Check the Available ARA-C01 Exam Dumps with 135 QA's UPDATED 2024 [Q15-Q35]

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Check the Available ARA-C01 Exam Dumps with 135 QA’s UPDATED 2024 [Q15-Q35]

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Check the Available ARA-C01 Exam Dumps with 135 QA’s UPDATED 2024

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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a professional accreditation designed for experienced data architects and engineers who specialize in building data solutions on the Snowflake platform. SnowPro Advanced Architect Certification certification validates an individual’s expertise in designing and implementing complex data architectures that can handle the demands of modern businesses. ARA-C01 exam covers a broad range of topics, including data modeling, data integration, security, scalability, and performance optimization.

To take the Snowflake ARA-C01 Certification Exam, candidates must have already achieved the SnowPro Core Certification, which validates their foundational knowledge of Snowflake. The ARA-C01 exam is designed to test the candidate’s ability to apply their knowledge to real-world scenarios and solve complex problems related to Snowflake architecture and implementation. ARA-C01 exam consists of multiple-choice questions, scenario-based questions, and hands-on exercises that test the candidate’s practical skills.

NO.15 When does a multi-cluster warehouse shuts down with default scaling policy

Immediately after the query is run

After 2 to 3 consecutive successful checks (performed at 1 minute intervals), which determine whether the load on the least-loaded cluster could be redistributed to the other clusters without spinning up the cluster again.

After 5 to 6 consecutive successful checks (performed at 1 minute intervals), which determine whether the load on the least-loaded cluster could be redistributed to the other clusters without spinning up the cluster again.

NO.16 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?

C5, C4, C2

C3, C4, C5

C1, C3, C2

C2, C1, C3

NO.17 Database DB1 has schema S1 which has one table, T1.
DB1 –> S1 –> T1
The retention period of EG1 is set to 10 days.
The retention period of s: is set to 20 days.
The retention period of t: Is set to 30 days.
The user runs the following command:
Drop Database DB1;
What will the Time Travel retention period be for T1?

10 days

20 days

30 days

37 days

NO.18 Which command do you run to remove files from stage?

REMOVE

DELETE

PURGE

CLEAN

NO.19 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).

Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage.

Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 Glacier storage.

Create an AWS Lambda function to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage.

Configure AWS Simple Notification Service (SNS) to notify Snowpipe when new files have arrived in Amazon S3 storage.

Configure the client application to issue a COPY INTO <TABLE> command to Snowflake when new files have arrived in Amazon S3 Glacier storage.

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

NO.20 The IT Security team has identified that there is an ongoing credential stuffing attack on many of their organization’s system.
What is the BEST way to find recent and ongoing login attempts to Snowflake?

Call the LOGIN_HISTORY Information Schema table function.

Query the LOGIN_HISTORY view in the ACCOUNT_USAGE schema in the SNOWFLAKE database.

View the History tab in the Snowflake UI and set up a filter for SQL text that contains the text
“LOGIN”.

View the Users section in the Account tab in the Snowflake UI and review the last login column.

NO.21 When using the copy into <table> command with the CSV file format, how does the match_by_column_name parameter behave?

It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name.

The parameter will be ignored.

The command will return an error.

The command will return a warning stating that the file has unmatched columns.

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.
Reference:
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.
1: COPY INTO <table> | Snowflake Documentation
2: MATCH_BY_COLUMN_NAME | Snowflake Documentation

NO.22 What is a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka?

The Connector only works in Snowflake regions that use AWS infrastructure.

The Connector works with all file formats, including text, JSON, Avro, Ore, Parquet, and XML.

The Connector creates and manages its own stage, file format, and pipe objects.

Loads using the Connector will have lower latency than Snowpipe and will ingest data in real time.

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

NO.23 Which of the below objects cannot be replicated?

Resource Monitors

Warehouses

Users

Databases

Roles

NO.24 Company A has recently acquired company B. The Snowflake deployment for company B is located in the Azure West Europe region.
As part of the integration process, an Architect has been asked to consolidate company B’s sales data into company A’s Snowflake account which is located in the AWS us-east-1 region.
How can this requirement be met?

