Get Latest [Sep-2026] Conduct effective penetration tests using DumpsKing ARA-C01
Penetration testers simulate ARA-C01 exam PDF
NEW QUESTION # 81
Which command do you run to remove files from stage?
- A. PURGE
- B. CLEAN
- C. DELETE
- D. REMOVE
Answer: D
NEW QUESTION # 82
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-minutes range.
- C. Call insertReport every 8 minutes for a 10-minute time range.
- D. Call loadHistoryScan every minute for the maximum time range.
Answer: B
Explanation:
The Snowpipe REST API provides two endpoints for retrieving the data load history: insertReport and loadHistoryScan. The insertReport endpoint returns the status of the files that were submitted to the insertFiles endpoint, while the loadHistoryScan endpoint returns the history of the files that were actually loaded into the table by Snowpipe. To keep a log of data load history, it is recommended to use the loadHistoryScan endpoint, which provides more accurate and complete information about the data ingestion process. The loadHistoryScan endpoint accepts a start time and an end time as parameters, and returns the files that were loaded within that time range. The maximum time range that can be specified is 15 minutes, and the maximum number of files that can be returned is 10,000. Therefore, to keep a log of data load history, the best option is to call the loadHistoryScan endpoint every 10 minutes for a 15-minute time range, and store the results in a log file or a table. This way, the log will capture all the files that were loaded by Snowpipe, and avoid any gaps or overlaps in the time range. The other options are incorrect because:
* Calling insertReport every 20 minutes, fetching the last 10,000 entries, will not provide a complete log of data load history, as some files may be missed or duplicated due to the asynchronous nature of Snowpipe. Moreover, insertReport only returns the status of the files that were submitted, not the files that were loaded.
* Calling loadHistoryScan every minute for the maximum time range will result in too many API calls and unnecessary overhead, as the same files will be returned multiple times. Moreover, the maximum time range is 15 minutes, not 1 minute.
* Calling insertReport every 8 minutes for a 10-minute time range will suffer from the same problems as option A, and also create gaps or overlaps in the time range.
Snowpipe REST API
Option 1: Loading Data Using the Snowpipe REST API
PIPE_USAGE_HISTORY
NEW QUESTION # 83
What is a valid object hierarchy when building a Snowflake environment?
- A. Account --> Database --> Schema --> Warehouse
- B. Account --> Schema > Table --> Stage
- C. Organization --> Account --> Stage --> Table --> View
- D. Organization --> Account --> Database --> Schema --> Stage
Answer: D
Explanation:
This is the valid object hierarchy when building a Snowflake environment, according to the Snowflake documentation and the web search results. Snowflake is a cloud data platform that supports various types of objects, such as databases, schemas, tables, views, stages, warehouses, and more. These objects are organized in a hierarchical structure, as follows:
* Organization: An organization is the top-level entity that represents a group of Snowflake accounts that are related by business needs or ownership. An organization can have one or more accounts, and can enable features such as cross-account data sharing, billing and usage reporting, and single sign-on across accounts12.
* Account: An account is the primary entity that represents a Snowflake customer. An account can have one or more databases, schemas, stages, warehouses, and other objects. An account can also have one or more users, roles, and security integrations. An account is associated with a specific cloud platform, region, and Snowflake edition34.
* Database: A database is a logical grouping of schemas. A database can have one or more schemas, and can store structured, semi-structured, or unstructured data. A database can also have properties such as retention time, encryption, and ownership56.
* Schema: A schema is a logical grouping of tables, views, stages, and other objects. A schema can have one or more objects, and can define the namespace and access control for the objects. A schema can also have properties such as ownership and default warehouse .
* Stage: A stage is a named location that references the files in external or internal storage. A stage can be used to load data into Snowflake tables using the COPY INTO command, or to unload data from Snowflake tables using the COPY INTO LOCATION command. A stage can be created at the account, database, or schema level, and can have properties such as file format, encryption, and credentials .
