from an external stage and making it accessible by dashboards with the least amount of coding.
* Snowpipe with auto-ingest is a feature that enables continuous and automated data loading from an external stage into a Snowflake table. Snowpipe uses event notifications from the cloud storage service to detect new or modified files in the stage and triggers a COPY INTO command to load the data into the table. Snowpipe is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Snowpipe also supports loading data from files of any size, as long as they are in a supported format1.
* A materialized view on an external table is a feature that enables creating a pre-computed result set from an external table and storing it in Snowflake. A materialized view can improve the performance and efficiency of querying data from an external table, especially for complex queries or dashboards. A materialized view can also support aggregations, joins, and filters on the external table data. A
* materialized view on an external table is automatically refreshed when the underlying data in the external stage changes, as long as the AUTO_REFRESH parameter is set to true2.
References:
* Snowpipe Overview | Snowflake Documentation
* Materialized Views on External Tables | Snowflake Documentation

NEW QUESTION # 160
Which SQL ALTER command will MAXIMIZE memory and compute resources for a Snowpark stored procedure when executed on the snowpark_opt_wh warehouse?

A. ALTER WAREHOUSE snowpark_opt_wh SET MAX_CONCURRENCY_LEVEL = 2;
B. ALTER WAREHOUSE snowpark_opt_wh SET MAX_CONCURRENCY_LEVEL = 16;
C. ALTER WAREHOUSE snowpark_opt_wh SET MAX_CONCURRENCY_LEVEL = 8;
D. ALTER WAREHOUSE snowpark_opt_wh SET MAX_CONCURRENCY_LEVEL = 1;

Answer: D

Explanation:
Snowpark workloads are often memory- and compute-intensive, especially when executing complex transformations, large joins, or machine learning logic inside stored procedures. In Snowflake, the MAX_CONCURRENCY_LEVEL warehouse parameter controls how many concurrent queries can run on a single cluster of a virtual warehouse. Lowering concurrency increases the amount of compute and memory available to each individual query.
Setting MAX_CONCURRENCY_LEVEL = 1 ensures that only one query can execute at a time on the warehouse cluster, allowing that query to consume the maximum possible share of CPU, memory, and I/O resources. This is the recommended configuration when the goal is to optimize performance for a single Snowpark job rather than maximizing throughput for many users. Higher concurrency levels would divide resources across multiple queries, reducing per-query performance and potentially causing spilling to remote storage.
For SnowPro Architect candidates, this question reinforces an important cost and performance tradeoff:
concurrency tuning is a powerful lever. When running batch-oriented or compute-heavy Snowpark workloads, architects should favor lower concurrency to maximize per-query resources, even if that means fewer concurrent workloads.
=========

NEW QUESTION # 161
Which data models can be used when modeling tables in a Snowflake environment? (Select THREE).

A. Dimensional/Kimball
B. Bayesian hierarchical model
C. lnmon/3NF
D. Data vault
E. Data lake
F. Graph model

Answer: A,C,D

Explanation:
Snowflake is a cloud data platform that supports various data models for modeling tables in a Snowflake environment. The data models can be classified into two categories: dimensional and normalized. Dimensional data models are designed to optimize query performance and ease of use for business intelligence and analytics. Normalized data models are designed to reduce data redundancy and ensure data integrity for transactional and operational systems. The following are some of the data models that can be used in Snowflake:
* Dimensional/Kimball: This is a popular dimensional data model that uses a star or snowflake schema to organize data into fact and dimension tables. Fact tables store quantitative measures and foreign keys to dimension tables. Dimension tables store descriptive attributes and hierarchies. A star schema has a single denormalized dimension table for each dimension, while a snowflake schema has multiple normalized dimension tables for each dimension. Snowflake supports both star and snowflake schemas, and allows users to create views and joins to simplify queries.
* Inmon/3NF: This is a common normalized data model that uses a third normal form (3NF) schema to organize data into entities and relationships. 3NF schema eliminates data duplication and ensures data consistency by applying three rules: 1) every column in a table must depend on the primary key, 2)
* every column in a table must depend on the whole primary key, not a part of it, and 3) every column in a table must depend only on the primary key, not on other columns. Snowflake supports 3NF schema and allows users to create referential integrity constraints and foreign key relationships to enforce data quality.
* Data vault: This is a hybrid data model that combines the best practices of dimensional and normalized data models to create a scalable, flexible, and resilient data warehouse. Data vault schema consists of three types of tables: hubs, links, and satellites. Hubs store business keys and metadata for each entity.
Links store associations and relationships between entities. Satellites store descriptive attributes and historical changes for each entity or relationship. Snowflake supports data vault schema and allows users to leverage its features such as time travel, zero-copy cloning, and secure data sharing to implement data vault methodology.
References: What is Data Modeling? | Snowflake, Snowflake Schema in Data Warehouse Model - GeeksforGeeks, [Data Vault 2.0 Modeling with Snowflake]

NEW QUESTION # 162
A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.
The data pipeline needs to run continuously ang efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.
Which design will meet these requirements?

A. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
B. Ingest the data using COPY INTO and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
C. Ingest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark connector.
Apply transformations using another Spark job. Develop a python program to do model inference by leveraging the Amazon Comprehend text analysis API. Then write the results to a Snowflake table and create a listing in the Snowflake Marketplace to make the data available to other companies.
D. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Answer: D

Explanation:
This design meets all the requirements for the data pipeline. Snowpipe is a feature that enables continuous data loading into Snowflake from object storage using event notifications. It is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Streams and tasks are features that enable automated data pipelines within Snowflake, using change data capture and scheduled execution. They are also efficient, scalable, and serverless, and they simplify the data transformation process.
External functions are functions that can invoke external services or APIs from within Snowflake. They can be used to integrate with Amazon Comprehend and perform sentiment analysis on the data. The results can be written back to a Snowflake table using standard SQL commands. Snowflake Marketplace is a platform that allows data providers to share data with data consumers across different accounts, regions, and cloud platforms. It is a secure and easy way to make data publicly available to other companies.
Snowpipe Overview | Snowflake Documentation
Introduction to Data Pipelines | Snowflake Documentation
External Functions Overview | Snowflake Documentation
Snowflake Data Marketplace Overview | Snowflake Documentation

NEW QUESTION # 163
A healthcare company is deploying a Snowflake account that may include Personal Health Information (PHI).
The company must ensure compliance with all relevant privacy standards.
Which best practice recommendations will meet data protection and compliance requirements? (Choose three.)

A. Rewrite SQL queries to eliminate projections of PHI data based on current_role().
B. Create Dynamic Data Masking policies and apply them to columns that contain PHI.
C. Use, at minimum, the Business Critical edition of Snowflake.
D. Use the External Tokenization feature to obfuscate sensitive data.
E. Avoid sharing data with partner organizations.
F. Use the Internal Tokenization feature to obfuscate sensitive data.

Answer: B,C,D

Explanation:
* A healthcare company that handles PHI data must ensure compliance with relevant privacy standards, such as HIPAA, HITRUST, and GDPR. Snowflake provides several features and best practices to help customers meet their data protection and compliance requirements1.
* One best practice recommendation is to use, at minimum, the Business Critical edition of Snowflake. This edition provides the highest level of data protection and security, including end-to-end encryption with customer-managed keys, enhanced object-level security, and HIPAA and HITRUST compliance2. Therefore, option A is correct.
* Another best practice recommendation is to create Dynamic Data Masking policies and apply them to columns that contain PHI. Dynamic Data Masking is a feature that allows masking or redacting sensitive data based on the current user's role. This way, only authorized users can view the unmasked data, while others will see masked values, such as NULL, asterisks, or random characters3. Therefore, option B is correct.
* A third best practice recommendation is to use the External Tokenization feature to obfuscate sensitive data. External Tokenization is a feature that allows replacing sensitive data with tokens that are
* generated and stored by an external service, such as Protegrity. This way, the original data is never stored or processed by Snowflake, and only authorized users can access the tokenized data through the external service4. Therefore, option D is correct.
* Option C is incorrect, because the Internal Tokenization feature is not available in Snowflake. Snowflake does not provide any native tokenization functionality, but only supports integration with external tokenization services4.
* Option E is incorrect, because rewriting SQL queries to eliminate projections of PHI data based on current_role() is not a best practice. This approach is error-prone, inefficient, and hard to maintain. A better alternative is to use Dynamic Data Masking policies, which can automatically mask data based on the user's role without modifying the queries3.
* Option F is incorrect, because avoiding sharing data with partner organizations is not a best practice.
Snowflake enables secure and governed data sharing with internal and external consumers, such as business units, customers, or partners. Data sharing does not involve copying or moving data, but only granting access privileges to the shared objects. Data sharing can also leverage Dynamic Data Masking and External Tokenization features to protect sensitive data5.
References: : Snowflake's Security & Compliance Reports : Snowflake Editions : Dynamic Data Masking : External Tokenization : Secure Data Sharing

NEW QUESTION # 164
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