2026 Valid ARA-C01 FREE EXAM DUMPS QUESTIONS & ANSWERS
Free ARA-C01 Exam Braindumps Snowflake Pratice Exam
Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a highly sought-after certification for professionals who work with the Snowflake cloud data platform. It is designed to test the expertise of architects who design and build complex data solutions on the Snowflake platform. SnowPro Advanced Architect Certification certification is an advanced level certification exam and requires a solid understanding of Snowflake's architecture, data modeling, and programming concepts.
Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a comprehensive certification program designed for professionals who want to demonstrate their expertise in advanced Snowflake architecture. SnowPro Advanced Architect Certification certification program is designed to validate the skills and knowledge of individuals who have a deep understanding of Snowflake's architecture and design principles, and who can translate business requirements into scalable, secure, and high-performing Snowflake solutions.
Snowflake ARA-C01 exam is a certification exam designed for advanced architects who are experienced in implementing and operating Snowflake solutions. ARA-C01 exam is the highest level of certification offered by Snowflake and is intended to validate the skills and expertise of professionals who are responsible for designing, building, and managing complex Snowflake environments.
NEW QUESTION # 135
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported.
What could be causing this?
- A. There were variations in string lengths for the JSON values in the recent data imports.
- B. The order of the keys in the JSON was changed.
- C. The recent data imports contained fewer fields than usual.
- D. There were JSON nulls in the recent data imports.
Answer: A
NEW QUESTION # 136
Which SQL alter command will MAXIMIZE memory and compute resources for a Snowpark stored procedure when executed on the snowpark_opt_wh warehouse?
- A.
- B.
- C.
- D.
Answer: A
Explanation:
To maximize memory and compute resources for a Snowpark stored procedure, you need to set the MAX_CONCURRENCY_LEVEL parameter for the warehouse that executes the stored procedure. This parameter determines the maximum number of concurrent queries that can run on a single warehouse. By setting it to 16, you ensure that the warehouse can use all the available CPU cores and memory on a single node, which is the optimal configuration for Snowpark-optimized warehouses. This will improve the performance and efficiency of the stored procedure, as it will not have to share resources with other queries or nodes. The other options are incorrect because they either do not change the MAX_CONCURRENCY_LEVEL parameter, or they set it to a lower value than 16, which will reduce the memory and compute resources for the stored procedure. References:
* [Snowpark-optimized Warehouses] 1
* [Training Machine Learning Models with Snowpark Python] 2
* [Snowflake Shorts: Snowpark Optimized Warehouses] 3
NEW QUESTION # 137
Which feature provides the capability to define an alternate cluster key for a table with an existing cluster key?
- A. External table
- B. Search optimization
- C. Materialized view
- D. Result cache
Answer: C
Explanation:
A materialized view is a feature that provides the capability to define an alternate cluster key for a table with an existing cluster key. A materialized view is a pre-computed result set that is stored in Snowflake and can be queried like a regular table. A materialized view can have a different cluster key than the base table, which can improve the performance and efficiency of queries on the materialized view. A materialized view can also support aggregations, joins, and filters on the base table data. A materialized view is automatically refreshed when the underlying data in the base table changes, as long as the AUTO_REFRESH parameter is set to true1.
Materialized Views | Snowflake Documentation
NEW QUESTION # 138
How can the Snowpipe REST API be used to keep a log of data load history?
- A. Call insertReport every 20 minutes, fetching the last 10,000 entries.
- B. Call loadHistoryScan every 10 minutes for a 15-minute time range.
- C. Call loadHistoryScan every minute for the maximum time range.
- D. Call insertReport every 8 minutes for a 10-minute time range.
Answer: B
Explanation:
Snowpipe is a service that automates and optimizes the loading of data from external stages into Snowflake tables. Snowpipe uses a queue to ingest files as they become available in the stage. Snowpipe also provides REST endpoints to load data and retrieve load history reports1.
The loadHistoryScan endpoint returns the history of files that have been ingested by Snowpipe within a specified time range. The endpoint accepts the following parameters2:
pipe: The fully-qualified name of the pipe to query.
startTimeInclusive: The start of the time range to query, in ISO 8601 format. The value must be within the past 14 days.
endTimeExclusive: The end of the time range to query, in ISO 8601 format. The value must be later than the start time and within the past 14 days.
recentFirst: A boolean flag that indicates whether to return the most recent files first or last. The default value is false, which means the oldest files are returned first.
showSkippedFiles: A boolean flag that indicates whether to include files that were skipped by Snowpipe in the response. The default value is false, which means only files that were loaded are returned.
