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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.
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An Architect on a new project has been asked to design an architecture that meets Snowflake security, compliance, and governance requirements as follows:
1) Use Tri-Secret Secure in Snowflake
2) Share some information stored in a view with another Snowflake customer
3) Hide portions of sensitive information from some columns
4) Use zero-copy cloning to refresh the non-production environment from the production environment To meet these requirements, which design elements must be implemented? (Choose three.)
A. Define row access policies.
B. Create a secure view.
C. Use the Enterprise edition of Snowflake.
D. Create a materialized view.
E. Use Dynamic Data Masking.
F. Use the Business Critical edition of Snowflake.
Answer: D,E,F
NEW QUESTION # 148
A Snowflake Architect is setting up database replication to support a disaster recovery plan. The primary database has external tables.
How should the database be replicated?
A. Move the external tables to a database that is not replicated, then replicate the primary database.
B. Replicate the database ensuring the replicated database is in the same region as the external tables.
C. Share the primary database with an account in the same region that the database will be replicated to.
D. Create a clone of the primary database then replicate the database.
Answer: A
Explanation:
Database replication is a feature that allows you to create a copy of a database in another account, region, or cloud platform for disaster recovery or business continuity purposes. However, not all database objects can be replicated. External tables are one of the exceptions, as they reference data files stored in an external stage that is not part of Snowflake. Therefore, to replicate a database that contains external tables, you need to move the external tables to a separate database that is not replicated, and then replicate the primary database that contains the other objects. This way, you can avoid replication errors and ensure consistency between the primary and secondary databases. The other options are incorrect because they either do not address the issue of external tables, or they use an alternative method that is not supported by Snowflake. You cannot create a clone of the primary database and then replicate it, as replication only works on the original database, not on its clones. You also cannot share the primary database with another account, as sharing is a different feature that does not create a copy of the database, but rather grants access to the shared objects. Finally, you do not need to ensure that the replicated database is in the same region as the external tables, as external tables can access data files stored in any region or cloud platform, as long as the stage URL is valid and accessible. Reference:
[Replication and Failover/Failback] 1
[Introduction to External Tables] 2
[Working with External Tables] 3
[Replication : How to migrate an account from One Cloud Platform or Region to another in Snowflake] 4
NEW QUESTION # 149
An Architect has a VPN_ACCESS_LOGS table in the SECURITY_LOGS schema containing timestamps of the connection and disconnection, username of the user, and summary statistics.
What should the Architect do to enable the Snowflake search optimization service on this table?
A. Assume role with ALL PRIVILEGES including ADD SEARCH OPTIMIZATION in the SECURITY LOGS schema.
B. Assume role with OWNERSHIP on future tables and ADD SEARCH OPTIMIZATION on the SECURITY_LOGS schema.
C. Assume role with ALL PRIVILEGES on VPN_ACCESS_LOGS and ADD SEARCH OPTIMIZATION in the SECURITY_LOGS schema.
D. Assume role with OWNERSHIP on VPN_ACCESS_LOGS and ADD SEARCH OPTIMIZATION in the SECURITY_LOGS schema.
Answer: D
Explanation:
According to the SnowPro Advanced: Architect Exam Study Guide, to enable the search optimization service on a table, the user must have the ADD SEARCH OPTIMIZATION privilege on the table and the schema.
The privilege can be granted explicitly or inherited from a higher-level object, such as a database or a role. The OWNERSHIP privilege on a table implies the ADD SEARCH OPTIMIZATION privilege, so the user who owns the table can enable the search optimization service on it. Therefore, the correct answer is to assume a role with OWNERSHIP on VPN_ACCESS_LOGS and ADD SEARCH OPTIMIZATION in the SECURITY_LOGS schema. This will allow the user to enable the search optimization service on the VPN_ACCESS_LOGS table and any future tables created in the SECURITY_LOGS schema. The other options are incorrect because they either grant excessive privileges or do not grant the required privileges on the table or the schema. References:
* SnowPro Advanced: Architect Exam Study Guide, page 11, section 2.3.1
* Snowflake Documentation: Enabling the Search Optimization Service
NEW QUESTION # 150
A company is storing large numbers of small JSON files (ranging from 1-4 bytes) that are received from IoT devices and sent to a cloud provider. In any given hour, 100,000 files are added to the cloud provider.
What is the MOST cost-effective way to bring this data into a Snowflake table?
A. A pipe
B. A stream
C. A copy command at regular intervals
D. An external table
Answer: A
Explanation:
A pipe is a Snowflake object that continuously loads data from files in a stage (internal or external) into a table. A pipe can be configured to use auto-ingest, which means that Snowflake automatically detects new or modified files in the stage and loads them into the table without any manual intervention1.
A pipe is the most cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it minimizes the number of COPY commands executed and the number of micro-partitions created. A pipe can use file aggregation, which means that it can combine multiple small files into a single larger file before loading them into the table. This reduces the load time and the storage cost of the data2.
