VALID SNOWFLAKE ADA-C01 TEST PREPARATION, ADA-C01 TRAINING FOR EXAM

Valid Snowflake ADA-C01 Test Preparation, ADA-C01 Training For Exam

Valid Snowflake ADA-C01 Test Preparation, ADA-C01 Training For Exam

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Snowflake ADA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Given a scenario, create and manage access control
  • Given a scenario, implement resource monitors
Topic 2
  • Snowflake Security, Role-Based Access Control (RBAC), and User Administration
  • Disaster Recovery, Backup, and Data Replication
Topic 3
  • Given a scenario, configure access controls
  • Set up and manage security administration and authorization
Topic 4
  • Implement and manage data governance in Snowflake
  • Data Sharing, Data Exchange, and Snowflake Marketplace

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Snowflake SnowPro Advanced Administrator Sample Questions (Q38-Q43):

NEW QUESTION # 38
A retailer uses a TRANSACTIONS table (100M rows, 1.2 TB) that has been clustered by the STORE_ID column (varchar(50)). The vast majority of analyses on this table are grouped by STORE_ID to look at store performance.
There are 1000 stores operated by the retailer but most sales come from only 20 stores. The Administrator notes that most queries are currently experiencing poor pruning, with large amounts of bytes processed by even simple queries.
Why is this occurring?

  • A. The cardinality of the stores to transaction count ratio is too low to use the STORE_ID as a clustering key.
  • B. Sales across stores are not uniformly distributed.
  • C. The STORE_ID should be numeric.
  • D. The table is not big enough to take advantage of the clustering key.

Answer: B

Explanation:
According to the Snowflake documentation1, clustering keys are most effective when the data is evenly distributed across the key values. If the data is skewed, such as in this case where most sales come from only 20 stores out of 1000, then the micro-partitions will not be well-clustered and the pruning will be poor. This means that more bytes will be scanned by queries, even if they filter by STORE_ID. Option A is incorrect because the data type of the clustering key does not affect the pruning. Option B is incorrect because the table is large enough to benefit from clustering, if the data was more balanced. Option D is incorrect because the cardinality of the clustering key is not relevant for pruning, as long as the key values are distinct.
1: Considerations for Choosing Clustering for a Table | Snowflake Documentation


NEW QUESTION # 39
A company has many users in the role ANALYST who routinely query Snowflake through a reporting tool.
The Administrator has noticed that the ANALYST users keep two
small clusters busy all of the time, and occasionally they need three or four clusters of that size.
Based on this scenario, how should the Administrator set up a virtual warehouse to MOST efficiently support this group of users?

  • A. Create a standard X-Large warehouse, which is equivalent to four small clusters. Set the warehouse to auto-resume and auto-suspend, and give USAGE privileges to the ANALYST role.
  • B. Create a multi-cluster warehouse with MIN_CLUSTERS set to 2. Set the warehouse to auto-resume and auto-suspend, and give USAGE privileges to the ANALYST role. Allow the warehouse to auto-scale.
  • C. Create four virtual warehouses (sized Small through XL) and set them to auto-suspend andauto-resume.
    Have users in the ANALYST role select the appropriate warehouse based on how many queries are being run.
  • D. Create a multi-cluster warehouse with MIN_CLUSTERS set to 1. Give MANAGE privileges to the ANALYST role so this group can start and stop the warehouse, and increase the number of clusters as needed.