Replicate the sales data from company B’s Snowflake account into company A’s Snowflake account using cross-region data replication within Snowflake. Configure a direct share from company B’s account to company A’s account.

Export the sales data from company B’s Snowflake account as CSV files, and transfer the files to company A’s Snowflake account. Import the data using Snowflake’s data loading capabilities.

Migrate company B’s Snowflake deployment to the same region as company A’s Snowflake deployment, ensuring data locality. Then perform a direct database-to-database merge of the sales data.

Build a custom data pipeline using Azure Data Factory or a similar tool to extract the sales data from company B’s Snowflake account. Transform the data, then load it into company A’s Snowflake account.

NO.25 How do you validate the data that is unloaded using COPY INTO command

After unloading, load the data into a relational table and validate the rows

Load the data into a CSV file to validate the rows

Use validation_mode=’RETURN_ROWS’; with COPY command

NO.26 You ran a query and the query
SELECT * FROM inventory WHERE BIBNUMBER = 2805127;
The query profile looks as below. If you would like to further tune the query, what is the best thing to do?

Execute the below query to enable auto clustering

alter table inventory cluster by (BIBNUMBER);

Create an index on column BIBNUMBER

Divide the table into multiple smaller tables

NO.27 The IT Security team has identified that there is an ongoing credential stuffing attack on many of their organization’s system.
What is the BEST way to find recent and ongoing login attempts to Snowflake?

Call the LOGIN_HISTORY Information Schema table function.

Query the LOGIN_HISTORY view in the ACCOUNT_USAGE schema in the SNOWFLAKE database.

View the History tab in the Snowflake UI and set up a filter for SQL text that contains the text “LOGIN”.

View the Users section in the Account tab in the Snowflake UI and review the last login column.

NO.28 This privilege applies to only shared databases. It grants ability to enable roles other than the owning role to access a shared database.
Which is that role?

IMPORTED PRIVILEGES

SHARED PRIVILEGES

IMPORT SHARE

NO.29 What will the below query return
SELECT TOP 10 GRADES FROM STUDENT;

The top 10 highest grades

The 10 lowest grades

Non-deterministic list of 10 grades

NO.30 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?

Utilize a higher Buffer.flush.time in the connector configuration.

Utilize a higher Buffer.size.bytes in the connector configuration.

Utilize a lower Buffer.size.bytes in the connector configuration.

Utilize a lower Buffer.count.records in the connector configuration.

NO.31 You have a very large table which is already clustered on columns that are used to retrieve data from the table by a business group. The base table data does not change much. Another business group came to you and requested for a relatively small subset of data from the table which they will query using complex aggregation logic. You know that querying with those columns will take a lot of time because the table is not clustered on those columns.
What is the most optimal solution that you will suggest to the business team?

CREATE A MATERIALIZED VIEW AND CLUSTER THE VIEW ON THOSE COLUMNS

CREATE A SECURE VIEW

CREATE A REGULAR VIEW

NO.32 What are purposes for creating a storage integration? (Choose three.)

Control access to Snowflake data using a master encryption key that is maintained in the cloud provider’s key management service.

Store a generated identity and access management (IAM) entity for an external cloud provider regardless of the cloud provider that hosts the Snowflake account.

Support multiple external stages using one single Snowflake object.

Avoid supplying credentials when creating a stage or when loading or unloading data.

Create private VPC endpoints that allow direct, secure connectivity between VPCs without traversing the public internet.

Manage credentials from multiple cloud providers in one single Snowflake object.

