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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q330-Q335):
NEW QUESTION # 330
A media company uses software as a service (SaaS) applications to gather data by using third- party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.
Which AWS service or feature will meet these requirements with the LEAST operational overhead?
- A. Amazon Kinesis
- B. Amazon Managed Streaming for Apache Kafka (Amazon MSK)
- C. AWS Glue Data Catalog
- D. Amazon AppFlow
Answer: D
Explanation:
https://d1.awsstatic.com/solutions/guidance/architecture-diagrams/integrating-third-party-saas- data-using-amazon-appflow.pdf Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between Software as a Service (SaaS) applications like Salesforce, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift, in just a few clicks. It can store the raw data pulled from SaaS applications in Amazon S3, and integrates with AWS Glue Data Catalog to catalog and store metadata.
NEW QUESTION # 331
An ecommerce company wants to use AWS to migrate data pipelines from an on-premises environment into the AWS Cloud. The company currently uses a third-party tool in the on- premises environment to orchestrate data ingestion processes.
The company wants a migration solution that does not require the company to manage servers.
The solution must be able to orchestrate Python and Bash scripts. The solution must not require the company to refactor any code.
Which solution will meet these requirements with the LEAST operational overhead?
- A. AWS Step Functions
- B. AWS Glue
- C. Amazon Managed Workflows for Apache Airflow (Amazon MVVAA)
- D. AWS Lambda
Answer: C
Explanation:
All of the components contained in the outer box (in the image below) appear as a single Amazon MWAA environment in your account. The Apache Airflow Scheduler and Workers are AWS Fargate (Fargate) containers that connect to the private subnets in the Amazon VPC for your environment. Each environment has its own Apache Airflow metadatabase managed by AWS that is accessible to the Scheduler and Workers Fargate containers via a privately-secured VPC endpoint.
https://docs.aws.amazon.com/mwaa/latest/userguide/what-is-mwaa.html
NEW QUESTION # 332
A company stores a 100 MB dataset in an Amazon S3 bucket as an Apache Parquet file. A data engineer needs to profile the data before performing data preparation steps on the data. Which solution will meet this requirement in the MOST operationally efficient way?
- A. Ingest the data into Amazon Redshift Spectrum. Use SQL queries to profile the data.
- B. Stream the data into Amazon Managed Service for Apache Flink for SQL queries. Use the Apache Flink dashboard to profile the data.
- C. Load the data into an Amazon QuickSight dataset. Build a topic to profile the data with questions.
- D. Create a profile job on the dataset in AWS Glue DataBrew. Review the profile job results.
Answer: D
Explanation:
AWS Glue DataBrew provides built-in data profiling specifically for exploring dataset structure, data types, distributions, missing values, and anomalies before preparation. For a 100 MB Parquet file in Amazon S3, creating a profile job is the most operationally efficient approach because it requires minimal setup and no custom infrastructure or query framework.
NEW QUESTION # 333
A company uses Amazon Redshift to store order transactions from the current day. The company has an orders table that contains the previous order data. The company also has a staging table that contains new or updated order records.
The company needs to remove stale records from the orders table and insert the most recent data in the orders table from the staging table. Several downstream applications need the orders table to display up-to-date information.
Which solution will meet these requirements?
- A. Write an Amazon Redshift stored procedure that deletes the stale records from the orders table and inserts new records from the staging table.
- B. Unload the orders table and the staging table to Amazon S3. Delete stale orders table data and insert new staging table data in Amazon S3 by using Amazon Athena. Copy the orders S3 table to the orders Amazon Redshift table.
- C. Use Amazon Athena federated queries to read stale records from the orders table. Delete the stale records and insert the records from the staging table into the orders table.
- D. Use Amazon Redshift Spectrum to delete stale records from the orders table and insert records from the staging table into the orders table.
Answer: A
Explanation:
An Amazon Redshift stored procedure can perform the delete-and-insert logic directly inside the warehouse in a single controlled operation. This is the most appropriate way to remove stale rows from the target table and load the latest records from the staging table while keeping the orders table current for downstream applications, without introducing unnecessary data movement or external processing.
NEW QUESTION # 334
Jeff, a Data Engineer, accessing elements in JSON object in its 3 data loading scripts, he unknow-ingly use the upper case while accessing the elements. e.g.
Script 1 --> fruits:apple.sweet
Script 2 --> FRUITS:apple.sweet
Script 3 --> FRUITS:Apple.Sweet
Which are the correct statements?
- A. Script 1,2,3 traverse path will treat in same way.
- B. Script 1 & 3 traverse path will be treated in same way.
- C. Script 2&3 traverse path will be same.
- D. Script 1 & Script 2 traverse path twill be treated same, but Script 2 will not.
Answer: D
Explanation:
Explanation
There are two ways to access elements in a JSON object:
Dot Notation
Bracket Notation
Regardless of which notation you use, the column name is case-insensitive but element names are case-sensitive.
NEW QUESTION # 335
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