The loadHistoryScan endpoint can be used to keep a log of data load history by calling it periodically with a suitable time range. The best option among the choices is D, which is to call loadHistoryScan every 10 minutes for a 15-minute time range. This option ensures that the endpoint is called frequently enough to capture the latest files that have been ingested, and that the time range is wide enough to avoid missing any files that may have been delayed or retried by Snowpipe. The other options are either too infrequent, too narrow, or use the wrong endpoint3.
Reference:
1: Introduction to Snowpipe | Snowflake Documentation
2: loadHistoryScan | Snowflake Documentation
3: Monitoring Snowpipe Load History | Snowflake Documentation
NEW QUESTION # 139
The following table exists in the production database:
A regulatory requirement states that the company must mask the username for events that are older than six months based on the current date when the data is queried.
How can the requirement be met without duplicating the event data and making sure it is applied when creating views using the table or cloning the table?
- A. Use a masking policy on the username column using a entitlement table with valid dates.
- B. Use a secure view on the user_events table using a case statement on the username column.
- C. Use a masking policy on the username column with event_timestamp as a conditional column.
- D. Use a row level policy on the user_events table using a entitlement table with valid dates.
Answer: C
Explanation:
A masking policy is a feature of Snowflake that allows masking sensitive data in query results based on the role of the user and the condition of the data. A masking policy can be applied to a column in a table or a view, and it can use another column in the same table or view as a conditional column. A conditional column is a column that determines whether the masking policy is applied or not based on its value1.
In this case, the requirement can be met by using a masking policy on the username column with event_timestamp as a conditional column. The masking policy can use a function that masks the username if the event_timestamp is older than six months based on the current date, and returns the original username otherwise. The masking policy can be applied to the user_events table, and it will also be applied when creating views using the table or cloning the table2.
The other options are not correct because:
A) Using a masking policy on the username column using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the masking policy function. This would add complexity and overhead to the masking policy, and it would not use the event_timestamp column as the condition for masking.
B) Using a row level policy on the user_events table using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the row access policy function. This would filter out the rows that have event_timestamp older than six months based on the valid dates, instead of masking the username column. This would not meet the requirement of masking the username, and it would also reduce the visibility of the event data.
D) Using a secure view on the user_events table using a case statement on the username column would require creating a view that uses a case expression to mask the username column based on the event_timestamp column. This would meet the requirement of masking the username, but it would not be applied when cloning the table. A secure view is a view that prevents the underlying data from being exposed by queries on the view. However, a secure view does not prevent the underlying data from being exposed by cloning the table3.
Reference:
1: Masking Policies | Snowflake Documentation
2: Using Conditional Columns in Masking Policies | Snowflake Documentation
3: Secure Views | Snowflake Documentation
NEW QUESTION # 140
A DevOps team has a requirement for recovery of staging tables used in a complex set of data pipelines. The staging tables are all located in the same staging schem a. One of the requirements is to have online recovery of data on a rolling 7-day basis.
After setting up the DATA_RETENTION_TIME_IN_DAYS at the database level, certain tables remain unrecoverable past 1 day.
What would cause this to occur? (Choose two.)
- A. The staging schema has not been setup for MANAGED ACCESS.
- B. The DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day.
- C. The DevOps role should be granted ALLOW_RECOVERY privilege on the staging schema.
- D. The tables exceed the 1 TB limit for data recovery.
- E. The staging tables are of the TRANSIENT type.
Answer: B,E
Explanation:
The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for an object (database, schema, or table) in Snowflake. This parameter specifies the number of days for which historical data is preserved and can be accessed using Time Travel operations (SELECT, CREATE ... CLONE, UNDROP)1.
The requirement for recovery of staging tables on a rolling 7-day basis means that the DATA_RETENTION_TIME_IN_DAYS parameter should be set to 7 at the database level. However, this parameter can be overridden at the lower levels (schema or table) if they have a different value1.
Therefore, one possible cause for certain tables to remain unrecoverable past 1 day is that the DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day. This would override the database level setting and limit the Time Travel retention period for all the tables in the schema to 1 day. To fix this, the parameter should be unset or set to 7 at the schema level1. Therefore, option B is correct.
