[Q28-Q50] The Best Valid ARA-C01 Dumps for Helping Passing ARA-C01 Exam!

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

UPDATED Snowflake ARA-C01 Exam Questions & Answer

NEW QUESTION # 28
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.)

• A. Consolidate data in the company's data lake and use EXTERNAL TABLES.
• B. Create a set of profile-specific databases that aligns data with usage patterns.
• C. Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.
• D. Create a raw database for landing and persisting raw data entering the data pipelines.
• E. Create a single star schema in a single database to support all consumers' requirements.

Answer: C,E

NEW QUESTION # 29
EXECUTE TASK privilege can be granted by whom

• A. ACCOUNTADMIN
• B. SECURITYADMIN
• C. ROLE WHO OWNS THE TASK

Answer: A

NEW QUESTION # 30
An Architect is implementing a CI/CD process. When attempting to clone a table from a production to a development environment, the cloning operation fails.
What could be causing this to happen?

• A. The table is transient.
• B. Tables cannot be cloned from a higher environment to a lower environment.
• C. The table has a masking policy.
• D. The retention time for the table is set to zero.

Answer: C

Explanation:
Cloning a table with a masking policy can cause the cloning operation to fail because the masking policy is not automatically cloned with the table. This is due to the fact that the masking policy is considered a separate object with its own set of privileges1.
References
Snowflake Documentation on Cloning Considerations1.

NEW QUESTION # 31
Which technique will efficiently ingest and consume semi-structured data for Snowflake data lake workloads?

• A. Schema-on-write
• B. IDEF1X
• C. Information schema
• D. Schema-on-read

Answer: D

Explanation:
Option C is the correct answer because schema-on-read is a technique that allows Snowflake to ingest and consume semi-structured data without requiring a predefined schema. Snowflake supports various semi-structured data formats such as JSON, Avro, ORC, Parquet, and XML, and provides native data types (ARRAY, OBJECT, and VARIANT) for storing them. Snowflake also provides native support for querying semi-structured data using SQL and dot notation. Schema-on-read enables Snowflake to query semi-structured data at the same speed as performing relational queries while preserving the flexibility of schema-on-read. Snowflake's near-instant elasticity rightsizes compute resources, and consumption-based pricing ensures you only pay for what you use.
Option A is incorrect because IDEF1X is a data modeling technique that defines the structure and constraints of relational data using diagrams and notations. IDEF1X is not suitable for ingesting and consuming semi-structured data, which does not have a fixed schema or structure.
Option B is incorrect because schema-on-write is a technique that requires defining a schema before loading and processing data. Schema-on-write is not efficient for ingesting and consuming semi-structured data, which may have varying or complex structures that are difficult to fit into a predefined schema. Schema-on-write also introduces additional overhead and complexity for data transformation and validation.
Option D is incorrect because information schema is a set of metadata views that provide information about the objects and privileges in a Snowflake database. Information schema is not a technique for ingesting and consuming semi-structured data, but rather a way of accessing metadata about the data.
Reference:
Semi-structured Data
Snowflake for Data Lake

NEW QUESTION # 32
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 20 minutes, fetching the last 10,000 entries.
• C. Call insertReport every 8 minutes for a 10-minute time range.
• 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.
Reference:
1: Introduction to Snowpipe | Snowflake Documentation
2: loadHistoryScan | Snowflake Documentation
3: Monitoring Snowpipe Load History | Snowflake Documentation

NEW QUESTION # 33
Snowflake supports the following query performance optimizing methods

• A. Caching techniques
• B. B-tree type indexes
• C. Retrieving results of previous query from cache

Answer: A,C

NEW QUESTION # 34
How does a standard virtual warehouse policy work in Snowflake?

• A. It starts only if the system estimates that there is a query load that will keep the cluster busy for at least 6 minutes.
• B. It prevents or minimizes queuing by starting additional clusters instead of conserving credits.
• C. It conserves credits by keeping running clusters fully loaded rather than starting additional clusters.
• D. It starts only f the system estimates that there is a query load that will keep the cluster busy for at least 2 minutes.

