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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q42-Q47):
NEW QUESTION # 42
A Data Engineer is working on a Snowflake deployment in AWS eu-west-1 (Ireland). The Engineer is planning to load data from staged files into target tables using the copy into command Which sources are valid? (Select THREE)
- A. Internal stage on AWS eu-central-1 (Frankfurt)
- B. External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt)
- C. External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland)
- D. External stage on GCP us-central1 (Iowa)
- E. Internal stage on GCP us-central1 (Iowa)
- F. SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland)
Answer: B,C,D
Explanation:
Explanation
The valid sources for loading data from staged files into target tables using the copy into command are:
External stage on GCP us-central1 (Iowa): This is a valid source because Snowflake supports cross-cloud data loading from external stages on different cloud platforms and regions than the Snowflake deployment.
External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland): This is a valid source because Snowflake supports data loading from external stages on the same cloud platform and region as the Snowflake deployment.
External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt): This is a valid source because Snowflake supports cross-region data loading from external stages on different regions than the Snowflake deployment within the same cloud platform. The invalid sources are:
Internal stage on GCP us-central1 (Iowa): This is an invalid source because internal stages are always located on the same cloud platform and region as the Snowflake deployment. Therefore, an internal stage on GCP us-central1 (Iowa) cannot be used for a Snowflake deployment on AWS eu-west-1 (Ireland).
Internal stage on AWS eu-central-1 (Frankfurt): This is an invalid source because internal stages are always located on the same region as the Snowflake deployment. Therefore, an internal stage on AWS eu-central-1 (Frankfurt) cannot be used for a Snowflake deployment on AWS eu-west-1 (Ireland).
SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland): This is an invalid source because SSO stands for Single Sign-On, which is a security integration feature in Snowflake, not a data staging option.
NEW QUESTION # 43
Ryan, a Data Engineer, wants to improve the performance of large, complex queries against large data sets. He decided to Scale up underlying warehouse/cluster. What is correct Snowflake consid-eration while scaling up so that he can achieve better performance results? [Select all that apply]
- A. Snowflake supports resizing a warehouse at any time, even while running.
- B. Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is charged for both the new warehouse and the old warehouse while the old warehouse is quiesced.
- C. Scaling up is not intended for handling concurrency issues; instead, use additional warehouses to handle the workload or use a multi-cluster warehouse (if this feature is available for your account).
- D. Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, once fully provisioned, are only used for queued and new queries.
- E. Resizing can help reduce the queuing that occurs if a warehouse does not have enough compute resources to process all the queries that are submitted concurrently.
Answer: A,B,C,D,E
Explanation:
Explanation
Resizing a warehouse generally improves query performance, particularly for larger, more complex queries. It can also help reduce the queuing that occurs if a warehouse does not have enough com-pute resources to process all the queries that are submitted concurrently. Note that warehouse resiz-ing is not intended for handling concurrency issues; instead, use additional warehouses to handle the workload or use a multi-cluster warehouse (if this feature is available for your account).
Snowflake supports resizing a warehouse at any time, even while running. If a query is running slowly and you have additional queries of similar size and complexity that you want to run on the same warehouse, you might choose to resize the warehouse while it is running; however, note the following:
Larger warehouse size is not necessarily faster; for smaller, basic queries that are already executing quickly, you may not see any significant improvement after resizing.
Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, once fully provisioned, are only used for queued and new queries.
Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is charged for both the new warehouse and the old warehouse while the old warehouse is quiesced.
NEW QUESTION # 44
A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3.
Which solution will meet these requirements in the MOST operationally efficient way?
- A. Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.
- B. Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.
- C. Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view.
Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day. - D. Create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
Answer: D
NEW QUESTION # 45
Which Snowflake objects does the Snowflake Kafka connector use? (Select THREE).
- A. Serverless task
- B. Internal user stage
- C. Pipe
- D. Storage integration
- E. Internal named stage
- F. Internal table stage
Answer: C,E,F
Explanation:
Explanation
The Snowflake Kafka connector uses three Snowflake objects: pipe, internal table stage, and internal named stage. The pipe object is used to load data from an external stage into a Snowflake table using COPY statements. The internal table stage is used to store files that are loaded from Kafka topics into Snowflake using PUT commands. The internal named stage is used to store files that are rejected by the COPY statements due to errors or invalid data. The other options are not objects that are used by the Snowflake Kafka connector.
Option B, serverless task, is an object that can execute SQL statements on a schedule without requiring a warehouse. Option C, internal user stage, is an object that can store files for a specific user in Snowflake using PUT commands. Option F, storage integration, is an object that can enable secure access to external cloud storage services without exposing credentials.
NEW QUESTION # 46
Select the correct usage statements with regards to SQL UDF?
- A. You can include only one query expression.
- B. The body of a UDF cannot contain DDL statements or any DML statement other than SELECT.
- C. All of above are correct.
- D. Scalar functions (UDFs) have a limit of 500 input arguments.
- E. When using a query expression in a SQL UDF, do not include a semicolon within the UDF body to terminate the query expression.
Answer: C
NEW QUESTION # 47
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