Explanation
* A storage integration is a Snowflake object that stores a generated identity and access management (IAM) entity for an external cloud provider, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. This integration allows Snowflake to read data from and write data to an external storage location referenced in an external stage1.
* One purpose of creating a storage integration is to support multiple external stages using one single Snowflake object. An integration can list buckets (and optional paths) that limitthe locations users can specify when creating external stages that use the integration. Note that many external stage objects can reference different buckets and paths and use the same storage integration for authentication1.
Therefore, option C is correct.
* Another purpose of creating a storage integration is to avoid supplying credentials when creating a stage or when loading or unloading data. Integrations are named, first-class Snowflake objects that avoid the need for passing explicit cloud provider credentials such as secret keys or access tokens. Integration objects store an IAM user ID, and an administrator in your organization grants the IAM user permissions in the cloud provider account1. Therefore, option D is correct.
* A third purpose of creating a storage integration is to store a generated IAM entity for an external cloud provider regardless of the cloud provider that hosts the Snowflake account. For example, you can create a storage integration for Amazon S3 even if your Snowflake account is hosted on Azure or Google Cloud Platform. This allows you to access data across different cloud platforms using Snowflake1.
Therefore, option B is correct.
* Option A is incorrect, because creating a storage integration does not control access to Snowflake data using a master encryption key. Snowflake encrypts all data using a hierarchical key model, and the master encryption key is managed by Snowflake or by the customer using a cloud provider’s key management service. This is independent of the storage integration feature2.
* Option E is incorrect, because creating a storage integration does not create private VPC endpoints.
Private VPC endpoints are a network configuration option that allow direct, secure connectivity between VPCs without traversing the public internet. This is also independent of the storage integration feature3.
* Option F is incorrect, because creating a storage integration does not manage credentials from multiple cloud providers in one single Snowflake object. A storage integration is specific to one cloud provider, and you need to create separate integrations for each cloud provider you want to access4.
References: : Encryption and Decryption : Private Link for Snowflake : CREATE STORAGE INTEGRATION : Option 1: Configuring a Snowflake Storage Integration to Access Amazon S3

NO.33 What conditions should be true for a table to consider search optimization

The table size is at least 100 GB

The table is not clustered OR The table is frequently queried on columns other than the primary cluster key

The table can be of any size

NO.34 Company A would like to share data in Snowflake with Company B. Company B is not on the same cloud platform as Company A.
What is required to allow data sharing between these two companies?

Create a pipeline to write shared data to a cloud storage location in the target cloud provider.

Ensure that all views are persisted, as views cannot be shared across cloud platforms.

Setup data replication to the region and cloud platform where the consumer resides.

Company A and Company B must agree to use a single cloud platform: Data sharing is only possible if the companies share the same cloud provider.

According to the SnowPro Advanced: Architect documents and learning resources, the requirement to allow data sharing between two companies that are not on the same cloud platform is to set up data replication to the region and cloud platform where the consumer resides. Data replication is a feature of Snowflake that enables copying databases across accounts in different regions and cloud platforms. Data replication allows data providers to securely share data with data consumers across different regions and cloud platforms by creating a replica database in the consumer’s account. The replica database is read-only and automatically synchronized with the primary database in the provider’s account. Data replication is useful for scenarios where data sharing is not possible or desirable due to latency, compliance, or security reasons1. The other options are incorrect because they are not required or feasible to allow data sharing between two companies that are not on the same cloud platform. Option A is incorrect because creating a pipeline to write shared data to a cloud storage location in the target cloud provider is not a secure or efficient way of sharing data. It would require additional steps to load the data from the cloud storage to the consumer’s account, and it would not leverage the benefits of Snowflake’s data sharing features. Option B is incorrect because ensuring that all views are persisted is not relevant for data sharing across cloud platforms. Views can be shared across cloud platforms as long as they reference objects in the same database. Persisting views is an option to improve the performance of querying views, but it is not required for data sharing2. Option D is incorrect because Company A and Company B do not need to agree to use a single cloud platform. Data sharing is possible across different cloud platforms using data replication or other methods, such as listings or auto-fulfillment3. Reference: Replicating Databases Across Multiple Accounts | Snowflake Documentation, Persisting Views | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation

NO.35 A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.
The data pipeline needs to run continuously ang efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.
Which design will meet these requirements?

Ingest the data using COPY INTO and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Ingest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark connector. Apply transformations using another Spark job. Develop a python program to do model inference by leveraging the Amazon Comprehend text analysis API. Then write the results to a Snowflake table and create a listing in the Snowflake Marketplace to make the data available to other companies.

Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

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Check the Available ARA-C01 Exam Dumps with 135 QA’s UPDATED 2024 [Q15-Q35]

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