Answer: B

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.
References:
* Snowflake Documentation: Multi-cluster Warehouses
* Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses

NEW QUESTION # 35
When using the Snowflake Connector for Kafka, what data formats are supported for the messages? (Choose two.)

• A. CSV
• B. XML
• C. Parquet
• D. JSON
• E. Avro

Answer: D,E

Explanation:
The data formats that are supported for the messages when using the Snowflake Connector for Kafka are Avro and JSON. These are the two formats that the connector can parse and convert into Snowflake table rows. The connector supports both schemaless and schematized JSON, as well as Avro with or without a schema registry1. The other options are incorrect because they are not supported data formats for the messages. CSV, XML, and Parquet are not formats that the connector can parse and convert into Snowflake table rows. If the messages are in these formats, the connector will load them as VARIANT data type and store them as raw strings in the table2. References: Snowflake Connector for Kafka | Snowflake Documentation, Loading Protobuf Data using the Snowflake Connector for Kafka | Snowflake Documentation

NEW QUESTION # 36
As of today snowflake supports replication for databases only

• A. TRUE
• B. FALSE

Answer: A

NEW QUESTION # 37
A large manufacturing company runs a dozen individual Snowflake accounts across its business divisions. The company wants to increase the level of data sharing to support supply chain optimizations and increase its purchasing leverage with multiple vendors.
The company's Snowflake Architects need to design a solution that would allow the business divisions to decide what to share, while minimizing the level of effort spent on configuration and management. Most of the company divisions use Snowflake accounts in the same cloud deployments with a few exceptions for European-based divisions.
According to Snowflake recommended best practice, how should these requirements be met?

• A. Deploy a Private Data Exchange and use replication to allow European data shares in the Exchange.
• B. Migrate the European accounts in the global region and manage shares in a connected graph architecture. Deploy a Data Exchange.
• C. Deploy to the Snowflake Marketplace making sure that invoker_share() is used in all secure views.
• D. Deploy a Private Data Exchange in combination with data shares for the European accounts.

Answer: D

Explanation:
According to Snowflake recommended best practice, the requirements of the large manufacturing company should be met by deploying a Private Data Exchange in combination with data shares for the European accounts. A Private Data Exchange is a feature of the Snowflake Data Cloud platform that enables secure and governed sharing of data between organizations. It allows Snowflake customers to create their own data hub and invite other parts of their organization or external partners to access and contribute data sets. A Private Data Exchange provides centralized management, granular access control, and data usage metrics for the data shared in the exchange1. A data share is a secure and direct way of sharing data between Snowflake accounts without having to copy or move the data. A data share allows the data provider to grant privileges on selected objects in their account to one or more data consumers in other accounts2. By using a Private Data Exchange in combination with data shares, the company can achieve the following benefits:
* The business divisions can decide what data to share and publish it to the Private Data Exchange, where it can be discovered and accessed by other members of the exchange. This reduces the effort and complexity of managing multiple data sharing relationships and configurations.
* The company can leverage the existing Snowflake accounts in the same cloud deployments to create the Private Data Exchange and invite the members to join. This minimizes the migration and setup costs and leverages the existing Snowflake features and security.
* The company can use data shares to share data with the European accounts that are in different regions or cloud platforms. This allows the company to comply with the regional and regulatory requirements for data sovereignty and privacy, while still enabling data collaboration across the organization.
* The company can use the Snowflake Data Cloud platform to perform data analysis and transformation on the shared data, as well as integrate with other data sources and applications. This enables the company to optimize its supply chain and increase its purchasing leverage with multiple vendors.
The other options are incorrect because they do not meet the requirements or follow the best practices. Option A is incorrect because migrating the European accounts to the global region may violate the data sovereignty and privacy regulations, and deploying a Data Exchange may not provide the level of control and management that the company needs. Option C is incorrect because deploying to the Snowflake Marketplace may expose the company's data to unwanted consumers, and using invoker_share() in secure views may not provide the desired level of security and governance. Option D is incorrect because using replication to allow European data shares in the Exchange may incur additional costs and complexity, and may not be necessary if data shares can be used instead. References: Private Data Exchange | Snowflake Documentation, Introduction to Secure Data Sharing | Snowflake Documentation

