[2025] ARA-C01 Dumps are Available for Instant Access [Q57-Q72]

[2025] ARA-C01 Dumps are Available for Instant Access

Valid ARA-C01 Dumps for Helping Passing ARA-C01 Exam!

Snowflake ARA-C01 certification exam consists of 100 multiple-choice questions that are designed to test the knowledge and skills of the candidates across various domains such as data modeling, data loading, performance tuning, security and access control, and data processing. ARA-C01 exam is conducted in a timed format and candidates are expected to complete it within two hours. SnowPro Advanced Architect Certification certification exam is proctored and can be taken online from anywhere in the world.

 

NEW QUESTION # 57
How can the Snowpipe REST API be used to keep a log of data load history?

• A. Call loadHistoryScan every 10 minutes for a 15-minute time range.
• B. Call insertReport every 8 minutes for a 10-minute time range.
• C. Call insertReport every 20 minutes, fetching the last 10,000 entries.
• D. Call loadHistoryScan every minute for the maximum time range.

Answer: A

Explanation:
* Snowpipe is a service that automates and optimizes the loading of data from external stages into Snowflake tables. Snowpipe uses a queue to ingest files as they become available in the stage. Snowpipe also provides REST endpoints to load data and retrieve load history reports1.
* The loadHistoryScan endpoint returns the history of files that have been ingested by Snowpipe within a specified time range. The endpoint accepts the following parameters2:
* pipe: The fully-qualified name of the pipe to query.
* startTimeInclusive: The start of the time range to query, in ISO 8601 format. The value must be within the past 14 days.
* endTimeExclusive: The end of the time range to query, in ISO 8601 format. The value must be later than the start time and within the past 14 days.
* recentFirst: A boolean flag that indicates whether to return the most recent files first or last. The default value is false, which means the oldest files are returned first.
* showSkippedFiles: A boolean flag that indicates whether to include files that were skipped by
* Snowpipe in the response. The default value is false, which means only files that were loaded are returned.
* The loadHistoryScan endpoint can be used to keep a log of data load history by calling it periodically with a suitable time range. The best option among the choices is D, which is to call loadHistoryScan every 10 minutes for a 15-minute time range. This option ensures that the endpoint is called frequently enough to capture the latest files that have been ingested, and that the time range is wide enough to avoid missing any files that may have been delayed or retried by Snowpipe. The other options are either too infrequent, too narrow, or use the wrong endpoint3.
References:
* 1: Introduction to Snowpipe | Snowflake Documentation
* 2: loadHistoryScan | Snowflake Documentation
* 3: Monitoring Snowpipe Load History | Snowflake Documentation

NEW QUESTION # 58
A company has an inbound share set up with eight tables and five secure views. The company plans to make the share part of its production data pipelines.
Which actions can the company take with the inbound share? (Choose two.)

• A. Grant modify permissions on the share.
• B. Clone a table from a share.
• C. Create a table from the shared database.
• D. Create additional views inside the shared database.
• E. Create a table stream on the shared table.

Answer: B,D

Explanation:
These two actions are possible with an inbound share, according to the Snowflake documentation and the web search results. An inbound share is a share that is created by another Snowflake account (the provider) and imported into your account (the consumer). An inbound share allows you to access the data shared by the provider, but not to modify or delete it. However, you can perform some actions with the inbound share, such as:
* Clone a table from a share. You can create a copy of a table from an inbound share using the CREATE TABLE ... CLONE statement. The clone will contain the same data and metadata as the original table,
* but it will be independent of the share. You can modify or delete the clone as you wish, but it will not reflect any changes made to the original table by the provider1.
* Create additional views inside the shared database. You can create views on the tables or views from an inbound share using the CREATE VIEW statement. The views will be stored in the shared database, but they will be owned by your account. You can query the views as you would query any other view in your account, but you cannot modify or delete the underlying objects from the share2.
The other actions listed are not possible with an inbound share, because they would require modifying the share or the shared objects, which are read-only for the consumer. You cannot grant modify permissions on the share, create a table from the shared database, or create a table stream on the shared table34.
References:
* Cloning Objects from a Share | Snowflake Documentation
* Creating Views on Shared Data | Snowflake Documentation
* Importing Data from a Share | Snowflake Documentation
* Streams on Shared Tables | Snowflake Documentation

NEW QUESTION # 59
The insertReport endpoint can be thought of like the UNIX command tail

• A. TRUE
• B. FALSE

Answer: A

NEW QUESTION # 60
An Architect uses COPY INTO with the ON_ERROR=SKIP_FILE option to bulk load CSV files into a table called TABLEA, using its table stage. One file named file5.csv fails to load. The Architect fixes the file and re-loads it to the stage with the exact same file name it had previously.
Which commands should the Architect use to load only file5.csv file from the stage? (Choose two.)

