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[Mar 20, 2025] Reliable ARA-C01 Exam Tips Test Pdf Exam Material [Q16-Q38]

[Mar 20, 2025] Reliable ARA-C01 Exam Tips Test Pdf Exam Material [Q16-Q38]

March 20, 2025 adminARA-C01, SnowflakeARA-C01 latest exam duration, ARA-C01 latest test notes, ARA-C01 new exam cram review, ARA-C01 new test camp pdf, ARA-C01 review guide, ARA-C01 valid study questions book, new ARA-C01 test topicsLeave a Comment on [Mar 20, 2025] Reliable ARA-C01 Exam Tips Test Pdf Exam Material [Q16-Q38]

[Mar 20, 2025] Reliable ARA-C01 Exam Tips Test Pdf Exam Material

New 2025 ARA-C01 Test Tutorial (Updated 162 Questions)

To prepare for the SnowPro Advanced Architect Certification exam, candidates can take advantage of various resources, including Snowflake’s official training courses, online forums, and documentation. There are also many third-party resources available, including practice exams and study guides. It is recommended that candidates have at least two years of hands-on experience working with the Snowflake platform before taking the exam.

 

QUESTION 16
A DevOps team has a requirement for recovery of staging tables used in a complex set of data pipelines. The staging tables are all located in the same staging schem a. One of the requirements is to have online recovery of data on a rolling 7-day basis.
After setting up the DATA_RETENTION_TIME_IN_DAYS at the database level, certain tables remain unrecoverable past 1 day.
What would cause this to occur? (Choose two.)

 
 
 
 
 

QUESTION 17
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).

 
 
 
 
 
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 Reference:
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
8: SnowPro Advanced: Architect | Study Guide
9: Snowpipe Overview
10: Using the Snowpipe REST API
11: Loading Data Using Snowpipe and AWS Lambda
12: Supported File Formats and Compression for Staged Data Files
13: Using Cloud Notifications to Trigger Snowpipe
14: Loading Data Using COPY into a Table

QUESTION 18
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).

 
 
 
 
 

QUESTION 19
Files stored in snowflake internal stage are automatically encrypted using either AES 128 or 256 strong encryption.

 
 

QUESTION 20
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.)

 
 
 
 
 
 

QUESTION 21
How can the Snowpipe REST API be used to keep a log of data load history?

 
 
 
 
* 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

QUESTION 22
Which organization-related tasks can be performed by the ORGADMIN role? (Choose three.)

 
 
 
 
 
 
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 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$GLOBAL_ACCOUNT_SET_PARAMETER function to enable database replication for an account in the organization. This allows the user to replicate databases across accounts in different regions and cloud platforms for data availability and durability4.
The other options are incorrect because they are not organization-related tasks that can be performed by the ORGADMIN role. Option A is incorrect because changing the name of the organization is not a task that can be performed by the ORGADMIN role. To change the name of an organization, the user must contact Snowflake Support3. Option D is incorrect because changing the name of an account is not a task that can be performed by the ORGADMIN role. To change the name of an account, the user must contact Snowflake Support5. Option E is incorrect because deleting an account is not a task that can be performed by the ORGADMIN role. To delete an account, the user must contact Snowflake Support. Reference: CREATE ACCOUNT | Snowflake Documentation, SHOW ORGANIZATION ACCOUNTS | Snowflake Documentation, Getting Started with Organizations | Snowflake Documentation, SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER | Snowflake Documentation, ALTER ACCOUNT | Snowflake Documentation, [DROP ACCOUNT | Snowflake Documentation]

QUESTION 23
Data replication in snowflake helps in

 
 
 
 

QUESTION 24
What is a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka?

 
 
 
 
Explanation
According to the SnowPro Advanced: Architect documents and learning resources, a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka is that the Connector creates and manages its own stage, file format, and pipe objects. The stage is an internal stage that is used to store the data files from the Kafka topics. The file format is a JSON or Avro file format that is used to parse the data files. The pipe is a Snowpipe object that is used to load the data files into the Snowflake table. The Connector automatically creates and configures these objects based on the Kafka configuration properties, and handles the cleanup and maintenance of these objects1.
The other options are incorrect because they are not characteristics of loading data into Snowflake using the Snowflake Connector for Kafka. Option A is incorrect because the Connector works in Snowflake regions that use any cloud infrastructure, not just AWS. The Connector supports AWS, Azure, and Google Cloud platforms, and can load data across different regions and cloud platforms using data replication2. Option B is incorrect because the Connector does not work with all file formats, only JSON and Avro. The Connector expects the data in the Kafka topics to be in JSON or Avro format, and parses the data accordingly. Other file formats, such as text, ORC, Parquet, or XML, are not supported by the Connector3. Option D is incorrect because loads using the Connector do not have lower latency than Snowpipe, and do not ingest data in real time. The Connector uses Snowpipe to load data into Snowflake, and inherits the same latency and performance characteristics of Snowpipe. The Connector does not provide real-time ingestion, but near real-time ingestion, depending on the frequency and size of the data files4. References: Installing and Configuring the Kafka Connector | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation, Overview of the Kafka Connector | Snowflake Documentation, Using Snowflake Connector for Kafka With Snowpipe Streaming | Snowflake Documentation