Answer: B

Explanation:
Explanation
According to the Snowflake documentation1, a multi-cluster warehouse is a virtual warehouse that consists of multiple clusters of compute resources that can scale up or down automatically to handle the concurrency and performance needs of the queries submitted to the warehouse. A multi-cluster warehouse has a minimum and maximum number of clusters that can be specified by the administrator. Option B is the most efficient way to support the group of users, as it allows the administrator to create a multi-cluster warehouse with MIN_CLUSTERS set to 2, which means that the warehouse will always have two clusters running to handle the standard workload. The warehouse can also auto-scale up to the maximum number of clusters (which can be set according to the peak workload) when there is a spike in demand, and then scale down when the demand decreases. The warehouse can also auto-resume and auto-suspend, which means that the warehouse will automatically start when a query is submitted and automatically stop after a period of inactivity. The administrator can also give USAGE privileges to the ANALYST role, which means that the users can use the warehouse to execute queries and load data, but not modify or operate the warehouse. Option A is not efficient, as it requires the users to manually start and stop the warehouse, and increase the number of clusters as needed, which can be time-consuming and error-prone. Option C is not efficient, as it creates a standard X-Large warehouse, which is equivalent to four small clusters, which may be more than needed for the standard workload, and may not be enough for the peak workload. Option D is not efficient, as it creates four virtual warehouses of different sizes, which can be confusing and cumbersome for the users to select the appropriate warehouse based on how many queries are being run, and may also result in wasted resources and costs.


NEW QUESTION # 40
An Administrator is evaluating a complex query using the EXPLAIN command. The Globalstats operation indicates 500 partitionsAssigned.
The Administrator then runs the query to completion and opens the Query Profile. They notice that the partitions scanned value is 429.
Why might the actual partitions scanned be lower than the estimate from the EXPLAIN output?

  • A. Runtime optimizations such as join pruning can reduce the number of partitions and bytes scanned during query execution.
  • B. The GlobalStats partition assignment includes the micro-partitions that will be assigned for preservation of the query results.
  • C. In-flight data compression will result in fewer micro-partitions being scanned at the virtual warehouse layer than were identified at the storage layer.
  • D. The EXPLAIN results always include a 10-15% safety factor in order to provide conservative estimates.

Answer: A

Explanation:
The EXPLAIN command returns the logical execution plan for a query, which shows the upper bound estimates for the number of partitions and bytes that might be scanned by the query1. However, these estimates do not account for the runtime optimizations that Snowflake performs to improve the query performance and reduce the resource consumption2. One of these optimizations is join pruning, which eliminates unnecessary partitions from the join inputs based on the join predicates2. This can result in fewer partitions and bytes scanned than the estimates from the EXPLAIN output3. Therefore, the actual partitions scanned value in the Query Profile can be lower than the partitionsAssigned value in the EXPLAIN output4.


NEW QUESTION # 41
A company has many users in the role ANALYST who routinely query Snowflake through a reporting tool.
The Administrator has noticed that the ANALYST users keep two
small clusters busy all of the time, and occasionally they need three or four clusters of that size.
Based on this scenario, how should the Administrator set up a virtual warehouse to MOST efficiently support this group of users?

  • A. Create four virtual warehouses (sized Small through XL) and set them to auto-suspend and auto-resume.Have users in the ANALYST role select the appropriate warehouse based on how many queries are being run.
  • B. Create a standard X-Large warehouse, which is equivalent to four small clusters. Set the warehouse to auto-resume and auto-suspend, and give USAGE privileges to the ANALYST role.
  • C. Create a multi-cluster warehouse with MIN_CLUSTERS set to 2. Set the warehouse to auto-resume and auto-suspend, and give USAGE privileges to the ANALYST role. Allow the warehouse to auto-scale.
  • D. Create a multi-cluster warehouse with MIN_CLUSTERS set to 1. Give MANAGE privileges to the ANALYST role so this group can start and stop the warehouse, and increase the number of clusters as needed.

