ARA-C01 Latest Braindumps Ppt & Real ARA-C01 Dumps

What's more, part of that DumpsActual ARA-C01 dumps now are free: https://drive.google.com/open?id=1WKaLd2h_arUmE20J-7deyHDOwVBMO7h2

As we all know, the world does not have two identical leaves. People’s tastes also vary a lot. So we have tried our best to develop the three packages of our ARA-C01 exam braindumps for you to choose. Now we have free demo of the ARA-C01 study materials exactly according to the three packages on the website for you to download before you pay for the ARA-C01 Practice Engine, and the free demos are a small part of the questions and answers. You can check the quality and validity by them.

Snowflake ARA-C01 Certification Exam is a challenging but rewarding experience for those who want to demonstrate their expertise in Snowflake architecture and implementation. It is an essential step in the career progression of Snowflake professionals and provides a significant boost to their professional credibility and earning potential.

>> ARA-C01 Latest Braindumps Ppt <<

100% Pass Quiz Snowflake - ARA-C01 - SnowPro Advanced Architect Certification –Valid Latest Braindumps Ppt

We all know that the importance of the SnowPro Advanced Architect Certification (ARA-C01) certification exam has increased. Many people remain unsuccessful in its ARA-C01 exam because of using invalid ARA-C01 Practice Test material. If you want to avoid failure and loss of money and time, download actual ARA-C01 Questions of DumpsActual.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q27-Q32):

NEW QUESTION # 27
An Architect runs the following SQL query:

How can this query be interpreted?

A. FILERONS is the file format location. FILE_ROW_NUMBER is a stage.
B. FILEROWS is a stage. FILE_ROW_NUMBER is line number in file.
C. FILEROWS is the table. FILE_ROW_NUMBER is the line number in the table.
D. FILEROWS is a file. FILE_ROW_NUMBER is the file format location.

Answer: B

Explanation:
* A stage is a named location in Snowflake that can store files for data loading and unloading. A stage can be internal or external, depending on where the files are stored.
* The query in the question uses the LIST function to list the files in a stage named FILEROWS. The function returns a table with various columns, including FILE_ROW_NUMBER, which is the line number of the file in the stage.
* Therefore, the query can be interpreted as listing the files in a stage named FILEROWS and showing the line number of each file in the stage.
References:
* : Stages
* : LIST Function

NEW QUESTION # 28
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. 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.
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 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.
D. All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion.
E. The copy into command with a task scheduled to run every second should be used to achieve the near- real time requirement.

Answer: A,C

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 # 29
An Architect is designing a pipeline to stream event data into Snowflake using the Snowflake Kafka connector. The Architect's highest priority is to configure the connector to stream data in the MOST cost-effective manner.
Which of the following is recommended for optimizing the cost associated with the Snowflake Kafka connector?

A. Utilize a lower Buffer.count.records in the connector configuration.
B. Utilize a lower Buffer.size.bytes in the connector configuration.
C. Utilize a higher Buffer.flush.time in the connector configuration.
D. Utilize a higher Buffer.size.bytes in the connector configuration.

Answer: C

Explanation:
The minimum value supported for the buffer.flush.time property is 1 (in seconds). For higher average data flow rates, we suggest that you decrease the default value for improved latency. If cost is a greater concern than latency, you could increase the buffer flush time. Be careful to flush the Kafka memory buffer before it becomes full to avoid out of memory exceptions. https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-kafka

NEW QUESTION # 30
Which organization-related tasks can be performed by the ORGADMIN role? (Choose three.)

A. Creating an account
B. Changing the name of an account
C. Viewing a list of organization accounts
D. Deleting an account
E. Changing the name of the organization
F. Enabling the replication of a database