The other options listed are not valid object hierarchies, because they either omit or misplace some objects in the structure. For example, option A omits the organization level and places the warehouse under the schema level, which is incorrect. Option C omits the organization, account, and stage levels, and places the table under the schema level, which is incorrect. Option D omits the database level and places the stage and table under the account level, which is incorrect.
Snowflake Documentation: Organizations
Snowflake Blog: Introducing Organizations in Snowflake
Snowflake Documentation: Accounts
Snowflake Blog: Understanding Snowflake Account Structures
Snowflake Documentation: Databases
Snowflake Blog: How to Create a Database in Snowflake
[Snowflake Documentation: Schemas]
[Snowflake Blog: How to Create a Schema in Snowflake]
[Snowflake Documentation: Stages]
[Snowflake Blog: How to Use Stages in Snowflake]
NEW QUESTION # 84
A company has several sites in different regions from which the company wants to ingest data.
Which of the following will enable this type of data ingestion?
- A. The company must have a Snowflake account in each cloud region to be able to ingest data to that account.
- B. The company must replicate data between Snowflake accounts.
- C. The company should provision a reader account to each site and ingest the data through the reader accounts.
- D. The company should use a storage integration for the external stage.
Answer: D
NEW QUESTION # 85
An Architect needs to meet a company requirement to ingest files from the company's AWS storage accounts into the company's Snowflake Google Cloud Platform (GCP) account. How can the ingestion of these files into the company's Snowflake account be initiated? (Select TWO).
- A. Configure the client application to issue a COPY INTO <TABLE> command to Snowflake when new files have arrived in Amazon S3 Glacier storage.
- B. Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage.
- C. Create an AWS Lambda function to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage.
- D. Configure AWS Simple Notification Service (SNS) to notify Snowpipe when new files have arrived in Amazon S3 storage.
- E. Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 Glacier storage.
Answer: B,C
Explanation:
Snowpipe is a feature that enables continuous, near-real-time data ingestion from external sources into Snowflake tables. Snowpipe can ingest files from Amazon S3, Google Cloud Storage, or Azure Blob Storage into Snowflake tables on any cloud platform. Snowpipe can be triggered in two ways: by using the Snowpipe REST API or by using cloud notifications2 To ingest files from the company's AWS storage accounts into the company's Snowflake GCP account, the Architect can use either of these methods:
* Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage. This method requires the client application to monitor the S3 buckets for new files and send a request to the Snowpipe REST API with the list of files to ingest. The client application must also handle authentication, error handling, and retry logic3
* Create an AWS Lambda function to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage. This method leverages the AWS Lambda service to execute a function that calls the Snowpipe REST API whenever an S3 event notification is received. The AWS Lambda function must be configured with the appropriate permissions, triggers, and code to invoke the Snowpipe REST API4 The other options are not valid methods for triggering Snowpipe:
* Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 Glacier storage. This option is not feasible because Snowpipe does not support ingesting files from Amazon S3 Glacier storage, which is a long-term archival storage service. Snowpipe only supports ingesting files from Amazon S3 standard storage classes5
* Configure AWS Simple Notification Service (SNS) to notify Snowpipe when new files have arrived in Amazon S3 storage. This option is not applicable because Snowpipe does not support cloud notifications from AWS SNS. Snowpipe only supports cloud notifications from AWS SQS, Google Cloud Pub/Sub, or Azure Event Grid6
* Configure the client application to issue a COPY INTO <TABLE> command to Snowflake when new files have arrived in Amazon S3 Glacier storage. This option is not relevant because it does not use Snowpipe, but rather the standard COPY command, which is a batch loading method. Moreover, the COPY command also does not support ingesting files from Amazon S3 Glacier storage7 References:
* 1: SnowPro Advanced: Architect | Study Guide 8
* 2: Snowflake Documentation | Snowpipe Overview 9
* 3: Snowflake Documentation | Using the Snowpipe REST API 10
* 4: Snowflake Documentation | Loading Data Using Snowpipe and AWS Lambda 11
* 5: Snowflake Documentation | Supported File Formats and Compression for Staged Data Files 12
* 6: Snowflake Documentation | Using Cloud Notifications to Trigger Snowpipe 13
* 7: Snowflake Documentation | Loading Data Using COPY into a Table
* : SnowPro Advanced: Architect | Study Guide
* : Snowpipe Overview
* : Using the Snowpipe REST API
* : Loading Data Using Snowpipe and AWS Lambda
* : Supported File Formats and Compression for Staged Data Files
* : Using Cloud Notifications to Trigger Snowpipe
* : Loading Data Using COPY into a Table
NEW QUESTION # 86
A company is using Snowflake in Azure in the Netherlands. The company analyst team also has data in JSON format that is stored in an Amazon S3 bucket in the AWS Singapore region that the team wants to analyze.