NEW QUESTION # 38
You have created a reader account for sharing data. who will pay for the compute usage of the account

• A. Provider
• B. Consumer

Answer: A

NEW QUESTION # 39
You got a response code of 429 from the insertFiles API.
What does that mean?

• A. Failure. pipeName not recognized
• B. Failure. Request rate limit exceeded
• C. Failure. Internal error occurred
• D. Failure. Invalid request due to an invalid format, or limit exceeded

Answer: B

NEW QUESTION # 40
COMPRESSION = AUTO can automatically detect below compression techniques when FORMAT TYPE is CSV

• A. RAW_DEFLATE
• B. GZIP
• C. ZSTD
• D. BZ2
• E. BROTLI
• F. DEFLATE

Answer: A,B,C,D,F

NEW QUESTION # 41
A user needs access to create materialized view on a shema mydb.myschema.
What is the appropriate command to provide the access?

• A. GRANT CREATE MATERIALIZED VIEW ON SCHEMA MYDB.MYSCHEMA TO USER1;
• B. GRANT ROLE MYROLE TO USER USER1; GRANT CREATE MATERIALIZED VIEW ON SCHEMA MYDB.MYSCHEMA TO MYROLE;
• C. GRANT CREATE MATERIALIZED VIEW ON SCHEMA MYDB.MYSCHEMA TO USER USER1;

Answer: B

NEW QUESTION # 42
Which of the following ingestion methods can be used to load near real-time data by using the messaging services provided by a cloud provider?

• A. Spark
• B. Snowpipe
• C. Snowflake streams
• D. Snowflake Connector for Kafka

Answer: B,D

Explanation:
Snowflake Connector for Kafka and Snowpipe are two ingestion methods that can be used to load near real-time data by using the messaging services provided by a cloud provider. Snowflake Connector for Kafka enables you to stream structured and semi-structured data from Apache Kafka topics into Snowflake tables. Snowpipe enables you to load data from files that are continuously added to a cloud storage location, such as Amazon S3 or Azure Blob Storage. Both methods leverage Snowflake's micro-partitioning and columnar storage to optimize data ingestion and query performance. Snowflake streams and Spark are not ingestion methods, but rather components of the Snowflake architecture. Snowflake streams provide change data capture (CDC) functionality by tracking data changes in a table. Spark is a distributed computing framework that can be used to process large-scale data and write it to Snowflake using the Snowflake Spark Connector. Reference:
Snowflake Connector for Kafka
Snowpipe
Snowflake Streams
Snowflake Spark Connector

NEW QUESTION # 43
The following DDL command was used to create a task based on a stream:

Assuming MY_WH is set to auto_suspend - 60 and used exclusively for this task, which statement is true?

• A. The warehouse MY_WH will never suspend.
• B. The warehouse MY_WH will only be active when there are results in the stream.
• C. The warehouse MY_WH will be made active every five minutes to check the stream.
• D. The warehouse MY_WH will automatically resize to accommodate the size of the stream.