An external table is a Snowflake object that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table does not store the data in Snowflake, but only provides a view of the data for querying. An external table is not a cost-effective way to bring data into a Snowflake table, because it does not support file aggregation, and it requires additional network bandwidth and compute resources to query the external data3.
A stream is a Snowflake object that records the history of changes (inserts, updates, and deletes) made to a table. A stream can be used to consume the changes from a table and apply them to another table or a task. A stream is not a way to bring data into a Snowflake table, but a way to process the data after it is loaded into a table4.
A copy command is a Snowflake command that loads data from files in a stage into a table. A copy command can be executed manually or scheduled using a task. A copy command is not a cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it does not support file aggregation, and it may create many micro-partitions that increase the storage cost of the data5.
NEW QUESTION # 151
A retail company has over 3000 stores all using the same Point of Sale (POS) system. The company wants to deliver near real-time sales results to category managers. The stores operate in a variety of time zones and exhibit a dynamic range of transactions each minute, with some stores having higher sales volumes than others.
Sales results are provided in a uniform fashion using data engineered fields that will be calculated in a complex data pipeline. Calculations include exceptions, aggregations, and scoring using external functions interfaced to scoring algorithms. The source data for aggregations has over 100M rows.
Every minute, the POS sends all sales transactions files to a cloud storage location with a naming convention that includes store numbers and timestamps to identify the set of transactions contained in the files. The files are typically less than 10MB in size.
How can the near real-time results be provided to the category managers? (Select TWO).
A. All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion.
B. An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs.
C. A Snowpipe should be created and configured with AUTO_INGEST = true. A stream should be created to process INSERTS into a single target table using the stream metadata to inform the store number and timestamps.
D. The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement.
E. A stream should be created to accumulate the near real-time data and a task should be created that runs at a frequency that matches the real-time analytics needs.
Answer: C,E
Explanation:
To provide near real-time sales results to category managers, the Architect can use the following steps:
* Create an external stage that references the cloud storage location where the POS sends the sales transactions files. The external stage should use the file format and encryption settings that match the source files2
* Create a Snowpipe that loads the files from the external stage into a target table in Snowflake. The Snowpipe should be configured with AUTO_INGEST = true, which means that it will automatically detect and ingest new files as they arrive in the external stage. The Snowpipe should also use a copy
* option to purge the files from the external stage after loading, to avoid duplicate ingestion3
* Create a stream on the target table that captures the INSERTS made by the Snowpipe. The stream should include the metadata columns that provide information about the file name, path, size, and last modified time. The stream should also have a retention period that matches the real-time analytics needs4
* Create a task that runs a query on the stream to process the near real-time data. The query should use the stream metadata to extract the store number and timestamps from the file name and path, and perform the calculations for exceptions, aggregations, and scoring using external functions. The query should also output the results to another table or view that can be accessed by the category managers. The task should be scheduled to run at a frequency that matches the real-time analytics needs, such as every minute or every 5 minutes.
The other options are not optimal or feasible for providing near real-time results:
* All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion. This option is not recommended because it would introduce additional latency and complexity in the data pipeline.
Concatenating files would require an external process or service that monitors the cloud storage location and performs the file merging operation. This would delay the ingestion of new files into Snowflake and increase the risk of data loss or corruption. Moreover, concatenating files would not avoid micro-ingestion, as Snowpipe would still ingest each concatenated file as a separate load.
* An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs. This option is not necessary because Snowpipe can automatically ingest new files from the external stage without requiring an external trigger or scheduler. Using an external scheduler would add more overhead and dependency to the data pipeline, and it would not guarantee near real-time ingestion, as it would depend on the polling interval and the availability of the external scheduler.
* The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement. This option is not feasible because tasks cannot be scheduled to run every second in Snowflake. The minimum interval for tasks is one minute, and even that is not guaranteed, as tasks are subject to scheduling delays and concurrency limits. Moreover, using the copy into command with a task would not leverage the benefits of Snowpipe, such as automatic file detection, load balancing, and micro-partition optimization. References:
* 1: SnowPro Advanced: Architect | Study Guide
* 2: Snowflake Documentation | Creating Stages
* 3: Snowflake Documentation | Loading Data Using Snowpipe
* 4: Snowflake Documentation | Using Streams and Tasks for ELT
* : Snowflake Documentation | Creating Tasks
* : Snowflake Documentation | Best Practices for Loading Data
* : Snowflake Documentation | Using the Snowpipe REST API
* : Snowflake Documentation | Scheduling Tasks
* : SnowPro Advanced: Architect | Study Guide
* : Creating Stages
* : Loading Data Using Snowpipe
* : Using Streams and Tasks for ELT
* : [Creating Tasks]
* : [Best Practices for Loading Data]
* : [Using the Snowpipe REST API]
* : [Scheduling Tasks]
NEW QUESTION # 152
......
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