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Snowflake ARA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Accounts and Security	25%	- Governance and Compliance 1. Object ownership and privilege management 2. Encryption and security architecture 3. Data governance strategies - Access Control and Authentication 1. Network policies 2. SSO and MFA 3. Data masking and row access policies 4. Role-based access control
Data Engineering	25%	- Data Loading and Pipelines 1. Streams and CDC 2. Snowpipe and tasks 3. External stages and file formats 4. Bulk and continuous data loading - Data Transformation 1. Data modeling strategies 2. Semi-structured data processing 3. ETL and ELT design
Performance Optimization	20%	- Cost and Resource Optimization 1. Resource monitors 2. Concurrency scaling 3. Warehouse cost optimization - Query Performance 1. Materialized views 2. Caching strategies 3. Search optimization service 4. Query profiling and tuning
Snowflake Architecture	30%	- Storage and Compute Design 1. Micro-partitions and clustering 2. Warehouse sizing and scaling 3. Database and schema design - Platform Architecture Design 1. Organization and account structure 2. Data sharing architecture 3. Multi-account strategies 4. Cross-region and cross-cloud architecture

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q193-Q198):

NEW QUESTION # 193
What step will improve the performance of queries executed against an external table?

A. Partition the external table.
B. Shorten the names of the source files.
C. Use an internal stage instead of an external stage to store the source files.
D. Convert the source files' character encoding to UTF-8.

Answer: A

Explanation:
Partitioning an external table is a technique that improves the performance of queries executed against the table by reducing the amount of data scanned. Partitioning an external table involves creating one or more partition columns that define how the table is logically divided into subsets of data based on the values in those columns. The partition columns can be derived from the file metadata (such as file name, path, size, or modification time) or from the file content (such as a column value or a JSON attribute). Partitioning an external table allows the query optimizer to prune the files that do not match the query predicates, thus avoiding unnecessary data scanning and processing2 The other options are not effective steps for improving the performance of queries executed against an external table:
* Shorten the names of the source files. This option does not have any impact on the query performance, as the file names are not used for query processing. The file names are only used for creating the external table and displaying the query results3
* Convert the source files' character encoding to UTF-8. This option does not affect the query performance, as Snowflake supports various character encodings for external table files, such as UTF-8,
* UTF-16, UTF-32, ISO-8859-1, and Windows-1252. Snowflake automatically detects the character encoding of the files and converts them to UTF-8 internally for query processing4
* Use an internal stage instead of an external stage to store the source files. This option is not applicable, as external tables can only reference files stored in external stages, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage. Internal stages are used for loading data into internal tables, not external tables5 References:
* 1: SnowPro Advanced: Architect | Study Guide
* 2: Snowflake Documentation | Partitioning External Tables
* 3: Snowflake Documentation | Creating External Tables
* 4: Snowflake Documentation | Supported File Formats and Compression for Staged Data Files
* 5: Snowflake Documentation | Overview of Stages
* : SnowPro Advanced: Architect | Study Guide
* : Partitioning External Tables
* : Creating External Tables
* : Supported File Formats and Compression for Staged Data Files
* : Overview of Stages

NEW QUESTION # 194
How is the change of local time due to daylight savings time handled in Snowflake tasks? (Choose two.)

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

Answer: A,D

NEW QUESTION # 195
A user named USER_01 needs access to create a materialized view on a schema EDW. STG_SCHEMA. How can this access be provided?

A. GRANT ROLE NEW_ROLE TO USER_01;
GRANT CREATE MATERIALIZED VIEW ON EDW.STG_SCHEMA TO NEW_ROLE;
B. GRANT CREATE MATERIALIZED VIEW ON DATABASE EDW TO USER USERJD1;
C. GRANT ROLE NEW_ROLE TO USER USER_01;
GRANT CREATE MATERIALIZED VIEW ON SCHEMA ECW.STG_SCHEKA TO NEW_ROLE;
D. GRANT CREATE MATERIALIZED VIEW ON SCHEMA EDW.STG_SCHEMA TO USER
USER_01;

Answer: D

Explanation:
* The correct answer is A because it grants the specific privilege to create a materialized view on the schema EDW.STG_SCHEMA to the user USER_01 directly.
* Option B is incorrect because it grants the privilege to create a materialized view on the entire database EDW, which is too broad and unnecessary. Also, there is a typo in the user name (USERJD1 instead of USER_01).
* Option C is incorrect because it grants the privilege to create a materialized view on a different schema (ECW.STG_SCHEKA instead of EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose.
* Option D is incorrect because it grants the privilege to create a materialized view on an invalid object (EDW.STG_SCHEMA is not a valid schema name, it should be EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose. References:
* Snowflake Documentation: CREATE MATERIALIZED VIEW
* Snowflake Documentation: Working with Materialized Views
* [Snowflake Documentation: GRANT Privileges on a Schema]

