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Snowflake ARA-C01 Exam Sample Questions

Question # 1

A company's Architect needs to find an efficient way to get data from an external partner, who is also a Snowflake user. The current solution is based on daily JSON extracts that are placed on an FTP server and uploaded to Snowflake manually. The files are changed several times each month, and the ingestion process needs to be adapted to accommodate these changes. What would be the MOST efficient solution?

A. Ask the partner to create a share and add the company's account.

B. Ask the partner to use the data lake export feature and place the data into cloud storage where Snowflake can natively ingest it (schema-on-read).

C. Keep the current structure but request that the partner stop changing files, instead only appending new files.

D. Ask the partner to set up a Snowflake reader account and use that account to get the data for ingestion.

Answer

A. Ask the partner to create a share and add the company's account.

Explanation:
The most efficient solution is to ask the partner to create a share and add the company’s account (Option A). This way, the company can access the live data from the partner without any data movement or manual intervention. Snowflake’s secure data sharing feature allows data providers to share selected objects in a database with other Snowflake accounts. The shared data is read-only and does not incur any storage or compute costs for the data consumers. The data consumers can query the shared data directly or create local copies of the shared objects in their own databases.
Option B is not efficient because it involves using the data lake export feature, which is intended for exporting data from Snowflake to an external data lake, not for importing data from another Snowflake account. The data lake export feature also requires the data provider to create an external stage on cloud storage and use the COPY INTO command to export the data into parquet files. The data consumer then needs to create an external table or a file format to load the data from the cloud storage into Snowflake. This process can be complex and costly, especially if the data changes frequently.
Option C is not efficient because it does not solve the problem of manual data ingestion and adaptation. Keeping the current structure of daily JSON extracts on an FTP server and requesting the partner to stop changing files, instead only appending new files, does not improve the efficiency or reliability of the data ingestion process. The company still needs to upload the data to Snowflake manually and deal with any schema changes or data quality issues.
Option D is not efficient because it requires the partner to set up a Snowflake reader account and use that account to get the data for ingestion. A reader account is a special type of account that can only consume data from the provider account that created it. It is intended for data consumers who are not Snowflake customers and do not have a licensing agreement with Snowflake. A reader account is not suitable for data ingestion from another Snowflake account, as it does not allow uploading, modifying, or unloading data. The company would need to use external tools or interfaces to access the data from the reader account and load it into their own account, which can be slow and expensive.

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Question # 2

Which of the following are characteristics of how row access policies can be applied to external tables? (Choose three.)

A. An external table can be created with a row access policy, and the policy can be applied to the VALUE column.

B. A row access policy can be applied to the VALUE column of an existing external table.

C. A row access policy cannot be directly added to a virtual column of an external table.

D. External tables are supported as mapping tables in a row access policy.

E. While cloning a database, both the row access policy and the external table will be cloned.

Answer

A. An external table can be created with a row access policy, and the policy can be applied to the VALUE column.
B. A row access policy can be applied to the VALUE column of an existing external table.
C. A row access policy cannot be directly added to a virtual column of an external table.

Explanation:
These three statements are true according to the Snowflake documentation and the web search results. A row access policy is a feature that allows filtering rows based on user-defined conditions. A row access policy can be applied to an external table, which is a table that reads data from external files in a stage. However, there are some limitations and considerations for using row access policies with external tables.

An external table can be created with a row access policy by using the WITH ROW ACCESS POLICY clause in the CREATE EXTERNAL TABLE statement. The policy can be applied to the VALUE column, which is the column that contains the raw data from the external files in a VARIANT data type.

A row access policy can also be applied to the VALUE column of an existing external table by using the ALTER TABLE statement with the SET ROW ACCESS POLICY clause.

A row access policy cannot be directly added to a virtual column of an external table. A virtual column is a column that is derived from the VALUE column using an expression. To apply a row access policy to a virtual column, the policy must be applied to the VALUE column and the expression must be repeated in the policy definition.

External tables are not supported as mapping tables in a row access policy. A mapping table is a table that is used to determine the access rights of users or roles based on some criteria. Snowflake does not support using an external table as a mapping table because it may cause performance issues or errors.

While cloning a database, Snowflake clones the row access policy, but not the external table. Therefore, the policy in the cloned database refers to a table that is not present in the cloned database. To avoid this issue, the external table must be manually cloned or recreated in the cloned database.

A row access policy can be applied to a view created on top of an external table. The policy can be applied to the view itself or to the underlying external table. However, if the policy is applied to the view, the view must be a secure view, which is a view that hides the underlying data and the view definition from unauthorized users.

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Question # 3

How is the change of local time due to daylight savings time handled in Snowflake tasks? (Choose two.)

A. A task scheduled in a UTC-based schedule will have no issues with the time changes.

B. Task schedules can be designed to follow specified or local time zones to accommodate the time changes.

C. A task will move to a suspended state during the daylight savings time change.

D. A frequent task execution schedule like minutes may not cause a problem, but will affect the task history.

E. A task schedule will follow only the specified time and will fail to handle lost or duplicated hours.

Answer

A. A task scheduled in a UTC-based schedule will have no issues with the time changes.
B. Task schedules can be designed to follow specified or local time zones to accommodate the time changes.

Explanation:
According to the Snowflake documentation1 and the web search results2, these two statements are true about how the change of local time due to daylight savings time is handled in Snowflake tasks. A task is a feature that allows scheduling and executing SQL statements or stored procedures in Snowflake. A task can be scheduled using a cron expression that specifies the frequency and time zone of the task execution.
A task scheduled in a UTC-based schedule will have no issues with the time changes. UTC is a universal time standard that does not observe daylight savings time. Therefore, a task that uses UTC as the time zone will run at the same time throughout the year, regardless of the local time changes1.
Task schedules can be designed to follow specified or local time zones to accommodate the time changes. Snowflake supports using any valid IANA time zone identifier in the cron expression for a task. This allows the task to run according to the local time of the specified time zone, which may include daylight savings time adjustments. For example, a task that uses Europe/London as the time zone will run one hour earlier or later when the local time switches between GMT and BST12.

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Question # 4

How does a standard virtual warehouse policy work in Snowflake?

A. It conserves credits by keeping running clusters fully loaded rather than starting additional clusters.

B. It starts only if the system estimates that there is a query load that will keep the cluster busy for at least 6 minutes.

C. It starts only if the system estimates that there is a query load that will keep the cluster busy for at least 2 minutes.

D. It prevents or minimizes queuing by starting additional clusters instead of conserving credits.

Answer

D. It prevents or minimizes queuing by starting additional clusters instead of conserving credits.

Explanation: A standard virtual warehouse policy is one of the two scaling policies available for multi-cluster warehouses in Snowflake. The other policy is economic. A standard policy aims to prevent or minimize queuing by starting additional clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This policy can improve query performance and concurrency, but it may also consume more credits than an economic policy, which tries to conserve credits by keeping the running clusters fully loaded before starting additional clusters. The scaling policy can be set when creating or modifying a warehouse, and it can be changed at any time.

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Question # 5

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. Use a Business Intelligence tool for in-memory computation to improve performance.

B. Configure a dedicated virtual warehouse for the Store Manager team.

C. Configure the virtual warehouse to be multi-clustered.

D. Configure the virtual warehouse to size 4-XL

E. Advise the Store Manager team to defer report execution to off-business hours.

Answer

B. Configure a dedicated virtual warehouse for the Store Manager team.
C. Configure the virtual warehouse to be multi-clustered.

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.

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