QUESTION 25
What is the best practice to follow when calling the SNOWPIPE REST API loadHistoryScan

 
 
 

QUESTION 26
The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:
1) Finance and Vendor Management team members who require reporting and visualization
2) Data Science team members who require access to raw data for ML model development
3) Sales team members who require engineered and protected data for data monetization What Snowflake data modeling approaches will meet these requirements? (Choose two.)

 
 
 
 
 
These two approaches are recommended by Snowflake for data modeling in a data lake scenario. Creating a raw database allows the data engineering team to ingest data from various sources without any transformation or cleansing, preserving the original data quality and format. This enables the data science team to access the raw data for ML model development. Creating a set of profile-specific databases allows the data engineering team to apply different transformations and optimizations for different use cases and data consumer requirements. For example, the finance and vendor management team can access a dimensional database that supports reporting and visualization, while the sales team can access a secure database that supports data monetization.
References:
* Snowflake Data Lake Architecture | Snowflake Documentation
* Snowflake Data Lake Best Practices | Snowflake Documentation

QUESTION 27
What step will improve the performance of queries executed against an external table?

 
 
 
 
Partitioning an external table is a technique that improves the performance of queries executed against the table by reducing the amount of data scanned. Partitioning an external table involves creating one or more partition columns that define how the table is logically divided into subsets of data based on the values in those columns. The partition columns can be derived from the file metadata (such as file name, path, size, or modification time) or from the file content (such as a column value or a JSON attribute). Partitioning an external table allows the query optimizer to prune the files that do not match the query predicates, thus avoiding unnecessary data scanning and processing2 The other options are not effective steps for improving the performance of queries executed against an external table:
Shorten the names of the source files. This option does not have any impact on the query performance, as the file names are not used for query processing. The file names are only used for creating the external table and displaying the query results3 Convert the source files’ character encoding to UTF-8. This option does not affect the query performance, as Snowflake supports various character encodings for external table files, such as UTF-8, UTF-16, UTF-32, ISO-8859-1, and Windows-1252. Snowflake automatically detects the character encoding of the files and converts them to UTF-8 internally for query processing4 Use an internal stage instead of an external stage to store the source files. This option is not applicable, as external tables can only reference files stored in external stages, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage. Internal stages are used for loading data into internal tables, not external tables5 Reference:
1: SnowPro Advanced: Architect | Study Guide
2: Snowflake Documentation | Partitioning External Tables
3: Snowflake Documentation | Creating External Tables
4: Snowflake Documentation | Supported File Formats and Compression for Staged Data Files
5: Snowflake Documentation | Overview of Stages
: SnowPro Advanced: Architect | Study Guide
: Partitioning External Tables
: Creating External Tables
: Supported File Formats and Compression for Staged Data Files
: Overview of Stages

QUESTION 28
The Business Intelligence team reports that when some team members run queries for their dashboards in parallel with others, the query response time is getting significantly slower What can a Snowflake Architect do to identify what is occurring and troubleshoot this issue?

 
 
 
 

QUESTION 29
A user needs access to create materialized view on a shema mydb.myschema.
What is the appropriate command to provide the access?

 
 
 

QUESTION 30
What is the data size limit for loading into a variant column?

 
 
 
 

QUESTION 31
An Architect has a design where files arrive every 10 minutes and are loaded into a primary database table using Snowpipe. A secondary database is refreshed every hour with the latest data from the primary database.
Based on this scenario, what Time Travel query options are available on the secondary database?

 
 
 
 
Snowflake’s Time Travel feature allows users to query historical data within a defined retention period. In the given scenario, since the secondary database is refreshed every hour, Time Travel can be used to query each hourly version of the table as long as it falls within the retention window. This does not include individual Snowpipe loads within each hour unless they coincide with the hourly refresh.
References: The answer is verified using Snowflake’s official documentation, which provides detailed information on Time Travel and its usage within the retention period123.

QUESTION 32
How do Snowflake databases that are created from shares differ from standard databases that are not created from shares? (Choose three.)