The Architect has been given the following requirements:
1. Provide access to frequently changing data
2. Keep egress costs to a minimum
3. Maintain low latency
How can these requirements be met with the LEAST amount of operational overhead?
- A. Use a materialized view on top of an external table against the S3 bucket in AWS Singapore.
- B. Use AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and an Azure Netherlands Blob storage, then use an external table against the Blob storage.
- C. Copy the data between providers from S3 to Azure Blob storage to collocate, then use Snowpipe for data ingestion.
- D. Use an external table against the S3 bucket in AWS Singapore and copy the data into transient tables.
Answer: D
NEW QUESTION # 87
What are characteristics of Dynamic Data Masking? (Select TWO).
- A. A masking policy can be applied to the value column of an external table.
- B. A masking policy can be applied to a column with the GEOGRAPHY data type.
- C. A single masking policy can be applied to columns in different tables.
- D. A masking policy that Is currently set on a table can be dropped.
- E. The role that creates the masking policy will always see unmasked data In query results
Answer: C,D
Explanation:
Dynamic Data Masking is a feature that allows masking sensitive data in query results based on the role of the user who executes the query. A masking policy is a user-defined function that specifies the masking logic and can be applied to one or more columns in one or more tables. A masking policy that is currently set on a table can be dropped using the ALTER TABLE command. A single masking policy can be applied to columns in different tables using the ALTER TABLE command with the SET MASKING POLICY clause. The other options are either incorrect or not supported by Snowflake. A masking policy cannot be applied to the value column of an external table, as external tables do not support column-level security. The role that creates the masking policy will not always see unmasked data in query results, as the masking policy can be applied to the owner role as well. A masking policy cannot be applied to a column with the GEOGRAPHY data type, as Snowflake only supports masking policies for scalar data types. Reference: Snowflake Documentation: Dynamic Data Masking, Snowflake Documentation: ALTER TABLE
NEW QUESTION # 88
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,C
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 # 89
Which Snowflake architecture recommendation needs multiple Snowflake accounts for implementation?
- A. Enable separation of the development, test, and production environments.
- B. Create external stages pointing to cloud providers and regions other than the region hosting the Snowflake account.
- C. Enable zero-copy cloning among the development, test, and production environments.
- D. Enable a disaster recovery strategy across multiple cloud providers.
Answer: A
Explanation:
The Snowflake architecture recommendation that necessitates multiple Snowflake accounts for implementation is the separation of development, test, and production environments. This approach, known as Account per Tenant (APT), isolates tenants into separate Snowflake accounts, ensuring dedicated resources and security isolation12.
References
*Snowflake's white paper on "Design Patterns for Building Multi-Tenant Applications on Snowflake" discusses the APT model and its requirement for separate Snowflake accounts for each tenant1.
*Snowflake Documentation on Secure Data Sharing, which mentions the possibility of sharing data across multiple accounts3.
