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Snowflake ARA-R01 Exam Syllabus Topics:

Topic	Details
Topic 1	Data Engineering: This section is about identifying the optimal data loading or unloading method to fulfill business requirements. Examine the primary tools within Snowflake's ecosystem and their integration with the platform.
Topic 2	Snowflake Architecture: This section assesses examining the advantages and constraints of different data models, devises data-sharing strategies, and developing architectural solutions that accommodate Development Lifecycles and workload needs.
Topic 3	Performance Optimization: This section is about summarizing performance tools, recommended practices, and their ideal application scenarios, addressing performance challenges within current architectures, and resolving them.
Topic 4	Accounts and Security: This section relates to creating a Snowflake account and a database strategy aligned with business needs. Users are tested for developing an architecture that satisfies data security, privacy, compliance, and governance standards.

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Snowflake SnowPro Advanced: Architect Recertification Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
An Architect needs to design a data unloading strategy for Snowflake, that will be used with the COPY INTO
<location> command.
Which configuration is valid?

A. Location of files: Google Cloud Storage
. File formats: Parquet
. File encoding: UTF-8
Compression: gzip
B. Location of files: Snowflake internal location
. File formats: CSV, XML
. File encoding: UTF-8
. Encryption: 128-bit
C. Location of files: Amazon S3
. File formats: CSV, JSON
. File encoding: Latin-1 (ISO-8859)
. Encryption: 128-bit
D. Location of files: Azure ADLS
. File formats: JSON, XML, Avro, Parquet, ORC
. Compression: bzip2
. Encryption: User-supplied key

Answer: A

Explanation:
For the configuration of data unloading in Snowflake, the valid option among the provided choices is "C." This is because Snowflake supports unloading data into Google Cloud Storage using the COPY INTO
<location> command with specific configurations. The configurations listed in option C, such as Parquet file format with UTF-8 encoding and gzip compression, are all supported by Snowflake. Notably, Parquet is a columnar storage file format, which is optimal for high-performance data processing tasks in Snowflake. The UTF-8 file encoding and gzip compression are both standard and widely used settings that are compatible with Snowflake's capabilities for data unloading to cloud storage platforms.References:
* Snowflake Documentation on COPY INTO command
* Snowflake Documentation on Supported File Formats
* Snowflake Documentation on Compression and Encoding Options

NEW QUESTION # 52
Consider the following scenario where a masking policy is applied on the CREDICARDND column of the CREDITCARDINFO table. The masking policy definition Is as follows:

Sample data for the CREDITCARDINFO table is as follows:
NAME EXPIRYDATE CREDITCARDNO
JOHN DOE 2022-07-23 4321 5678 9012 1234
if the Snowflake system rotes have not been granted any additional roles, what will be the result?

A. The sysadmin can see the CREDICARDND column data in clear text.
B. Anyone with the Pl_ANALYTICS role will see the last 4 characters of the CREDICARDND column data in dear text.
C. Anyone with the Pl_ANALYTICS role will see the CREDICARDND column as*** 'MASKED* **'.
D. The owner of the table will see the CREDICARDND column data in clear text.

Answer: C

Explanation:
* The masking policy defined in the image indicates that if a user has the PI_ANALYTICS role, they will be able to see the last 4 characters of the CREDITCARDNO column data in clear text. Otherwise, they will see 'MASKED'. Since Snowflake system roles have not been granted any additional roles, they won't have the PI_ANALYTICS role and therefore cannot view the last 4 characters of credit card numbers.
* To apply a masking policy on a column in Snowflake, you need to use the ALTER TABLE ... ALTER COLUMN command or the ALTER VIEW command and specify the policy name. For example, to apply the creditcardno_mask policy on the CREDITCARDNO column of the CREDITCARDINFO table, you can use the following command:
ALTER TABLE CREDITCARDINFO ALTER COLUMN CREDITCARDNO SET MASKING POLICY creditcardno_mask;
* For more information on how to create and use masking policies in Snowflake, you can refer to the following resources:
CREATE MASKING POLICY: This document explains the syntax and usage of the CREATE MASKING POLICY command, which allows you to create a new masking policy or replace an existing one.
Using Dynamic Data Masking: This guide provides instructions on how to configure and use dynamic data masking in Snowflake, which is a feature that allows you to mask sensitive data based on the execution context of the user.
ALTER MASKING POLICY: This document explains the syntax and usage of the ALTER MASKING POLICY command, which allows you to modify the properties of an existing masking policy.
References: 1: https://docs.snowflake.com/en/sql-reference/sql/create-masking-policy 2:
https://docs.snowflake.com/en/user-guide/security-column-ddm-use 3:
https://docs.snowflake.com/en/sql-reference/sql/alter-masking-policy

