Exam Snowflake ARA-C01 Dumps - ARA-C01 Exam Outline

P.S. Free 2025 Snowflake ARA-C01 dumps are available on Google Drive shared by Actual4dump: https://drive.google.com/open?id=11ZA2CVMoG4IkaO_wpRmDo_jXgHBZs0Vr

Our ARA-C01 PDF format is also an effective format to do test preparation. In your spare time, you can easily use the ARA-C01 dumps PDF file for study or revision. The PDF file of Snowflake ARA-C01 real questions is convenient and manageable. These Snowflake ARA-C01 Questions are also printable, giving you the option of paper study since some Snowflake ARA-C01 applicants prefer off-screen preparation rather than on a screen.

Snowflake ARA-C01 certification exam is not for the faint-hearted. It is a rigorous and challenging exam that requires a deep understanding of Snowflake architecture, data modeling, performance optimization, security, and administration. ARA-C01 exam consists of 60 multiple-choice questions that must be completed within 120 minutes. The passing score for the ARA-C01 exam is 80%, and candidates who pass the exam are awarded the SnowPro Advanced Architect Certification.

The SnowPro Advanced Architect Certification exam is designed to test the candidate's ability to design, implement, and manage secure, scalable, and reliable Snowflake solutions. ARA-C01 Exam covers a broad range of topics, including Snowflake architecture, data modeling, security, performance optimization, scalability, and migration. Successful candidates will demonstrate their ability to design and implement Snowflake solutions that meet the complex data management needs of organizations.

>> Exam Snowflake ARA-C01 Dumps <<

ARA-C01 Exam Outline & ARA-C01 Latest Dumps Book

Actual4dump expect to design such an efficient study plan to help you build a high efficient learning attitude for your further development. Our ARA-C01 study torrent are cater every candidate no matter you are a student or office worker, a green hand or a staff member of many years' experience. Therefore, you have no need to worry about whether you can pass the ARA-C01 Exam, because we guarantee you to succeed with our technology strength. The language of our ARA-C01 exam questions are easy to follow and the pass rate of our ARA-C01 learning guide is as high as 99% to 100%.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q51-Q56):

NEW QUESTION # 51
An Architect is designing a data lake with Snowflake. The company has structured, semi-structured, and unstructured data. The company wants to save the data inside the data lake within the Snowflake system. The company is planning on sharing data among its corporate branches using Snowflake data sharing.
What should be considered when sharing the unstructured data within Snowflake?

A. A scoped URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with a 24-hour time limit for the URL.
B. A file URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with a 7-day time limit for the URL.
C. A file URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with the "expiration_time" argument defined for the URL time limit.
D. A pre-signed URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with no time limit for the URL.

Answer: A

Explanation:
When sharing unstructured data within Snowflake, using a scoped URL is recommended. Scoped URLs provide temporary access to staged files without granting privileges to the stage itself, enhancing security. The URL expires when the persisted query result period ends, which is currently set to 24 hours. This approach is suitable for sharing unstructured data over secure views within Snowflake's data sharing framework.

NEW QUESTION # 52
A table contains five columns and it has millions of records. The cardinality distribution of the columns is shown below:

Column C4 and C5 are mostly used by SELECT queries in the GROUP BY and ORDER BY clauses.
Whereas columns C1, C2 and C3 are heavily used in filter and join conditions of SELECT queries.
The Architect must design a clustering key for this table to improve the query performance.
Based on Snowflake recommendations, how should the clustering key columns be ordered while defining the multi-column clustering key?

A. C2, C1, C3
B. C1, C3, C2
C. C5, C4, C2
D. C3, C4, C5

Answer: B

Explanation:
Explanation
According to the Snowflake documentation, the following are some considerations for choosing clustering for a table1:
* Clustering is optimal when either:
* You require the fastest possible response times, regardless of cost.
* Your improved query performance offsets the credits required to cluster and maintain the table.
* Clustering is most effective when the clustering key is used in the following types of query predicates:
* Filter predicates (e.g. WHERE clauses)
* Join predicates (e.g. ON clauses)
* Grouping predicates (e.g. GROUP BY clauses)
* Sorting predicates (e.g. ORDER BY clauses)
* Clustering is less effective when the clustering key is not used in any of the above query predicates, or when the clustering key is used in a predicate that requires a function or expression to be applied to the key (e.g. DATE_TRUNC, TO_CHAR, etc.).
* For most tables, Snowflake recommends a maximum of 3 or 4 columns (or expressions) per key.
Adding more than 3-4 columns tends to increase costs more than benefits.
Based on these considerations, the best option for the clustering key columns is C. C1, C3, C2, because:
* These columns are heavily used in filter and join conditions of SELECT queries, which are the most effective types of predicates for clustering.
* These columns have high cardinality, which means they have many distinct values and can help reduce the clustering skew and improve the compression ratio.
* These columns are likely to be correlated with each other, which means they can help co-locate similar rows in the same micro-partitions and improve the scan efficiency.
* These columns do not require any functions or expressions to be applied to them, which means they can be directly used in the predicates without affecting the clustering.
References: 1: Considerations for Choosing Clustering for a Table | Snowflake Documentation