• A. COPY INTO tablea FROM @%tablea FILES = ('file5.csv');
• B. COPY INTO tablea FROM @%tablea RETURN_FAILED_ONLY = TRUE;
• C. COPY INTO tablea FROM @%tablea;
• D. COPY INTO tablea FROM @%tablea NEW_FILES_ONLY = TRUE;
• E. COPY INTO tablea FROM @%tablea MERGE = TRUE;
• F. COPY INTO tablea FROM @%tablea FORCE = TRUE;

Answer: A,C

Explanation:
* Option A (RETURN_FAILED_ONLY) will only load files that previously failed to load. Since file5.
csv already exists in the stage with the same name, it will not be considered a new file and will not be loaded.
* Option D (FORCE) will overwrite any existing data in the table. This is not desired as we only want to load the data from file5.csv.
* Option E (NEW_FILES_ONLY) will only load files that have been added to the stage since the last COPY command. This will not work because file5.csv was already in the stage before it was fixed.
* Option F (MERGE) is used to merge data from a stage into an existing table, creating new rows for any data not already present. This is not needed in this case as we simply want to load the data from file5.
csv.
Therefore, the architect can use either COPY INTO tablea FROM @%tablea or COPY INTO tablea FROM
@%tablea FILES = ('file5.csv') to load only file5.csv from the stage. Both options will load the data from the specified file without overwriting any existing data or requiring additional configuration

NEW QUESTION # 61
A table for IOT devices that measures water usage is created. The table quickly becomes large and contains more than 2 billion rows.

The general query patterns for the table are:
1. DeviceId, lOT_timestamp and Customerld are frequently used in the filter predicate for the select statement
2. The columns City and DeviceManuf acturer are often retrieved
3. There is often a count on Uniqueld
Which field(s) should be used for the clustering key?

• A. City and DeviceManuf acturer
• B. Deviceld and Customerld
• C. lOT_timestamp
• D. Uniqueld

Answer: B

Explanation:
A clustering key is a subset of columns or expressions that are used to co-locate the data in the same micro- partitions, which are the units of storage in Snowflake. Clustering can improve the performance of queries that filter on the clustering key columns, as it reduces the amount of data that needs to be scanned. The best choice for a clustering key depends on the query patterns and the data distribution in the table. In this case, the columns DeviceId, IOT_timestamp, and CustomerId are frequently used in the filter predicate for the select statement, which means they are good candidates for the clustering key. The columns City and DeviceManufacturer are often retrieved, but not filtered on, so they are not as important for the clustering key.
The column UniqueId is used for counting, but it is not a good choice for the clustering key, as it is likely to have a high cardinality and a uniform distribution, which means it will not help to co-locate the data.
Therefore, the best option is to use DeviceId and CustomerId as the clustering key, as they can help to prune the micro-partitions and speed up the queries. References: Clustering Keys & Clustered Tables, Micro- partitions & Data Clustering, A Complete Guide to Snowflake Clustering

NEW QUESTION # 62
What are some of the characteristics of result set caches? (Choose three.)

• A. The retention period can be reset for a maximum of 31 days.
• B. The result set cache is not shared between warehouses.
• C. Time Travel queries can be executed against the result set cache.
• D. Snowflake persists the data results for 24 hours.
• E. Each time persisted results for a query are used, a 24-hour retention period is reset.
• F. The data stored in the result cache will contribute to storage costs.

Answer: B,D,E

Explanation:
In Snowflake, the characteristics of result set caches include persistence of data results for 24 hours (B), each use of persisted results resets the 24-hour retention period (C), and result set caches are not shared between different warehouses (F). The result set cache is specifically designed to avoid repeated execution of the same query within this timeframe, reducing computational overhead and speeding up query responses. These caches do not contribute to storage costs, and their retention period cannot be extended beyond the default duration nor up to 31 days, as might be misconstrued.
References:Snowflake Documentation on Result Set Caching.

NEW QUESTION # 63
An Architect uses COPY INTO with the ON_ERROR=SKIP_FILE option to bulk load CSV files into a table called TABLEA, using its table stage. One file named file5.csv fails to load. The Architect fixes the file and re-loads it to the stage with the exact same file name it had previously.
Which commands should the Architect use to load only file5.csv file from the stage? (Choose two.)

