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Snowflake ARA-C01: SnowPro Advanced Architect Certification is a highly sought-after certification exam that tests the advanced architectural knowledge of Snowflake Data Cloud architects. ARA-C01 Exam is designed for individuals who have a deep understanding of Snowflake's architecture, best practices, and design principles. SnowPro Advanced Architect Certification certification is a testament to the candidate's expertise in designing and implementing complex Snowflake architectures that meet the needs of modern data-driven organizations.
NEW QUESTION # 58
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?
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 # 59
Which query will identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse to improve the performance of a particular workload?
Answer: D
Explanation:
The correct answer is option B. This query is designed to assess the need for a multi-cluster warehouse by examining the queuing time (AVG_QUEUED_LOAD) on different days and virtual warehouses. When the AVG_QUEUED_LOAD is greater than zero, it suggests that queries are waiting for resources, which can be an indicator that performance might be improved by using a multi-cluster warehouse to handle the workload more efficiently. By grouping by date and warehouse name and filtering on the sum of the average queued load being greater than zero, the query identifies specific days and warehouses where the workload exceeded the available compute resources. This information is valuable when considering scaling out warehouses to multi-cluster configurations for improved performance.
NEW QUESTION # 60
Which technique will efficiently ingest and consume semi-structured data for Snowflake data lake workloads?
Answer: B
Explanation:
Option C is the correct answer because schema-on-read is a technique that allows Snowflake to ingest and consume semi-structured data without requiring a predefined schema. Snowflake supports various semi-structured data formats such as JSON, Avro, ORC, Parquet, and XML, and provides native data types (ARRAY, OBJECT, and VARIANT) for storing them. Snowflake also provides native support for querying semi-structured data using SQL and dot notation. Schema-on-read enables Snowflake to query semi-structured data at the same speed as performing relational queries while preserving the flexibility of schema-on-read. Snowflake's near-instant elasticity rightsizes compute resources, and consumption-based pricing ensures you only pay for what you use.
Option A is incorrect because IDEF1X is a data modeling technique that defines the structure and constraints of relational data using diagrams and notations. IDEF1X is not suitable for ingesting and consuming semi-structured data, which does not have a fixed schema or structure.
Option B is incorrect because schema-on-write is a technique that requires defining a schema before loading and processing data. Schema-on-write is not efficient for ingesting and consuming semi-structured data, which may have varying or complex structures that are difficult to fit into a predefined schema. Schema-on-write also introduces additional overhead and complexity for data transformation and validation.
Option D is incorrect because information schema is a set of metadata views that provide information about the objects and privileges in a Snowflake database. Information schema is not a technique for ingesting and consuming semi-structured data, but rather a way of accessing metadata about the data.
Reference:
Semi-structured Data
Snowflake for Data Lake
NEW QUESTION # 61
A company is designing high availability and disaster recovery plans and needs to maximize redundancy and minimize recovery time objectives for their critical application processes. Cost is not a concern as long as the solution is the best available. The plan so far consists of the following steps:
1. Deployment of Snowflake accounts on two different cloud providers.
2. Selection of cloud provider regions that are geographically far apart.
3. The Snowflake deployment will replicate the databases and account data between both cloud provider accounts.
4. Implementation of Snowflake client redirect.
What is the MOST cost-effective way to provide the HIGHEST uptime and LEAST application disruption if there is a service event?
Answer: B
Explanation:
To provide the highest uptime and least application disruption in case of a service event, the best option is to use the Business Critical Snowflake edition and connect the applications using the <organization_name>-
<accountLocator> URL. The Business Critical Snowflake edition offers the highest level of security, performance, and availability for Snowflake accounts. It includes features such as customer-managed encryption keys, HIPAA compliance, and 4-hour RPO and RTO SLAs. It also supports account replication and failover across regions and cloud platforms, which enables business continuity and disaster recovery. By using the <organization_name>-<accountLocator> URL, the applications can leverage the Snowflake Client Redirect feature, which automatically redirects the client connections to the secondary account in case of a failover. This way, the applications can seamlessly switch to the backup account without any manual intervention or configuration changes. The other options are less cost-effective or less reliable because they either use a lower edition of Snowflake, which does not support account replication and failover, or they use the <organization_name>-<connection_name> URL, which does not support client redirect and requires manual updates to the connection string in case of a failover. References:
* [Snowflake Editions] 1
* [Replication and Failover/Failback] 2
* [Client Redirect] 3
* [Snowflake Account Identifiers] 4
NEW QUESTION # 62
While choosing a cluster key, what is recommended by snowflake?
Answer: B,C
NEW QUESTION # 63
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