Another possible cause for certain tables to remain unrecoverable past 1 day is that the staging tables are of the TRANSIENT type. Transient tables are tables that do not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. Transient tables are suitable for temporary or intermediate data that can be easily reproduced or replicated2. To fix this, the tables should be created as permanent tables, which can have a Time Travel retention period of up to 90 days1. Therefore, option D is correct.
Option A is incorrect because the MANAGED ACCESS feature is not related to the data recovery requirement. MANAGED ACCESS is a feature that allows granting access privileges to objects without explicitly granting the privileges to roles. It does not affect the Time Travel retention period or the data availability3.
Option C is incorrect because there is no 1 TB limit for data recovery in Snowflake. The data storage size does not affect the Time Travel retention period or the data availability4.
Option E is incorrect because there is no ALLOW_RECOVERY privilege in Snowflake. The privilege required to perform Time Travel operations is SELECT, which allows querying historical data in tables5.
NEW QUESTION # 141
Based on the Snowflake object hierarchy, what securable objects belong directly to a Snowflake account? (Select THREE).
- A. Stage
- B. Warehouse
- C. Table
- D. Database
- E. Schema
- F. Role
Answer: B,D,F
NEW QUESTION # 142
Which of the below commands will use warehouse credits?
- A. SHOW TABLES LIKE 'SNOWFL%';
- B. SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID;
- C. SELECT COUNT(*) FROM SNOWFLAKE;
- D. SELECT MAX(FLAKE_ID) FROM SNOWFLAKE;
Answer: B,C,D
Explanation:
* Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake. A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.
* Among the commands listed in the question, the following ones will use warehouse credits:
* SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.
* SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows in the table3. Therefore, option C is correct.
* SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.
* The command that will not use warehouse credits is:
* SHOW TABLES LIKE 'SNOWFL%': This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern 'SNOWFL%' in the current database and schema5. Therefore, option A is incorrect.
Understanding Compute Cost : MAX Function : COUNT Function : GROUP BY Clause : SHOW TABLES
NEW QUESTION # 143
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 Snowpipe with auto-ingest.
- B. Use a materialized view on an external table.
- C. Use a COPY command with a task.
- D. Use a combination of a task and a stream.
- E. Use the COPY INTO command.
Answer: A,D
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 # 144
A company needs to share its product catalog data with one of its partners. The product catalog data is stored in two database tables: product_category, and product_details. Both tables can be joined by the product_id column. Data access should be governed, and only the partner should have access to the records.
The partner is not a Snowflake customer. The partner uses Amazon S3 for cloud storage.
Which design will be the MOST cost-effective and secure, while using the required Snowflake features?
- A. Create a reader account for the partner and share the data sets as secure views.
- B. Create a database user for the partner and give them access to the required data sets.
- C. Publish product_category and product_details data sets on the Snowflake Marketplace.
- D. Use Secure Data Sharing with an S3 bucket as a destination.
Answer: A
Explanation:
A reader account is a type of Snowflake account that allows external users to access data shared by a provider account without being a Snowflake customer. A reader account can be created and managed by the provider account, and can use the Snowflake web interface or JDBC/ODBC drivers to query the shared data. A reader account is billed to the provider account based on the credits consumed by the queries1. A secure view is a type of view that applies row-level security filters to the underlying tables, and masks the data that is not accessible to the user. A secure view can be shared with a reader account to provide granular and governed access to the data2. In this scenario, creating a reader account for the partner and sharing the data sets as secure views would be the most cost-effective and secure design, while using the required Snowflake features, because:
It would avoid the data transfer and storage costs of using an S3 bucket as a destination, and the potential security risks of exposing the data to unauthorized access or modification.
It would avoid the complexity and overhead of publishing the data sets on the Snowflake Marketplace, and the potential loss of control over the data ownership and pricing.
It would avoid the need to create a database user for the partner and grant them access to the required data sets, which would require the partner to have a Snowflake account and consume the provider's resources.
Reference:
Reader Accounts
Secure Views
NEW QUESTION # 145
When unloading from a snowflake table to an internal or external stage, the COPY INTO command supports which of the below ones?
- A. Omission of columns
- B. Casts
- C. Join
- D. String truncation
- E. Reordering columns
Answer: A,B,C,D,E
NEW QUESTION # 146
A company has an external vendor who puts data into Google Cloud Storage. The company's Snowflake account is set up in Azure.
What would be the MOST efficient way to load data from the vendor into Snowflake?
- A. Ask the vendor to create a Snowflake account, load the data into Snowflake and create a data share.
- B. Copy the data from Google Cloud Storage to Azure Blob storage using external tools and load data from Blob storage to Snowflake.