NEW QUESTION # 196
A retail company has over 3000 stores all using the same Point of Sale (POS) system. The company wants to deliver near real-time sales results to category managers. The stores operate in a variety of time zones and exhibit a dynamic range of transactions each minute, with some stores having higher sales volumes than others.
Sales results are provided in a uniform fashion using data engineered fields that will be calculated in a complex data pipeline. Calculations include exceptions, aggregations, and scoring using external functions interfaced to scoring algorithms. The source data for aggregations has over 100M rows.
Every minute, the POS sends all sales transactions files to a cloud storage location with a naming convention that includes store numbers and timestamps to identify the set of transactions contained in the files. The files are typically less than 10MB in size.
How can the near real-time results be provided to the category managers? (Select TWO).

A. A stream should be created to accumulate the near real-time data and a task should be created that runs at a frequency that matches the real-time analytics needs.
B. An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs.
C. All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion.
D. A Snowpipe should be created and configured with AUTO_INGEST = true. A stream should be created to process INSERTS into a single target table using the stream metadata to inform the store number and timestamps.
E. The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement.

Answer: A,D

Explanation:
To provide near real-time sales results to category managers, the Architect can use the following steps:
* Create an external stage that references the cloud storage location where the POS sends the sales transactions files. The external stage should use the file format and encryption settings that match the source files2
* Create a Snowpipe that loads the files from the external stage into a target table in Snowflake. The Snowpipe should be configured with AUTO_INGEST = true, which means that it will automatically detect and ingest new files as they arrive in the external stage. The Snowpipe should also use a copy
* option to purge the files from the external stage after loading, to avoid duplicate ingestion3
* Create a stream on the target table that captures the INSERTS made by the Snowpipe. The stream should include the metadata columns that provide information about the file name, path, size, and last modified time. The stream should also have a retention period that matches the real-time analytics needs4
* Create a task that runs a query on the stream to process the near real-time data. The query should use the stream metadata to extract the store number and timestamps from the file name and path, and perform the calculations for exceptions, aggregations, and scoring using external functions. The query should also output the results to another table or view that can be accessed by the category managers. The task should be scheduled to run at a frequency that matches the real-time analytics needs, such as every minute or every 5 minutes.
The other options are not optimal or feasible for providing near real-time results:
* All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion. This option is not recommended because it would introduce additional latency and complexity in the data pipeline.
Concatenating files would require an external process or service that monitors the cloud storage location and performs the file merging operation. This would delay the ingestion of new files into Snowflake and increase the risk of data loss or corruption. Moreover, concatenating files would not avoid micro-ingestion, as Snowpipe would still ingest each concatenated file as a separate load.
* An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs. This option is not necessary because Snowpipe can automatically ingest new files from the external stage without requiring an external trigger or scheduler. Using an external scheduler would add more overhead and dependency to the data pipeline, and it would not guarantee near real-time ingestion, as it would depend on the polling interval and the availability of the external scheduler.
* The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement. This option is not feasible because tasks cannot be scheduled to run every second in Snowflake. The minimum interval for tasks is one minute, and even that is not guaranteed, as tasks are subject to scheduling delays and concurrency limits. Moreover, using the copy into command with a task would not leverage the benefits of Snowpipe, such as automatic file detection, load balancing, and micro-partition optimization. References:
* 1: SnowPro Advanced: Architect | Study Guide
* 2: Snowflake Documentation | Creating Stages
* 3: Snowflake Documentation | Loading Data Using Snowpipe
* 4: Snowflake Documentation | Using Streams and Tasks for ELT
* : Snowflake Documentation | Creating Tasks
* : Snowflake Documentation | Best Practices for Loading Data
* : Snowflake Documentation | Using the Snowpipe REST API
* : Snowflake Documentation | Scheduling Tasks
* : SnowPro Advanced: Architect | Study Guide
* : Creating Stages
* : Loading Data Using Snowpipe
* : Using Streams and Tasks for ELT
* : [Creating Tasks]
* : [Best Practices for Loading Data]
* : [Using the Snowpipe REST API]
* : [Scheduling Tasks]

NEW QUESTION # 197
An Architect needs to allow a user to create a database from an inbound share.
To meet this requirement, the user's role must have which privileges? (Choose two.)

A. IMPORT SHARE;
B. CREATE DATABASE;
C. IMPORT DATABASE;
D. CREATE SHARE;
E. IMPORT PRIVILEGES;

Answer: B,E

NEW QUESTION # 198
......

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