Answer: B

Explanation:
The warehouse MY_WH will only be active when there are results in the stream. This is because the task is created based on a stream, which means that the task will only be executed when there are new data in the stream. Additionally, the warehouse is set to auto_suspend - 60, which means that the warehouse will automatically suspend after 60 seconds of inactivity. Therefore, the warehouse will only be active when there are results in the stream. References:
* [CREATE TASK | Snowflake Documentation]
* [Using Streams and Tasks | Snowflake Documentation]
* [CREATE WAREHOUSE | Snowflake Documentation]

NEW QUESTION # 44
An Architect has been asked to clone schema STAGING as it looked one week ago, Tuesday June 1st at 8:00 AM, to recover some objects.
The STAGING schema has 50 days of retention.
The Architect runs the following statement:
CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp => '2021-06-01 08:00:00'); The Architect receives the following error: Time travel data is not available for schema STAGING. The requested time is either beyond the allowed time travel period or before the object creation time.
The Architect then checks the schema history and sees the following:
CREATED_ON|NAME|DROPPED_ON
2021-06-02 23:00:00 | STAGING | NULL
2021-05-01 10:00:00 | STAGING | 2021-06-02 23:00:00
How can cloning the STAGING schema be achieved?

• A. Undrop the STAGING schema and then rerun the CLONE statement.
• B. Rename the STAGING schema and perform an UNDROP to retrieve the previous STAGING schema version, then run the CLONE statement.
• C. Modify the statement: CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp =>
  '2021-05-01 10:00:00');
• D. Cloning cannot be accomplished because the STAGING schema version was not active during the proposed Time Travel time period.

Answer: D

Explanation:
The error encountered during the cloning attempt arises because the schema STAGING as it existed on June
1st, 2021, is not within the Time Travel retention period. According to the schema history, STAGING was recreated on June 2nd, 2021, after being dropped on the same day. The requested timestamp of '2021-06-01
08:00:00' is prior to this recreation, hence not available. The STAGING schema from before June 2nd was dropped and exceeded the Time Travel period for retrieval by the time of the cloning attempt. Therefore, cloning STAGING as it looked on June 1st, 2021, cannot be achieved because the data from that time is no longer available within the allowed Time Travel window.References: Snowflake documentation on Time Travel and data cloning, which is covered under the SnowPro Advanced: Architect certification resources.

NEW QUESTION # 45
A Data Engineer is designing a near real-time ingestion pipeline for a retail company to ingest event logs into Snowflake to derive insights. A Snowflake Architect is asked to define security best practices to configure access control privileges for the data load for auto-ingest to Snowpipe.
What are the MINIMUM object privileges required for the Snowpipe user to execute Snowpipe?

• A. OWNERSHIP on the named pipe, USAGE on the named stage, target database, and schema, and INSERT and SELECT on the target table
• B. CREATE on the named pipe, USAGE and READ on the named stage, USAGE on the target database and schema, and INSERT end SELECT on the target table
• C. OWNERSHIP on the named pipe, USAGE and READ on the named stage, USAGE on the target database and schema, and INSERT end SELECT on the target table
• D. USAGE on the named pipe, named stage, target database, and schema, and INSERT and SELECT on the target table

Answer: C

Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, the minimum object privileges required for the Snowpipe user to execute Snowpipe are:
* OWNERSHIP on the named pipe. This privilege allows the Snowpipe user to create, modify, and drop the pipe object that defines the COPY statement for loading data from the stage to the table1.
* USAGE and READ on the named stage. These privileges allow the Snowpipe user to access and read the data files from the stage that are loaded by Snowpipe2.
* USAGE on the target database and schema. These privileges allow the Snowpipe user to access the database and schema that contain the target table3.
* INSERT and SELECT on the target table. These privileges allow the Snowpipe user to insert data into the table and select data from the table4.
The other options are incorrect because they do not specify the minimum object privileges required for the Snowpipe user to execute Snowpipe. Option A is incorrect because it does not include the READ privilege on the named stage, which is required for the Snowpipe user to read the data files from the stage. Option C is incorrect because it does not include the OWNERSHIP privilege on the named pipe, which is required for the Snowpipe user to create, modify, and drop the pipe object. Option D is incorrect because it does not include the OWNERSHIP privilege on the named pipe or the READ privilege on the named stage, which are both required for the Snowpipe user to execute Snowpipe. References: CREATE PIPE | Snowflake Documentation, CREATE STAGE | Snowflake Documentation, CREATE DATABASE | Snowflake Documentation, CREATE TABLE | Snowflake Documentation

NEW QUESTION # 46
A user is executing the following command sequentially within a timeframe of 10 minutes from start to finish:

What would be the output of this query?

