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| Section | Weight | Objectives |
|---|---|---|
| Snowflake Architecture | 30% | - Object hierarchy and architectural impact - Data sharing architecture design - Development lifecycle and workload support - Data modeling and use cases - Data protection and recovery strategies |
| Data Engineering | 25% | - Data loading and unloading solutions - Transformation and processing frameworks - Data pipeline and integration design |
| Performance Optimization | 20% | - Virtual warehouse configuration and sizing - Clustering and partitioning strategies - Cost optimization and resource management - Query performance tuning |
| Accounts and Security | 25% | - Security principles and implementation scenarios - Security, compliance and governance architecture - Account and database strategy design |
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NEW QUESTION # 219
An Architect wants to build an automated ETL pipeline that reads data from an external stage using an external table, performs transformations on changed data, joins with dimension tables, and loads results into a target table.
What should the Architect use?
Answer: B
Explanation:
Streams and tasks provide Snowflake's native framework for building automated, incremental ETL pipelines.
A stream can track changes in the external table (or staging table), while tasks orchestrate the execution of transformation logic and loading into target tables (Answer A).
This approach supports complex transformations, joins with dimension tables, and controlled scheduling or event-driven execution. Materialized views cannot perform joins with arbitrary tables and are not suitable for complex ETL. Dynamic tables simplify transformations but are not designed to consume change data directly from external tables. Snowpipe focuses on ingestion only and does not support downstream transformations.
SnowPro Architect exams frequently test understanding of when to use streams and tasks versus newer abstractions like dynamic tables.
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NEW QUESTION # 220
A user has the appropriate privilege to see unmasked data in a column.
If the user loads this column data into another column that does not have a masking policy, what will occur?
Answer: B
NEW QUESTION # 221
An Architect executes the following query:
SELECT query_hash,
COUNT(*) AS query_count,
SUM(QH.EXECUTION_TIME) AS total_execution_time,
SUM((QH.EXECUTION_TIME / (1000 * 60 * 60)) * 8) AS c
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY QH
WHERE warehouse_name = 'WH_L'
AND DATE_TRUNC('day', start_time) >= CURRENT_DATE() - 3
GROUP BY query_hash
ORDER BY c DESC
LIMIT 10;
What information does this query provide? (Select TWO).
Answer: C,E
Explanation:
This query groups results by QUERY_HASH, which represents logically identical SQL statements. As a result, the aggregation is performed at the query group level, not at the individual execution level. This allows architects to identify patterns where the same query (or same logical SQL) repeatedly consumes a large amount of compute (Answer B).
The cost calculation converts execution time from milliseconds to hours by dividing by (1000 * 60 * 60) and then multiplies the result by 8, which represents the hourly credit consumption of the WH_L warehouse size.
This provides a relative estimate of credit usage per query group, not an exact billing value but a useful approximation for cost analysis (Answer E).
The query does not identify the most frequently executed queries; although COUNT(*) is included, the ordering is done by calculated cost (c), not by frequency. This type of analysis is directly aligned with SnowPro Architect responsibilities, helping architects optimize workloads, refactor expensive query patterns, and right-size warehouses to control costs.
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NEW QUESTION # 222
What does a Snowflake Architect need to consider when implementing a Snowflake Connector for Kafka?
Answer: D
Explanation:
The Snowflake Connector for Kafka is a Kafka Connect sink connector that reads data from one or more Apache Kafka topics and loads the data into a Snowflake table. The connector supports different authentication methods to connect to Snowflake, such as key pair authentication, OAUTH, and basic authentication (for example, username and password). The connector also supports different encryption methods, such as HTTPS and SSL1. The connector does not require that every Kafka message is in JSON or Avro format, as it can handle other formats such as CSV, XML, and Parquet2. The default retention time for Kafka topics is not relevant for the connector, as it only consumes the messages that are available in the topics and does not store them in Kafka. The connector will create one table and one pipe to ingest data for each topic by default, but this behavior can be customized by using the snowflake.topic2table.map configuration property3. If the connector cannot create the table or the pipe, it will log an error and retry the operation until it succeeds or the connector is stopped4. References:
* Installing and Configuring the Kafka Connector
* Overview of the Kafka Connector
* Managing the Kafka Connector
* Troubleshooting the Kafka Connector
NEW QUESTION # 223
Database DB1 has schema S1 which has one table, T1.
DB1 --> S1 --> T1
The retention period of EG1 is set to 10 days.
The retention period of s: is set to 20 days.
The retention period of t: Is set to 30 days.
The user runs the following command:
Drop Database DB1;
What will the Time Travel retention period be for T1?
Answer: D
Explanation:
The Time Travel retention period for T1 will be 30 days, which is the retention period set at the table level.
The Time Travel retention period determines how long the historical data is preserved and accessible for an object after it is modified or dropped. The Time Travel retention period can be set at the account level, the database level, the schema level, or the table level. The retention period set at the lowest level of the hierarchy takes precedence over the higher levels. Therefore, the retention period set at the table level overrides the retention periods set at the schema level, the database level, or the account level. When the user drops the database DB1, the table T1 is also dropped, but the historical data is still preserved for 30 days, which is the retention period set at the table level. The user can use the UNDROP command to restore the table T1 within the 30-day period. The other options are incorrect because:
* 10 days is the retention period set at the database level, which is overridden by the table level.
* 20 days is the retention period set at the schema level, which is also overridden by the table level.
* 37 days is not a valid option, as it is not the retention period set at any level.
References:
* Understanding & Using Time Travel
* AT | BEFORE
* Snowflake Time Travel & Fail-safe
NEW QUESTION # 224
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