Answer: C

Explanation:
Explanation
According to the Snowflake documentation1, a multi-cluster warehouse is a virtual warehouse that consists of multiple clusters of compute resources that can scale up or down automatically to handle the concurrency and performance needs of the queries submitted to the warehouse. A multi-cluster warehouse has a minimum and maximum number of clusters that can be specified by the administrator. Option B is the most efficient way to support the group of users, as it allows the administrator to create a multi-cluster warehouse with MIN_CLUSTERS set to 2, which means that the warehouse will always have two clusters running to handle the standard workload. The warehouse can also auto-scale up to the maximum number of clusters (which can be set according to the peak workload) when there is a spike in demand, and then scale down when the demand decreases. The warehouse can also auto-resume and auto-suspend, which means that the warehouse will automatically start when a query is submitted and automatically stop after a period of inactivity. The administrator can also give USAGE privileges to the ANALYST role, which means that the users can use the warehouse to execute queries and load data, but not modify or operate the warehouse. Option A is not efficient, as it requires the users to manually start and stop the warehouse, and increase the number of clusters as needed, which can be time-consuming and error-prone. Option C is not efficient, as it creates a standard X-Large warehouse, which is equivalent to four small clusters, which may be more than needed for the standard workload, and may not be enough for the peak workload. Option D is not efficient, as it creates four virtual warehouses of different sizes, which can be confusing and cumbersome for the users to select the appropriate warehouse based on how many queries are being run, and may also result in wasted resources and costs.


NEW QUESTION # 42
A Snowflake Administrator needs to set up Time Travel for a presentation area that includes facts and dimensions tables, and receives a lot of meaningless and erroneous loT data. Time Travel is being used as a component of the company's data quality process in which the ingestion pipeline should revert to a known quality data state if any anomalies are detected in the latest load. Data from the past 30 days may have to be retrieved because of latencies in the data acquisition process.
According to best practices, how should these requirements be met? (Select TWO).

  • A. The fact and dimension tables should have the same DATA_RETENTION_TIME_IN_ DAYS.
  • B. Related data should not be placed together in the same schema. Facts and dimension tables should each have their own schemas.
  • C. Only TRANSIENT tables should be used to ensure referential integrity between the fact and dimension tables.
  • D. The fact and dimension tables should be cloned together using the same Time Travel options to reduce potential referential integrity issues with the restored data.
  • E. The DATA_RETENTION_TIME_IN_DAYS should be kept at the account level and never used for lower level containers (databases and schemas).

Answer: A,D

Explanation:
Explanation
According to the Understanding & Using Time Travel documentation, Time Travel is a feature that allows you to query, clone, and restore historical data in tables, schemas, and databases for up to 90 days. To meet the requirements of the scenario, the following best practices should be followed:
*The fact and dimension tables should have the same DATA_RETENTION_TIME_IN_DAYS. This parameter specifies the number of days for which the historical data is preserved and can be accessed by Time Travel. To ensure that the fact and dimension tables can be reverted to a consistent state in case of any anomalies in the latest load, they should have the same retention period. Otherwise, some tables may lose their historical data before others, resulting in data inconsistency and quality issues.
*The fact and dimension tables should be cloned together using the same Time Travel options to reduce potential referential integrity issues with the restored data. Cloning is a way of creating a copy of an object (table, schema, or database) at a specific point in time using Time Travel. To ensure that the fact and dimension tables are cloned with the same data set, they should be cloned together using the same AT or BEFORE clause. This will avoid any referential integrity issues that may arise from cloning tables at different points in time.
The other options are incorrect because:
*Related data should not be placed together in the same schema. Facts and dimension tables should each have their own schemas. This is not a best practice for Time Travel, as it does not affect the ability to query, clone, or restore historical data. However, it may be a good practice for data modeling and organization, depending on the use case and design principles.
*The DATA_RETENTION_TIME_IN_DAYS should be kept at the account level and never used for lower level containers (databases and schemas). This is not a best practice for Time Travel,as it limits the flexibility and granularity of setting the retention period for different objects. The retention period can be set at the account, database, schema, or table level, and the most specific setting overrides the more general ones. This allows for customizing the retention period based on the data needs and characteristics of each object.
*Only TRANSIENT tables should be used to ensure referential integrity between the fact and dimension tables. This is not a best practice for Time Travel, as it does not affect the referential integrity between the tables. Transient tables are tables that do not have a Fail-safe period, which means that they cannot be recovered by Snowflake after the retention period ends. However, they still support Time Travel within the retention period, and can be queried, cloned, and restored like permanent tables. The choice of table type depends on the data durability and availability requirements, not on the referential integrity.


NEW QUESTION # 43
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