• A. COPY INTO tablea FROM @%tablea FILES = ('file5.csv');
• B. COPY INTO tablea FROM @%tablea RETURN_FAILED_ONLY = TRUE;
• C. COPY INTO tablea FROM @%tablea;
• D. COPY INTO tablea FROM @%tablea NEW_FILES_ONLY = TRUE;
• E. COPY INTO tablea FROM @%tablea MERGE = TRUE;
• F. COPY INTO tablea FROM @%tablea FORCE = TRUE;

Answer: A,C

Explanation:
Explanation
* Option A (RETURN_FAILED_ONLY) will only load files that previously failed to load. Since file5.csv already exists in the stage with the same name, it will not be considered a new file and will not be loaded.
* Option D (FORCE) will overwrite any existing data in the table. This is not desired as we only want to load the data from file5.csv.
* Option E (NEW_FILES_ONLY) will only load files that have been added to the stage since the last COPY command. This will not work because file5.csv was already in the stage before it was fixed.
* Option F (MERGE) is used to merge data from a stage into an existing table, creating new rows for any data not already present. This is not needed in this case as we simply want to load the data from file5.csv.
Therefore, the architect can use either COPY INTO tablea FROM @%tablea or COPY INTO tablea FROM
@%tablea FILES = ('file5.csv') to load only file5.csv from the stage. Both options will load the data from the specified file without overwriting any existing data or requiring additional configuration

NEW QUESTION # 64
Which of the below commands will use warehouse credits?

• A. SELECT COUNT(*) FROM SNOWFLAKE;
• B. SHOW TABLES LIKE 'SNOWFL%';
• C. SELECT MAX(FLAKE_ID) FROM SNOWFLAKE;
• D. SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID;

Answer: A,C,D

Explanation:
* Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake.
A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.
* Among the commands listed in the question, the following ones will use warehouse credits:
* SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the
* SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.
* SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows in the table3. Therefore, option C is correct.
* SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.
* The command that will not use warehouse credits is:
* SHOW TABLES LIKE 'SNOWFL%': This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern 'SNOWFL%' in the current database and schema5. Therefore, option A is incorrect.
References: : Understanding Compute Cost : MAX Function : COUNT Function : GROUP BY Clause : SHOW TABLES

NEW QUESTION # 65
Which data models can be used when modeling tables in a Snowflake environment? (Select THREE).

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

Answer: B,C,D

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.
References: What is Data Modeling? | Snowflake, Snowflake Schema in Data Warehouse Model - GeeksforGeeks, [Data Vault 2.0 Modeling with Snowflake]

NEW QUESTION # 66
Which of the following are characteristics of Snowflake's parameter hierarchy?

• A. Virtual warehouse parameters override user parameters.
• B. Table parameters override virtual warehouse parameters.
• C. Session parameters override virtual warehouse parameters.
• D. Schema parameters override account parameters.

Answer: D

Explanation:
This is the correct answer because it reflects the characteristics of Snowflake's parameter hierarchy.
Snowflake provides three types of parameters that can be set for an account: account parameters, session parameters, and object parameters. All parameters have default values, which can be set and then overridden at different levels depending on the parameter type. The following diagram illustrates the hierarchical relationship between the different parameter types and how individual parameters can be overridden at each level1:
As shown in the diagram, schema parameters are a type of object parameters that can be set for schemas.
Schema parameters can override the account parameters that are set at the account level. For example, the LOG_LEVEL parameter can be set at the account level to control the logging level for all objects in the account, but it can also be overridden at the schema level to control the logging level for specific stored procedures and UDFs in that schema2.
The other options listed are not correct because they do not reflect the characteristics of Snowflake's parameter hierarchy. Session parameters do not override virtual warehouse parameters, because virtual warehouse parameters are a type of session parameters that can be set for virtual warehouses. Virtual warehouse parameters do not override user parameters, because user parameters are a type of session parameters that can be set for users. Table parameters do not override virtual warehouse parameters, because table parameters are a type of object parameters that can be set for tables, and object parameters do not affect session parameters1.
References:
* Snowflake Documentation: Parameters
* Snowflake Documentation: Setting Log Level

NEW QUESTION # 67
What Snowflake system functions are used to view and or monitor the clustering metadata for a table? (Select TWO).

• A. SYSTEMSCLUSTERING
• B. SYSTEMSCLUSTERING_DEPTH
• C. SYSTEMSCLUSTERING_INFORMATION
• D. SYSTEMSTABLE_CLUSTERING
• E. SYSTEMSCLUSTERING_RATIO

Answer: B,C

Explanation:
The Snowflake system functions used to view and monitor the clustering metadata for a table are:
SYSTEM$CLUSTERING_INFORMATION
SYSTEM$CLUSTERING_DEPTH
Comprehensive But Short Explanation:
The SYSTEM$CLUSTERING_INFORMATION function in Snowflake returns a variety of clustering information for a specified table. This information includes the average clustering depth, total number of micro-partitions, total constant partition count, average overlaps, average depth, and a partition depth histogram. This function allows you to specify either one or multiple columns for which the clustering information is returned, and it returns this data in JSON format.
The SYSTEM$CLUSTERING_DEPTH function computes the average depth of a table based on specified columns or the clustering key defined for the table. A lower average depth indicates that the table is better clustered with respect to the specified columns. This function also allows specifying columns to calculate the depth, and the values need to be enclosed in single quotes.
Reference:
SYSTEM$CLUSTERING_INFORMATION: Snowflake Documentation
SYSTEM$CLUSTERING_DEPTH: Snowflake Documentation

NEW QUESTION # 68
Which Snowflake objects can be used in a data share? (Select TWO).