- C. Create an external stage on Google Cloud Storage and use the external table to load the data into Snowflake.
- D. Create a Snowflake Account in the Google Cloud Platform (GCP), ingest data into this account and use data replication to move the data from GCP to Azure.
Answer: C
Explanation:
The most efficient way to load data from the vendor into Snowflake is to create an external stage on Google Cloud Storage and use the external table to load the data into Snowflake (Option B). This way, you can avoid copying or moving the data across different cloud platforms, which can incur additional costs and latency.
You can also leverage the external table feature to query the data directly from Google Cloud Storage without loading it into Snowflake tables, which can save storage space and improve performance. Option A is not efficient because it requires the vendor to create a Snowflake account and a data share, which can be complicated and costly. Option C is not efficient because it involves copying the data from Google Cloud Storage to Azure Blob storage using external tools, which can be slow and expensive. Option D is not efficient because it requires creating a Snowflake account in the Google Cloud Platform (GCP), ingesting data into this account, and using data replication to move the data from GCP to Azure, which can be complex and time-consuming. References: The answer can be verified from Snowflake's official documentation on external stages and external tables available on their website. Here are some relevant links:
* Using External Stages | Snowflake Documentation
* Using External Tables | Snowflake Documentation
* Loading Data from a Stage | Snowflake Documentation
NEW QUESTION # 147
The IT Security team has identified that there is an ongoing credential stuffing attack on many of their organization's system.
What is the BEST way to find recent and ongoing login attempts to Snowflake?
- A. Call the LOGIN_HISTORY Information Schema table function.
- B. View the History tab in the Snowflake UI and set up a filter for SQL text that contains the text "LOGIN".
- C. View the Users section in the Account tab in the Snowflake UI and review the last login column.
- D. Query the LOGIN_HISTORY view in the ACCOUNT_USAGE schema in the SNOWFLAKE database.
Answer: A
NEW QUESTION # 148
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported.
What could be causing this?
- A. There were variations in string lengths for the JSON values in the recent data imports.
- B. The recent data imports contained fewer fields than usual.
- C. There were JSON nulls in the recent data imports.
- D. The order of the keys in the JSON was changed.
Answer: A,D
Explanation:
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:
* The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column-like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.
* There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column.
Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the
* JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.
The other options are not valid causes for poor query performance:
* There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi-structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.
* The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.
References:
* Considerations for Semi-structured Data Stored in VARIANT
* Snowflake Architect Training
* Snowflake query performance on unique element in variant column
* Snowflake variant performance
NEW QUESTION # 149
Which of the below commands will use warehouse credits?
- A. SHOW TABLES LIKE 'SNOWFL%';
- B. SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID;
- C. SELECT COUNT(*) FROM SNOWFLAKE;
- D. SELECT MAX(FLAKE_ID) FROM SNOWFLAKE;
Answer: B,C,D
Explanation:
Explanation
* Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake.
A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.
* Among the commands listed in the question, the following ones will use warehouse credits:
* SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.
* SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits
* because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows in the table3. Therefore, option C is correct.
* SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouseto execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.
* The command that will not use warehouse credits is:
* SHOW TABLES LIKE 'SNOWFL%': This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern 'SNOWFL%' in the current database and schema5. Therefore, option A is incorrect.
References: : Understanding Compute Cost : MAX Function : COUNT Function : GROUP BY Clause : SHOW TABLES
NEW QUESTION # 150
A retail company has 2000+ stores spread across the country. Store Managers report that they are having trouble running key reports related to inventory management, sales targets, payroll, and staffing during business hours. The Managers report that performance is poor and time-outs occur frequently.
Currently all reports share the same Snowflake virtual warehouse.
How should this situation be addressed? (Select TWO).
- A. Configure the virtual warehouse to be multi-clustered.
- B. Configure a dedicated virtual warehouse for the Store Manager team.
- C. Use a Business Intelligence tool for in-memory computation to improve performance.
- D. Configure the virtual warehouse to size 4-XL
- E. Advise the Store Manager team to defer report execution to off-business hours.
Answer: A,B
Explanation:
The best way to address the performance issues and time-outs faced by the Store Manager team is to configure a dedicated virtual warehouse for them and make it multi-clustered. This will allow them to run their reports independently from other workloads and scale up or down the compute resources as needed. A dedicated virtual warehouse will also enable them to apply specific security and access policies for their data.
