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Snowflake ARA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Performance Optimization20%- Cost optimization and resource management
- Query performance tuning
- Clustering and partitioning strategies
- Virtual warehouse configuration and sizing
Snowflake Architecture30%- Object hierarchy and architectural impact
- Data modeling and use cases
- Data protection and recovery strategies
- Development lifecycle and workload support
- Data sharing architecture design
Accounts and Security25%- Account and database strategy design
- Security, compliance and governance architecture
- Security principles and implementation scenarios
Data Engineering25%- Data loading and unloading solutions
- Transformation and processing frameworks
- Data pipeline and integration design

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q32-Q37):

NEW QUESTION # 32
An Architect has designed a data pipeline that Is receiving small CSV files from multiple sources. All of the files are landing in one location. Specific files are filtered for loading into Snowflake tables using the copy command. The loading performance is poor.
What changes can be made to Improve the data loading performance?

Answer: A

Explanation:
According to the Snowflake documentation, the data loading performance can be improved by following some best practices and guidelines for preparing and staging the data files. One of the recommendations is to aim for data files that are roughly 100-250 MB (or larger) in size compressed, as this will optimize the number of parallel operations for a load. Smaller files should be aggregated and larger files should be split to achieve this size range. Another recommendation is to use a multi-cluster warehouse for loading, as this will allow for scaling up or out the compute resources depending on the load demand. A single-cluster warehouse may not be able to handle the load concurrency and throughput efficiently. Therefore, by creating a multi-cluster warehouse and merging smaller files to create bigger files, the data loading performance can be improved. References:
* Data Loading Considerations
* Preparing Your Data Files
* Planning a Data Load


NEW QUESTION # 33
How is the change of local time due to daylight savings time handled in Snowflake tasks? (Choose two.)

Answer: C,E

Explanation:
Explanation
According to the Snowflake documentation1 and the web search results2, these two statements are true about how the change of local time due to daylight savings time is handled in Snowflake tasks. A task is a feature that allows scheduling and executing SQL statements or stored procedures in Snowflake. A task can be scheduled using a cron expression that specifies the frequency and time zone of the task execution.
* A task scheduled in a UTC-based schedule will have no issues with the time changes. UTC is a universal time standard that does not observe daylight savings time. Therefore, a task that uses UTC as the time zone will run at the same time throughout the year, regardless of the local time changes1.
* Task schedules can be designed to follow specified or local time zones to accommodate the time changes. Snowflake supports using any valid IANA time zone identifier in the cron expression for a task. This allows the task to run according to the local time of the specified time zone, which may include daylight savings time adjustments. For example, a task that uses Europe/London as the time zone will run one hour earlier or later when the local time switches between GMT and BST12.
References:
* Snowflake Documentation: Scheduling Tasks
* Snowflake Community: Do the timezones used in scheduling tasks in Snowflake adhere to daylight savings?


NEW QUESTION # 34
Which role in Snowflake allows a user to administer users and manage all database objects?

Answer: A


NEW QUESTION # 35
When activating Tri-Secret Secure in a hierarchical encryption model in a Snowflake account, at what level is the customer-managed key used?

Answer: B

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 # 36
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?

Answer: C

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.
Reference:
Snowflake Documentation on COPY INTO command
Snowflake Documentation on Supported File Formats
Snowflake Documentation on Compression and Encoding Options


NEW QUESTION # 37
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