• A. Secure view
• B. Standard view
• C. External table
• D. Stored procedure
• E. Stream

Answer: A,B

Explanation:
Data sharing is a feature that allows you to share selected objects in a database in your account with other Snowflake accounts. You can share the following Snowflake database objects: external tables, dynamic tables, secure views, secure materialized views, secure UDFs, and tables. However, not all of these objects can be used in a data share. A data share is a named object that encapsulates the information required to share a database. You can grant privileges on objects to a share either via a database role or directly to a share. The objects that can be granted privileges directly to a share are: standard views, secure views, secure UDFs, and tables. Therefore, the correct answer is A and B. The other options are incorrect because they cannot be granted privileges directly to a share. External tables, dynamic tables, and streams can only be shared via a database role. Stored procedures cannot be shared at all. References:
* [Introduction to Secure Data Sharing] 1
* [Working with Shares] 2
* [Choosing How to Share Database Objects] 3

NEW QUESTION # 69
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 two different roles with grants to the different datasets to match the different user personas, and grant these roles to the corresponding users.
• B. 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.
• C. 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.
• 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 # 70
There are two databases in an account, named fin_db and hr_db which contain payroll and employee data, respectively. Accountants and Analysts in the company require different permissions on the objects in these databases to perform their jobs. Accountants need read-write access to fin_db but only require read-only access to hr_db because the database is maintained by human resources personnel.
An Architect needs to create a read-only role for certain employees working in the human resources department.
Which permission sets must be granted to this role?

• A. USAGE on database hr_db, USAGE on all schemas in database hr_db, REFERENCES on all tables in database hr_db
• B. MODIFY on database hr_db, USAGE on all schemas in database hr_db, USAGE on all tables in database hr_db
• C. USAGE on database hr_db, USAGE on all schemas in database hr_db, SELECT on all tables in database hr_db
• D. USAGE on database hr_db, SELECT on all schemas in database hr_db, SELECT on all tables in database hr_db

Answer: C

Explanation:
To create a read-only role for certain employees working in the human resources department, the role needs to have the following permissions on the hr_db database:
USAGE on the database: This allows the role to access the database and see its schemas and objects.
USAGE on all schemas in the database: This allows the role to access the schemas and see their objects.
SELECT on all tables in the database: This allows the role to query the data in the tables.
Option A is the correct answer because it grants the minimum permissions required for a read-only role on the hr_db database.
Option B is incorrect because SELECT on schemas is not a valid permission. Schemas only support USAGE and CREATE permissions.
Option C is incorrect because MODIFY on the database is not a valid permission. Databases only support USAGE, CREATE, MONITOR, and OWNERSHIP permissions. Moreover, USAGE on tables is not sufficient for querying the data. Tables support SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, and OWNERSHIP permissions.
Option D is incorrect because REFERENCES on tables is not relevant for querying the data. REFERENCES permission allows the role to create foreign key constraints on the tables.
Reference:
: https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#database-privileges
: https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#schema-privileges
: https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#table-privileges

NEW QUESTION # 71
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 should provision a reader account to each site and ingest the data through the reader accounts.
• B. The company must have a Snowflake account in each cloud region to be able to ingest data to that account.
• C. The company must replicate data between Snowflake accounts.
• D. The company should use a storage integration for the external stage.

Answer: B

NEW QUESTION # 72
......

To take the Snowflake ARA-C01 exam, candidates must have previously passed the SnowPro Core Certification and have significant experience working with Snowflake's cloud data warehouse platform. ARA-C01 exam consists of 60 multiple-choice questions and must be completed within 90 minutes. A passing score of 80% or higher is required to earn the SnowPro Advanced Architect Certification, which is recognized by organizations worldwide as a validation of advanced skill and knowledge in Snowflake data warehousing.

Snowflake ARA-C01 certification exam is designed for professionals who want to demonstrate their advanced architectural knowledge of Snowflake's cloud data platform. This credential is highly valued in the industry and is a testament to a candidate’s expertise and proficiency in Snowflake. ARA-C01 exam covers various domains of the Snowflake platform, including architecture, design, security, performance tuning, and data integration. Passing the Snowflake ARA-C01 certification exam is a significant achievement for any data professional looking to advance their career in the field.

 

Updated ARA-C01 Dumps Questions For Snowflake Exam: https://www.pass4training.com/ARA-C01-pass-exam-training.html

UPDATED Snowflake ARA-C01 Exam Questions & Answer: https://drive.google.com/open?id=1mmKQ1WJhhWTcJ8NyqlDDxodM9vzDEgLL