NEW QUESTION # 53
How is the change of local time due to daylight savings time handled in Snowflake tasks? (Choose two.)

A. A task schedule will follow only the specified time and will fail to handle lost or duplicated hours.
B. A frequent task execution schedule like minutes may not cause a problem, but will affect the task history.
C. Task schedules can be designed to follow specified or local time zones to accommodate the time changes.
D. A task scheduled in a UTC-based schedule will have no issues with the time changes.
E. A task will move to a suspended state during the daylight savings time change.

Answer: C,D

NEW QUESTION # 54
A company's daily Snowflake workload consists of a huge number of concurrent queries triggered between
9pm and 11pm. At the individual level, these queries are smaller statements that get completed within a short time period.
What configuration can the company's Architect implement to enhance the performance of this workload?
(Choose two.)

A. Set the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level.
B. Enable a multi-clustered virtual warehouse in maximized mode during the workload duration.
C. Set the connection timeout to a higher value than its default.
D. Reduce the amount of data that is being processed through this workload.
E. Increase the size of the virtual warehouse to size X-Large.

Answer: A,B

Explanation:
These two configuration options can enhance the performance of the workload that consists of a huge number of concurrent queries that are smaller and faster.
Enabling a multi-clustered virtual warehouse in maximized mode allows the warehouse to scale out automatically by adding more clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This can improve the concurrency and throughput of the workload by minimizing or preventing queuing. The maximized mode is suitable for workloads that require high performance and low latency, and are less sensitive to credit consumption1.
Setting the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level allows the warehouse to run more queries concurrently on each cluster. This can improve the utilization and efficiency of the warehouse resources, especially for smaller and faster queries that do not require a lot of processing power. The MAX_CONCURRENCY_LEVEL parameter can be set when creating or modifying a warehouse, and it can be changed at any time2.
References:
Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses
Snowflake Documentation: MAX_CONCURRENCY_LEVEL

NEW QUESTION # 55
When activating Tri-Secret Secure in a hierarchical encryption model in a Snowflake account, at what level is the customer-managed key used?

A. At the account level (AMK)
B. At the micro-partition level
C. At the root level (HSM)
D. At the table level (TMK)

Answer: A

Explanation:
Tri-Secret Secure is a feature that allows customers to use their own key, called the customer-managed key (CMK), in addition to the Snowflake-managed key, to create a composite master key that encrypts the data in Snowflake. The composite master key is also known as the account master key (AMK), as it is unique for each account and encrypts the table master keys (TMKs) that encrypt the file keys that encrypt the data files. The customer-managed key is used at the account level, not at the root level, the table level, or the micro-partition level. The root level is protected by a hardware security module (HSM), the table level is protected by the TMKs, and the micro-partition level is protected by the file keys12. References:
Understanding Encryption Key Management in Snowflake
Tri-Secret Secure FAQ for Snowflake on AWS

NEW QUESTION # 56
......

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Exam ARA-R01 Review, New ARA-R01 Test Preparation

Snowflake ARA-R01 Exam Syllabus Topics:

New ARA-R01 Test Preparation, Real ARA-R01 Testing Environment

Snowflake SnowPro Advanced: Architect Recertification Exam Sample Questions (Q51-Q56):

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