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Snowflake DEA-C01 Exam Syllabus Topics:
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Reference: https://learn.snowflake.com/en/certifications/snowpro-advanced-dataengineer/
Snowflake SnowPro Advanced: Data Engineer Certification Sample Questions:
A healthcare company stores patient records in an on-premises MySQL database. The company creates an application to access the MySQL database. The company must enforce security protocols to protect the patient records. The company currently rotates database credentials every 30 days to minimize the risk of unauthorized access.
The company wants a solution that does require the company to modify the application code for each credential rotation.
Which solution will meet this requirement with the LEAST operational overhead?
- A. Assign an IAM role access permissions to the database. Configure the application to obtain temporary credentials through the IAM role.
- B. Store credentials in an encrypted Amazon S3 bucket. Rotate the credentials every month by using an S3 Lifecycle policy. Use bucket policies to control access.
- C. Use AWS Key Management Service (AWS KMS) to generate encryption keys. Configure automatic key rotation. Store the encrypted credentials in an Amazon DynamoDB table.
- D. Use AWS Secrets Manager to automatically rotate credentials. Allow the application to retrieve the credentials by using API calls.
Correct Answer: D 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
A company uses an Amazon RDS for Oracle database to store data for an ecommerce application. The company needs a solution to make the data in all tables available for future analytics. The solution must process data from a table named customer_complaints in near real time by using a REST API.
Which solution will meet these requirements MOST cost-effectively?
- A. Use AWS Database Migration Service (AWS DMS) to replicate the Oracle database to an Amazon RDS for PostgreSQL database instance. Create an AWS Lambda function to process data from the PostgreSQL customer_complaints table and to call the REST API.
- B. Create a task in AWS Database Migration Service (AWS DMS) to replicate the Oracle database to Amazon Redshift. Set the Oracle database as the source. Configure an AWS Lambda function to use the Amazon Redshift Data API to query the data from the customer_complaints table and to call the REST API. Unload the data from Amazon Redshift to Amazon S3.
- C. Create a read replica of the Amazon RDS for Oracle database. Configure an AWS Lambda function to process data from the read replica customer_complaints table and to call the REST API.
- D. Set up one AWS Database Migration Service (AWS DMS) task to stream the customer_complaints table data to Amazon Kinesis Data Streams. Set up a second AWS DMS task to send data to an Amazon S3 bucket. Configure an AWS Lambda function to process Kinesis data and to call the REST API.
Correct Answer: D 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
A company stores raw clickstream data in an Amazon S3 bucket. The company needs a solution to process the data every day by using complex PySpark transformations that rely on custom internal libraries. After the data is transformed, the company must store the data in Amazon Redshift for analytics. The solution must be highly scalable to handle large data workloads. Which solution will meet these requirements with the LEAST operational overhead?
- A. Use Amazon EC2 Auto Scaling groups with a custom AMI that contains the custom libraries to run a PySpark application.
- B. Use AWS Glue Studio to build and schedule PySpark jobs. Configure an AWS Glue data connection that includes the custom libraries.
- C. Use Amazon EMR to run PySpark jobs. Use bootstrap actions to install the custom libraries
- D. Use Amazon SageMaker Processing jobs to run PySpark code that uses native SageMaker libraries.
Correct Answer: B 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB.
The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)
- A. Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables. Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.
- B. Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes.
Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables. - C. Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.
- D. Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
- E. Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
Correct Answer: B,D 🗳️
A data engineer needs to onboard a new data producer into AWS. The data producer needs to migrate data products to AWS.
The data producer maintains many data pipelines that support a business application. Each pipeline must have service accounts and their corresponding credentials. The data engineer must establish a secure connection from the data producer's on-premises data center to AWS. The data engineer must not use the public internet to transfer data from an on-premises data center to AWS.
Which solution will meet these requirements?
- A. Create a security group in a public subnet. Configure the security group to allow only connections from the CIDR blocks that correspond to the data producer. Create Amazon S3 buckets than contain presigned URLS that have one-day expiration dates.
- B. Instruct the new data producer to create Amazon Machine Images (AMIs) on Amazon Elastic Container Service (Amazon ECS) to store the code base of the application. Create security groups in a public subnet that allow connections only to the on-premises data center.
- C. Create an AWS Direct Connect connection to the on-premises data center. Store the service account credentials in AWS Secrets manager.
- D. Create an AWS Direct Connect connection to the on-premises data center. Store the application keys in AWS Secrets Manager. Create Amazon S3 buckets that contain presigned URLS that have one-day expiration dates.
Correct Answer: C 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
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