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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.
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Snowflake ARA-C01 Certification Exam consists of 60 multiple-choice questions that are designed to test the candidate's knowledge and understanding of Snowflake architecture, data loading and unloading, security, performance optimization, and scalability. Candidates have 120 minutes to complete the exam, and they must score at least 70% to pass.
NEW QUESTION # 120
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 and 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?
Answer: C
Explanation:
Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2.
Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.
Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets.
PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.
Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
References: 1: Snowpipe Overview 2: Using Streams and Tasks to Automate Data Pipelines 3: External Functions Overview 4: Snowflake Data Marketplace Overview : [Loading Data Using COPY INTO] : [What is Amazon EMR?] : [PySpark Overview]
NEW QUESTION # 121
Several in-house applications need to connect to Snowflake without browser access or redirect capabilities.
What is the Snowflake best practice for authentication?
Answer: A
Explanation:
For non-interactive, service-to-service authentication scenarios, Snowflake recommends key pair authentication using a service user (Answer D). This method avoids hardcoding passwords, supports automated rotation of credentials, and aligns with security best practices.
OAuth-based methods typically require browser redirects or user interaction, which are not available in this scenario. Username/password authentication introduces security risks and operational overhead.
Key pair authentication enables strong, certificate-based security and is widely used in SnowPro Architect designs for applications, ETL tools, and automated workloads.
NEW QUESTION # 122
While choosing a cluster key, what is recommended by snowflake?
Answer: A,C
NEW QUESTION # 123
How can the Snowpipe REST API be used to keep a log of data load history?
Answer: C
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 # 124
How do you refresh a materialized view?
Answer: A
NEW QUESTION # 125
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