• A. Time Travel data is not available for table T_SALES.
• B. Table T_SALES_CLONE successfully created.
• C. Syntax error line 1 at position 58 unexpected 'at'.
• D. The offset -> is not a valid clause in the clone operation.

Answer: B

Explanation:
The query is executing a clone operation on an existing table t_sales with an offset to account for the retention time. The syntax used is correct for cloning a table in Snowflake, and the use of the at(offset => -60*30) clause is valid. This specifies that the clone should be based on the state of the table 30 minutes prior (60 seconds * 30). Assuming the table t_sales exists and has been modified within the last 30 minutes, and considering the data_retention_time_in_days is set to 1 day (which enables time travel queries for the past
24 hours), the table t_sales_clone would be successfully created based on the state of t_sales 30 minutes before the clone command was issued.

NEW QUESTION # 47
An Architect is troubleshooting a query with poor performance using the QUERY_HIST0RY function. The Architect observes that the COMPILATIONJHME is greater than the EXECUTIONJTIME.
What is the reason for this?

• A. The query has overly complex logic.
• B. The query is reading from remote storage.
• C. The query is queued for execution.
• D. The query is processing a very large dataset.

Answer: D

NEW QUESTION # 48
Which role in Snowflake allows a user to administer users and manage all database objects?

• A. ACCOUNTADMIN
• B. SECURITYADMIN
• C. SYSADMIN
• D. ROOT

Answer: A

NEW QUESTION # 49
What integration object should be used to place restrictions on where data may be exported?

• A. API integration
• B. Security integration
• C. Stage integration
• D. Storage integration

Answer: B

Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, the integration object that should be used to place restrictions on where data may be exported is the security integration. A security integration is a Snowflake object that provides an interface between Snowflake and third-party security services, such as Okta, Duo, or Google Authenticator. A security integration can be used to enforce policies on data export, such as requiring multi-factor authentication (MFA) or restricting the export destination to a specific network or domain. A security integration can also be used to enable single sign-on (SSO) or federated authentication for Snowflake users1.
The other options are incorrect because they are not integration objects that can be used to place restrictions on where data may be exported. Option A is incorrect because a stage integration is not a valid type of integration object in Snowflake. A stage is a Snowflake object that references a location where data files are stored, such as an internal stage, an external stage, or a named stage. A stage is not an integration object that provides an interface between Snowflake and third-party services2. Option C is incorrect because a storage integration is a Snowflake object that provides an interface between Snowflake and external cloud storage, such as Amazon S3, Azure Blob Storage, or Google Cloud Storage. A storage integration can be used to securely access data files from external cloud storage without exposing the credentials, but it cannot be used to place restrictions on where data may be exported3. Option D is incorrect because an API integration is a Snowflake object that provides an interface between Snowflake and third-party services that use REST APIs, such as Salesforce, Slack, or Twilio. An API integration can be used to securely call external REST APIs from Snowflake using the CALL_EXTERNAL_API function, but it cannot be used to place restrictions on where data may be exported4. Reference: CREATE SECURITY INTEGRATION | Snowflake Documentation, CREATE STAGE | Snowflake Documentation, CREATE STORAGE INTEGRATION | Snowflake Documentation, CREATE API INTEGRATION | Snowflake Documentation

NEW QUESTION # 50
......

Updated ARA-C01 Dumps Questions For Snowflake Exam: https://www.vceengine.com/ARA-C01-vce-test-engine.html

Latest Success Metrics For Actual ARA-C01 Exam Realistic Dumps: https://drive.google.com/open?id=1dJc8zcoAYhVSCtENit84SiZSq4D8Tu4J