NEW QUESTION # 90
What is the MOST efficient way to design an environment where data retention is not considered critical, and customization needs are to be kept to a minimum?
- A. Use a temporary table.
- B. Use a transient database.
- C. Use a transient schema.
- D. Use a transient table.
Answer: B
Explanation:
Transient databases in Snowflake are designed for situations where data retention is not critical, and they do not have the fail-safe period that regular databases have. This means that data in a transient database is not recoverable after the Time Travel retention period. Using a transient database is efficient because it minimizes storage costs while still providing most functionalities of a standard database without the overhead of data protection features that are not needed when data retention is not a concern.
NEW QUESTION # 91
An Architect has chosen to separate their Snowflake Production and QA environments using two separate Snowflake accounts.
The QA account is intended to run and test changes on data and database objects before pushing those changes to the Production account. It is a requirement that all database objects and data in the QA account need to be an exact copy of the database objects, including privileges and data in the Production account on at least a nightly basis.
Which is the LEAST complex approach to use to populate the QA account with the Production account's data and database objects on a nightly basis?
- A. 1) Create a stage in the Production account
2) Create a stage in the QA account that points to the same external object-storage location
3) Create a task that runs nightly to unload each table in the Production account into the stage
4) Use Snowpipe to populate the QA account - B. 1) Create a share in the Production account for each database
2) Share access to the QA account as a Consumer
3) The QA account creates a database directly from each share
4) Create clones of those databases on a nightly basis
5) Run tests directly on those cloned databases - C. 1) Enable replication for each database in the Production account
2) Create replica databases in the QA account
3) Create clones of the replica databases on a nightly basis
4) Run tests directly on those cloned databases - D. 1) In the Production account, create an external function that connects into the QA account and returns all the data for one specific table
2) Run the external function as part of a stored procedure that loops through each table in the Production account and populates each table in the QA account
Answer: C
Explanation:
This approach is the least complex because it uses Snowflake's built-in replication feature to copy the data and database objects from the Production account to the QA account. Replication is a fast and efficient way to synchronize data across accounts, regions, and cloud platforms. It also preserves the privileges and metadata of the replicated objects. By creating clones of the replica databases, the QA account can run tests on the cloned data without affecting the original data.
Clones are also zero-copy, meaning they do not consume any additional storage space unless the data is modified. This approach does not require any external stages, tasks, Snowpipe, or external functions, which can add complexity and overhead to the data transfer process.
Reference:
Introduction to Replication and Failover
Replicating Databases Across Multiple Accounts
Cloning Considerations
NEW QUESTION # 92
A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes.
Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation.
The external vendor is a Snowflake customer.
What solution will MINIMIZE complexity and MAXIMIZE performance?
- A. 1. Create a Snowpipe to bring the JSON data into Snowflake.2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.3. Ask the vendor to expose an API so an external function call can be made to join the vendor's data back to the loT data in a transformation procedure.4. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.5. Give the materialized views access to the dashboard tool.
- B. 1. Create a Snowpipe to bring the JSON data into Snowflake.2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.3. Ask the vendor to create a data share with the required data that is then imported into the Snowflake account.4. Join the vendor's data back to the loT data in a transformation procedure5. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.6. Give the materialized views access to the dashboard tool.
- C. 1. Create an external table over the JSON data in cloud storage.2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp.3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account.
4. Join the vendor's data back to the loT data using a transformation procedure.5. Create views over the larger dataset to perform the aggregations required by the dashboard.6. Give the views access to the dashboard tool. - D. 1. Create an external table over the JSON data in cloud storage.2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp.3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure.4. Give the transformed table access to the dashboard tool.5.
Perform the aggregations on the dashboard tool.
Answer: B
Explanation:
Using Snowpipe for continuous, automated data ingestion minimizes the need for manual intervention and ensures that data is available in Snowflake promptly after it is generated. Leveraging Snowflake's data sharing capabilities allows for efficient and secure access to the vendor's data without the need for complex API integrations. Materialized views provide pre-aggregated data for fast access, which is ideal for dashboards that require high performance1234.