NEW QUESTION # 53
When using the copy into <table> command with the CSV file format, how does the match_by_column_name parameter behave?

A. The parameter will be ignored.
B. The command will return a warning stating that the file has unmatched columns.
C. The command will return an error.
D. It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name.

Answer: A

Explanation:
Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2.
Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.
Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets.
PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.
Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
References: 1: Snowpipe Overview 2: Using Streams and Tasks to Automate Data Pipelines 3: External Functions Overview 4: Snowflake Data Marketplace Overview : [Loading Data Using COPY INTO] : [What is Amazon EMR?] : [PySpark Overview]
* The copy into <table> command is used to load data from staged files into an existing table in Snowflake. The command supports various file formats, such as CSV, JSON, AVRO, ORC, PARQUET, and XML1.
* The match_by_column_name parameter is a copy option that enables loading semi-structured data into separate columns in the target table that match corresponding columns represented in the source data. The parameter can have one of the following values2:
* CASE_SENSITIVE: The column names in the source data must match the column names in the target table exactly, including the case. This is the default value.
* CASE_INSENSITIVE: The column names in the source data must match the column names in the target table, but the case is ignored.
* NONE: The column names in the source data are ignored, and the data is loaded based on the order of the columns in the target table.
* The match_by_column_name parameter only applies to semi-structured data, such as JSON, AVRO, ORC, PARQUET, and XML. It does not apply to CSV data, which is considered structured data2.
* When using the copy into <table> command with the CSV file format, the match_by_column_name parameter behaves as follows2:
* It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name. This means that the first row of the CSV file must contain the column names, and they must match the column names in the target table exactly, including the case. If the header is missing or does not match, the command will return an error.
* The parameter will not be ignored, even if it is set to NONE. The command will still try to match the column names in the CSV file with the column names in the target table, and will return an error if they do not match.
* The command will not return a warning stating that the file has unmatched columns. It will either load the data successfully if the column names match, or return an error if they do not match.
References:
* 1: COPY INTO <table> | Snowflake Documentation
* 2: MATCH_BY_COLUMN_NAME | Snowflake Documentation

NEW QUESTION # 54
How does a standard virtual warehouse policy work in Snowflake?

A. It starts only f the system estimates that there is a query load that will keep the cluster busy for at least 2 minutes.
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 conserves credits by keeping running clusters fully loaded rather than starting additional clusters.
D. It prevents or minimizes queuing by starting additional clusters instead of conserving credits.

Answer: D

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.
Reference:
Snowflake Documentation: Multi-cluster Warehouses
Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses

NEW QUESTION # 55
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. Share the primary database with an account in the same region that the database will be replicated to.
B. Create a clone of the primary database then replicate the database.
C. Replicate the database ensuring the replicated database is in the same region as the external tables.
D. Move the external tables to a database that is not replicated, then replicate the primary database.

Answer: D

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 # 56
......

There is a high demand for Snowflake Development certification, therefore there is an increase in the number of Snowflake ARA-C01 exam candidates. Many resources are available on the internet to prepare for the SnowPro Advanced Architect Certification exam. Actual4dump is one of the best certification exam preparation material providers where you can find newly released Snowflake ARA-C01 Dumps for your exam preparation. With years of experience in compiling top-notch relevant Snowflake ARA-C01 dumps questions, we also offer the Snowflake ARA-C01 practice test (online and offline) to help you get familiar with the actual exam environment.

ARA-C01 Exam Outline: https://www.actual4dump.com/Snowflake/ARA-C01-actualtests-dumps.html

P.S. Free & New ARA-C01 dumps are available on Google Drive shared by Actual4dump: https://drive.google.com/open?id=11ZA2CVMoG4IkaO_wpRmDo_jXgHBZs0Vr

Jack Shaw Jack Shaw

Biografie

Exam Snowflake ARA-C01 Dumps - ARA-C01 Exam Outline

ARA-C01 Exam Outline & ARA-C01 Latest Dumps Book

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q51-Q56):

Links

Informatie

Support