Answer: A,C,F

Explanation:
Explanation
According to the SnowPro Advanced: Architect documents and learning resources, the organization-related tasks that can be performed by the ORGADMIN role are:
* Creating an account in the organization. A user with the ORGADMIN role can use the CREATE ACCOUNT command to create a new account that belongs to the same organization as the current account1.
* Viewing a list of organization accounts. A user with the ORGADMIN role can use the SHOW ORGANIZATION ACCOUNTS command to view the names and properties of all accounts in the organization2. Alternatively, the user can use the Admin a Accounts page in the web interface to view the organization name and account names3.
* Enabling the replication of a database. A user with the ORGADMIN role can use the SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER function to enable database replication for an account in the organization. This allows the user to replicate databases across accounts in different regions and cloud platforms for data availability and durability4.
The other options are incorrect because they are not organization-related tasks that can be performed by the ORGADMIN role. Option A is incorrect because changing the name of the organization is not a task that can be performed by the ORGADMIN role. To change the name of an organization, the user must contact Snowflake Support3. Option D is incorrect because changing the name of an account is not a task that can be performed by the ORGADMIN role. To change the name of an account, the user must contact Snowflake Support5. Option E is incorrect because deleting an account is not a task that can be performed by the ORGADMIN role. To delete an account, the user must contact Snowflake Support. References: CREATE ACCOUNT | Snowflake Documentation, SHOW ORGANIZATION ACCOUNTS | Snowflake Documentation, Getting Started with Organizations | Snowflake Documentation, SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER | Snowflake Documentation, ALTER ACCOUNT | Snowflake Documentation, [DROP ACCOUNT | Snowflake Documentation]

NEW QUESTION # 31
Which data models can be used when modeling tables in a Snowflake environment? (Select THREE).

A. Bayesian hierarchical model
B. Data lake
C. Graph model
D. Data vault
E. Dimensional/Kimball
F. lnmon/3NF

Answer: D,E,F

Explanation:
Snowflake is a cloud data platform that supports various data models for modeling tables in a Snowflake environment. The data models can be classified into two categories: dimensional and normalized.
Dimensional data models are designed to optimize query performance and ease of use for business intelligence and analytics. Normalized data models are designed to reduce data redundancy and ensure data integrity for transactional and operational systems. The following are some of the data models that can be used in Snowflake:
* Dimensional/Kimball: This is a popular dimensional data model that uses a star or snowflake schema to organize data into fact and dimension tables. Fact tables store quantitative measures and foreign keys to dimension tables. Dimension tables store descriptive attributes and hierarchies. A star schema has a single denormalized dimension table for each dimension, while a snowflake schema has multiple normalized dimension tables for each dimension. Snowflake supports both star and snowflake schemas, and allows users to create views and joins to simplify queries.
* Inmon/3NF: This is a common normalized data model that uses a third normal form (3NF) schema to organize data into entities and relationships. 3NF schema eliminates data duplication and ensures data consistency by applying three rules: 1) every column in a table must depend on the primary key, 2) every column in a table must depend on the whole primary key, not a part of it, and 3) every column in a table must depend only on the primary key, not on other columns. Snowflake supports 3NF schema and allows users to create referential integrity constraints and foreign key relationships to enforce data quality.
* Data vault: This is a hybrid data model that combines the best practices of dimensional and normalized data models to create a scalable, flexible, and resilient data warehouse. Data vault schema consists of three types of tables: hubs, links, and satellites. Hubs store business keys and metadata for each entity.
Links store associations and relationships between entities. Satellites store descriptive attributes and historical changes for each entity or relationship. Snowflake supports data vault schema and allows users to leverage its features such as time travel, zero-copy cloning, and secure data sharing to implement data vault methodology.
What is Data Modeling? | Snowflake, Snowflake Schema in Data Warehouse Model - GeeksforGeeks, [Data Vault 2.0 Modeling with Snowflake]

NEW QUESTION # 32
......

DumpsActual have made customizable Snowflake ARA-C01 practice tests so that users can take unlimited tests and improve Snowflake ARA-C01 exam preparation day by day. These ARA-C01 practice tests are based on the real examination scenario so the students can feel the pressure and learn to deal with it. The customers can access the result of their previous given ARA-C01 Exam history and try not to make any excessive mistakes in the future.

Real ARA-C01 Dumps: https://www.dumpsactual.com/ARA-C01-actualtests-dumps.html

BONUS!!! Download part of DumpsActual ARA-C01 dumps for free: https://drive.google.com/open?id=1WKaLd2h_arUmE20J-7deyHDOwVBMO7h2

Will Hall Will Hall

Biography

ARA-C01 Latest Braindumps Ppt & Real ARA-C01 Dumps

100% Pass Quiz Snowflake - ARA-C01 - SnowPro Advanced Architect Certification –Valid Latest Braindumps Ppt

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q27-Q32):

Archives