Exam: ARA-R01
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ARA-R01 SnowPro Advanced Architect Recertification
SNOWPRO ADVANCED: ARCHITECT RECERTIFICATION OVERVIEW
The SnowPro Advanced: Architect Recertification exam is available for
candidates with an expiring SnowPro Advanced: Architect Certification.
Exam Version: ARA-R01
Total Number of Questions: 40
Question Types: Multiple Select, Multiple Choice
Time Limit: 85 minutes
Language: English
Registration fee: $25 USD
Passing Score: 750 + Scaled Scoring from 0 - 1000
Unscored Content: Exams may include unscored items to gather statistical
information for future use. These items are not identified on the form and do
not impact your score, and additional time is factored into account for this
content.
Prerequisites: SnowPro Core Certified & SnowPro Advanced:Architect Certified
Delivery Options:
1. Online Proctoring
2. Onsite Testing Centers
By passing the SnowPro Advanced: Architect Recertification, a candidate's
SnowPro Core Certification + SnowPro Advanced: Architect status will extend an
additional 2 years from the date of passing the recertification exam.
Candidates must hold a valid SnowPro Advanced: Architect Certification at the
time they take the recertification exam to be eligible. This recertification
exam shares the same exam guide as SnowPro Advanced: Architect.
EXAM DOMAIN BREAKDOWN
This exam guide includes test domains, weightings, and objectives. It is not a
comprehensive listing of all the content that will be presented on this
examination. The table below lists the main content domains and their
weightings.
Domain Domain Weightings on Exams
1.0 Accounts and Security 25-30%
2.0 Snowflake Architecture 25-30%
3.0 Data Engineering 20-25%
4.0 Performance Optimization 20-25%
EXAM TOPICS
Outlined below are the Domains & Objectives measured on the exam. To view
subtopics, download the exam study guide.
Domain 1.0: Accounts and Security
1.1 Design a Snowflake account and database strategy, based on business
requirements.
1.2 Design an architecture that meets data security, privacy, compliance, and
governance requirements.
1.3 Outline Snowflake security principles and identify use cases where they
should be applied.
Domain 2.0: Snowflake Architecture
2.1 Outline the benefits and limitations of various data models in a
Snowflake environment.
2.2 Design data sharing solutions, based on different use cases.
2.3 Create architecture solutions that support Development Lifecycles as well as
workload requirements.
2.4 Given a scenario, outline how objects exist within the Snowflake Object
hierarchy and how the hierarchy impacts an architecture.
2.5 Determine the appropriate data recovery solution in Snowflake and how data
can be restored.
Domain 3.0: Data Engineering
3.1 Determine the appropriate data loading or data unloading solution to
meet business needs.
3.2 Outline key tools in Snowflake’s ecosystem and how they interact with
Snowflake.
3.3 Determine the appropriate data transformation solution to meet business
needs.
Domain 4.0: Performance Optimization
4.1 Outline performance tools, best practices, and appropriate scenarios
where they should be applied.
4.2 Troubleshoot performance issues with existing architectures.
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QUESTION 1
What built-in Snowflake features make use of the change tracking metadata for a
table? (Choose two.)
A. The MERGE command
B. The UPSERT command
C. The CHANGES clause
D. A STREAM object
E. The CHANGE_DATA_CAPTURE command
Answer: A, D
Explanation:
In Snowflake, the change tracking metadata for a table is utilized by the MERGE
command and the
STREAM object. The MERGE command uses change tracking to determine how to apply
updataes and
inserts efficiently based on differences between source and target tables.
STREAM objects, on the
other hand, specifically capture and store change data, enabling incremental
processing based on
changes made to a table since the last stream offset was committed.
Reference: Snowflake Documentation on MERGE and STREAM Objects.
QUESTION 2
When using the Snowflake Connector for Kafka, what data formats are
supported for the messages? (Choose two.)
A. CSV
B. XML
C. Avro
D. JSON
E. Parquet
Answer: C, D
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. Reference: Snowflake Connector for Kafka | Snowflake Documentation,
Loading Protobuf
Data using the Snowflake Connector for Kafka | Snowflake Documentation
QUESTION 3
At which object type level can the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY
and APPLY SESSION POLICY privileges be granted?
A. Global
B. Database
C. Schema
D. Table
Answer: A
Explanation:
The object type level at which the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY
and APPLY
SESSION POLICY privileges can be granted is global. These are account-level
privileges that control
who can apply or unset these policies on objects such as columns, tables, views,
accounts, or users.
These privileges are granted to the ACCOUNTADMIN role by default, and can be
granted to other
roles as needed. The other options are incorrect because they are not the object
type level at which
these privileges can be granted. Database, schema, and table are lower-level
object types that do not
support these privileges. Reference: Access Control Privileges | Snowflake
Documentation, Using
Dynamic Data Masking | Snowflake Documentation, Using Row Access Policies |
Snowflake
Documentation, Using Session Policies | Snowflake Documentation
QUESTION 4
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 RETURN_FAILED_ONLY = TRUE;
B. COPY INTO tablea FROM @%tablea;
C. COPY INTO tablea FROM @%tablea FILES = ('file5.csv');
D. COPY INTO tablea FROM @%tablea FORCE = TRUE;
E. COPY INTO tablea FROM @%tablea NEW_FILES_ONLY = TRUE;
F. COPY INTO tablea FROM @%tablea MERGE = TRUE;
Answer: BC
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
QUESTION 5
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 companys 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. Migrate the European accounts in the global region and manage shares in a
connected graph architecture. Deploy a Data Exchange.
B. Deploy a Private Data Exchange in combination with data shares for the
European accounts.
C. Deploy to the Snowflake Marketplace making sure that invoker_share() is used
in all secure views.
D. Deploy a Private Data Exchange and use replication to allow European data
shares in the Exchange.
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
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Students Feedback / Reviews/ Discussion
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