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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a globally recognized certification program that validates an individual's expertise in designing and implementing Snowflake solutions. SnowPro Advanced Architect Certification certification is designed for experienced architects who have already achieved the SnowPro Core Certification and have a deep understanding of Snowflake's data warehousing platform. The Snowflake ARA-C01 exam covers a wide range of advanced topics, including Snowflake architecture, query optimization, data modeling, security, and performance tuning.
Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Exam is designed for experienced Snowflake architects who are looking to take their skills and knowledge to the next level. ARA-C01 exam is the culmination of the SnowPro Certification program and is the highest level of certification that Snowflake offers. It is a challenging exam that covers a wide range of topics related to Snowflake architecture, including data modeling, security, performance, and scalability.
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Snowflake ARA-C01 certification exam is designed to test a candidate's knowledge and skills related to Snowflake's advanced architectural concepts. It is a rigorous exam that requires candidates to have a strong understanding of Snowflake's architecture, data modeling, performance tuning, security, and data integration. ARA-C01 Exam is divided into multiple sections, each of which covers a specific topic related to Snowflake's architecture. Candidates must demonstrate their proficiency in each section to earn their certification.
NEW QUESTION # 68
A company needs an efficient way to get data from an external partner who is also a Snowflake user. The current process involves daily JSON files uploaded manually and frequently changed.
What is the MOST efficient solution?
Answer: B
Explanation:
When both parties are Snowflake users, Secure Data Sharing is the most efficient and reliable way to exchange data (Answer A). By creating a share, the data provider can grant real-time access to selected objects without copying or moving data. This eliminates ingestion pipelines, reduces latency, and automatically adapts to schema changes.
Exporting data to cloud storage introduces unnecessary complexity, delays, and maintenance overhead.
Reader accounts are intended for consumers who are not Snowflake customers, which does not apply in this scenario. Maintaining manual file-based ingestion is inefficient and error-prone, especially when schemas change frequently.
For SnowPro Architect candidates, this question reinforces a core Snowflake principle: whenever possible, use native Secure Data Sharing to simplify architectures, improve freshness, and reduce operational costs.
NEW QUESTION # 69
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?
Answer: D
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.
Snowflake Documentation: Column Masking
Snowflake Learning: Column Masking
NEW QUESTION # 70
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.)
Answer: A,D,F
Explanation:
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 # 71
A user, analyst_user has been granted the analyst_role, and is deploying a SnowSQL script to run as a background service to extract data from Snowflake.
What steps should be taken to allow the IP addresses to be accessed? (Select TWO).
Answer: C,E
Explanation:
To ensure that ananalyst_usercan only access Snowflake from specific IP addresses, the following steps are required:
* Option B: This alters the network policy directly linked toanalyst_user. Setting a network policy on the user level is effective and ensures that the specified network restrictions apply directly and exclusively to this user.
* Option D: Before a network policy can be set or altered, the appropriate role with permission to manage network policies must be used.SECURITYADMINis typically the role that has privileges to create and manage network policies in Snowflake. Creating a network policy that specifies allowed IP addresses ensures that only requests coming from those IPs can access Snowflake under this policy. After creation, this policy can be linked to specific users or roles as needed.
Options A and E mention altering roles or using the wrong role (USERADMINtypically does not manage network security settings), and option C incorrectly attempts to set a network policy directly as an IP address, which is not syntactically or functionally valid.
References:Snowflake's security management documentation covering network policies and role-based access controls.
NEW QUESTION # 72
An Architect is troubleshooting a query with poor performance using the QUERY_HIST0RY function. The Architect observes that the COMPILATIONJHME is greater than the EXECUTIONJTIME.
What is the reason for this?
Answer: D
Explanation:
Compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. It also involves some pruning of partition files, making the query execution efficient2 If the compilation time is greater than the execution time, it means that the optimizer spent more time analyzing the query than actually running it. This could indicate that the query has overly complex logic, such as multiple joins, subqueries, aggregations, or expressions. The complexity of the query could also affect the size and quality of the query plan, which could impact the performance of the query3 To reduce the compilation time, the Architect can try to simplify the query logic, use views or common table expressions (CTEs) to break down the query into smaller parts, or use hints to guide the optimizer. The Architect can also use the EXPLAIN command to examine the query plan and identify potential bottlenecks or inefficiencies4 References:
* 1: SnowPro Advanced: Architect | Study Guide 5
* 2: Snowflake Documentation | Query Profile Overview 6
* 3: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time 7
* 4: Snowflake Documentation | Optimizing Query Performance 8
* : SnowPro Advanced: Architect | Study Guide
* : Query Profile Overview
* : Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time
* : Optimizing Query Performance
NEW QUESTION # 73
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