A multi-clustered virtual warehouse will provide high availability and concurrency for their queries and avoid queuing or throttling.
Using a Business Intelligence tool for in-memory computation may improve performance, but it will not solve the underlying issue of insufficient compute resources in the shared virtual warehouse. It will also introduce additional costs and complexity for the data architecture.
Configuring the virtual warehouse to size 4-XL may increase the performance, but it will also increase the cost and may not be optimal for the workload. It will also not address the concurrency and availability issues that may arise from sharing the virtual warehouse with other workloads.
Advising the Store Manager team to defer report execution to off-business hours may reduce the load on the shared virtual warehouse, but it will also reduce the timeliness and usefulness of the reports for the business.
It will also not guarantee that the performance issues and time-outs will not occur at other times.
References:
Snowflake Architect Training
Snowflake SnowPro Advanced Architect Certification - Preparation Guide
SnowPro Advanced: Architect Exam Study Guide
NEW QUESTION # 151
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. Request a technical ETL user with the sysadmin role.
- B. Request that the two data domains share data using the Data Exchange.
- C. Create a hierarchy between the two read roles.
- D. Use secondary roles for all users.
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 # 152
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. Use the External Tokenization feature to obfuscate sensitive data.
- B. Rewrite SQL queries to eliminate projections of PHI data based on current_role().
- C. Use the Internal Tokenization feature to obfuscate sensitive data.
- D. Use, at minimum, the Business Critical edition of Snowflake.
- E. Avoid sharing data with partner organizations.
- F. Create Dynamic Data Masking policies and apply them to columns that contain PHI.
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 # 153
What considerations apply when using database cloning for data lifecycle management in a development environment? (Select TWO).
- A. The clone inherits all granted privileges of all child objects, including the database.
- B. Any pipes in the source referring to external stages are not cloned.
- C. Any pipes in the source are not cloned.
- D. Any pipes in the source referring to internal stages are not cloned.
- E. The clone inherits all granted privileges of all child objects, excluding the database.
Answer: B,E
Explanation:
Zero-copy cloning in Snowflake creates a metadata-only snapshot of databases, schemas, or tables, enabling rapid environment refreshes without duplicating data. However, not all dependent objects behave identically during cloning.
Pipes that reference external stages are not cloned because external stages depend on credentials and integrations that may not exist or be valid in the cloned environment (Answer C). This is a documented Snowflake behavior and an important consideration when cloning production environments into development.
Regarding privileges, a cloned database inherits privileges granted on child objects (such as schemas and tables), but it does not inherit privileges granted directly on the source database itself (Answer E). This design prevents unintended access escalation and ensures that security must be explicitly re-granted at the database level.
Understanding these nuances is essential for SnowPro Architect candidates when designing environment promotion strategies and managing data lifecycle processes using cloning.
=========
NEW QUESTION # 154
What Snowflake system functions are used to view and or monitor the clustering metadata for a table? (Select TWO).
- A. SYSTEMSCLUSTERING
- B. SYSTEMSCLUSTERING_DEPTH
- C. SYSTEMSCLUSTERING_RATIO
- D. SYSTEMSTABLE_CLUSTERING
- E. SYSTEMSCLUSTERING_INFORMATION
Answer: B,E
Explanation:
The Snowflake system functions used to view and monitor the clustering metadata for a table are:
* SYSTEM$CLUSTERING_INFORMATION
* SYSTEM$CLUSTERING_DEPTH
Comprehensive But Short Explanation:
* The SYSTEM$CLUSTERING_INFORMATION function in Snowflake returns a variety of clustering information for a specified table. This information includes the average clustering depth, total number of micro-partitions, total constant partition count, average overlaps, average depth, and a partition depth histogram. This function allows you to specify either one or multiple columns for which the clustering information is returned, and it returns this data in JSON format.
* The SYSTEM$CLUSTERING_DEPTH function computes the average depth of a table based on specified columns or the clustering key defined for the table. A lower average depth indicates that the table is better clustered with respect to the specified columns. This function also allows specifying columns to calculate the depth, and the values need to be enclosed in single quotes.
References:
* SYSTEM$CLUSTERING_INFORMATION: Snowflake Documentation
* SYSTEM$CLUSTERING_DEPTH: Snowflake Documentation
NEW QUESTION # 155
......
Prepare For Realistic ARA-C01 Dumps PDF - 100% Passing Guarantee: https://passcollection.actual4labs.com/Snowflake/ARA-C01-actual-exam-dumps.html