 
 
 
 
 
 
Explanation
According to the SnowPro Advanced: Architect documents and learning resources, the ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares are:
* Shared databases are read-only. This means that the data consumers who access the shared databases cannot modify or delete the data or the objects in the databases. The data providers who share the databases have full control over the data and the objects, and can grant or revoke privileges on them1.
* Shared databases cannot be cloned. This means that the data consumers who access the shared databases cannot create a copy of the databases or the objects in the databases. The data providers who share the databases can clone the databases or the objects, but the clones are not automatically shared2.
* Shared databases are not supported by Time Travel. This means that the data consumers who access the shared databases cannot use the AS OF clause to query historical data or restore deleted data. The data providers who share the databases can use Time Travel on the databases or the objects, but the historical data is not visible to the data consumers3.
The other options are incorrect because they are not ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares. Option B is incorrectbecause shared databases do not need to be refreshed in order for new data to be visible. The data consumers who access the shared databases can see the latest data as soon as the data providers update the data1. Option E is incorrect because shared databases will not have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share. The data consumers who access the shared databases can only see the objects that the data providers grant to the share, and the PUBLIC and INFORMATION_SCHEMA schemas are not granted by default4. Option F is incorrect because shared databases cannot be created as transient databases. Transient databases are databases that do not support Time Travel or Fail-safe, and can be dropped without affecting the retention period of the data. Shared databases are always created as permanent databases, regardless of the type of the source database5. References: Introduction to Secure Data Sharing | Snowflake Documentation, Cloning Objects | Snowflake Documentation, Time Travel | Snowflake Documentation, Working with Shares | Snowflake Documentation, CREATE DATABASE | Snowflake Documentation

QUESTION 33
A new table and streams are created with the following commands:
CREATE OR REPLACE TABLE LETTERS (ID INT, LETTER STRING) ;
CREATE OR REPLACE STREAM STREAM_1 ON TABLE LETTERS;
CREATE OR REPLACE STREAM STREAM_2 ON TABLE LETTERS APPEND_ONLY = TRUE;
The following operations are processed on the newly created table:
INSERT INTO LETTERS VALUES (1, ‘A’);
INSERT INTO LETTERS VALUES (2, ‘B’);
INSERT INTO LETTERS VALUES (3, ‘C’);
TRUNCATE TABLE LETTERS;
INSERT INTO LETTERS VALUES (4, ‘D’);
INSERT INTO LETTERS VALUES (5, ‘E’);
INSERT INTO LETTERS VALUES (6, ‘F’);
DELETE FROM LETTERS WHERE ID = 6;
What would be the output of the following SQL commands, in order?
SELECT COUNT (*) FROM STREAM_1;
SELECT COUNT (*) FROM STREAM_2;

 
 
 
 
In Snowflake, a stream records data manipulation language (DML) changes to its base table since the stream was created or last consumed. STREAM_1 will show all changes including the TRUNCATE operation, while STREAM_2, being APPEND_ONLY, will not show deletions like TRUNCATE. Therefore, STREAM_1 will count the three inserts, the TRUNCATE (counted as a single operation), and the subsequent two inserts before the delete, totaling 4. STREAM_2 will only count the three initial inserts and the two after the TRUNCATE, totaling 3, as it does not count the TRUNCATE or the delete operation.

QUESTION 34
Which organization-related tasks can be performed by the ORGADMIN role? (Choose three.)

 
 
 
 
 
 

QUESTION 35
Which of the below commands will use warehouse credits?

 
 
 
 
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 warehouseto 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

QUESTION 36
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.)

 
 
 
 
 
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

QUESTION 37
A company is using a Snowflake account in Azure. The account has SAML SSO set up using ADFS as a SCIM identity provider. To validate Private Link connectivity, an Architect performed the following steps:
* Confirmed Private Link URLs are working by logging in with a username/password account
* Verified DNS resolution by running nslookups against Private Link URLs
* Validated connectivity using SnowCD
* Disabled public access using a network policy set to use the company’s IP address range However, the following error message is received when using SSO to log into the company account:
IP XX.XXX.XX.XX is not allowed to access snowflake. Contact your local security administrator.
What steps should the Architect take to resolve this error and ensure that the account is accessed using only Private Link? (Choose two.)

 
 
 
 
 

QUESTION 38
You have a medium warehouse with auto suspend of 5 minutes. You ran a query on table#1. After 10 minutes, you ran a query that joins table#1 and table#2, but you see that the query did not use data
cache.
Why?

 
 
 

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Snowflake ARA-C01: SnowPro Advanced Architect Certification is a prestigious and highly sought-after certification that validates the skills and knowledge of experienced architects and consultants working with Snowflake’s cloud data platform. ARA-C01 exam is designed to test the advanced level of expertise in designing, deploying, managing, and optimizing Snowflake solutions.

 

ARA-C01 Cert Guide PDF 100% Cover Real Exam Questions: https://www.prepawayexam.com/Snowflake/braindumps.ARA-C01.ete.file.html

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