References =
*Snowflake Documentation on Snowpipe4
*Snowflake Documentation on Secure Data Sharing2
*Best Practices for Data Ingestion with Snowflake1
NEW QUESTION # 93
A company is designing its serving layer for data that is in cloud storage. Multiple terabytes of the data will be used for reporting. Some data does not have a clear use case but could be useful for experimental analysis. This experimentation data changes frequently and is sometimes wiped out and replaced completely in a few days.
The company wants to centralize access control, provide a single point of connection for the end-users, and maintain data governance.
What solution meets these requirements while MINIMIZING costs, administrative effort, and development overhead?
- A. Import all the data in cloud storage to be used for reporting into a Snowflake schema with native tables. Then create a role that has access to this schema and manage access to the data through that role.
- B. Import all the data in cloud storage to be used for reporting into a Snowflake schema with native tables. Then create two different roles with grants to the different datasets to match the different user personas, and grant these roles to the corresponding users.
- C. Import the data used for reporting into a Snowflake schema with native tables. Then create views that have SELECT commands pointing to the cloud storage files for the experimentation data. Then create two different roles to match the different user personas, and grant these roles to the corresponding users.
- D. Import the data used for reporting into a Snowflake schema with native tables. Then create external tables pointing to the cloud storage folders used for the experimentation data. Then create two different roles with grants to the different datasets to match the different user personas, and grant these roles to the corresponding users.
Answer: D
Explanation:
The most cost-effective and administratively efficient solution is to use a combination of native and external tables. Native tables for reporting data ensure performance and governance, while external tables allow for flexibility with frequently changing experimental data. Creating roles with specific grants to datasets aligns with the principle of least privilege, centralizing access control and simplifying user management12.
Reference
* Snowflake Documentation on Optimizing Cost1.
* Snowflake Documentation on Controlling Cost2.
NEW QUESTION # 94
You have turned on auto clustering in your table.
How will you monitor how much credit is getting burnt for the auto clustering in the past week for a specific table?
- A. Run the below query select * from table(information_schema.automatic_clustering_history(date_range_start=>dateadd(d, -7, current_date), date_range_end=>current_date, table_name=>'mydb.myschema.mytable'));
- B. SHOW AUTO CLUSTERING INFO
- C. SHOW CLUSTERING INFO
Answer: A
NEW QUESTION # 95
Which statements describe characteristics of the use of materialized views in Snowflake? (Choose two.)
- A. They can include context functions, such as CURRENT_TIME().
- B. They cannot include nested subqueries.
- C. They can support inner joins, but not outer joins.
- D. They can support MIN and MAX aggregates.
- E. They can include ORDER BY clauses.
Answer: A,D
NEW QUESTION # 96
Which of the following are characteristics of Snowflake's parameter hierarchy?
- A. Schema parameters override account parameters.
- B. Virtual warehouse parameters override user parameters.
- C. Session parameters override virtual warehouse parameters.
- D. Table parameters override virtual warehouse parameters.
Answer: B
Explanation:
In Snowflake's parameter hierarchy, virtual warehouse parameters take precedence over user parameters. This hierarchy is designed to ensure that settings at the virtual warehouse level, which typically reflect the requirements of a specific workload or set of queries, override the preferences set at the individual user level. This helps maintain consistent performance and resource utilization as specified by the administrators managing the virtual warehouses.
Reference: Snowflake documentation on parameter hierarchy, found in the SnowPro Advanced: Architect learning materials.
NEW QUESTION # 97
......
Tested Material Used To ARA-C01 Test Engine: https://www.dumpsking.com/ARA-C01-testking-dumps.html
Steps Necessary To Pass The ARA-C01 Exam: https://drive.google.com/open?id=1NL-D3ONCQdRKk8GS73v7mr-AFpYwPqge
