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Exam DEA-C01 Format | Latest DEA-C01 Examprep

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
For evolving schema and high compatibility, which data format should be chosen for downstream analytics?

A. JSON
B. Parquet
C. CSV
D. Avro

Answer: D

NEW QUESTION # 105
Company A and Company B both have Snowflake accounts. Company A's account is hosted on a different cloud provider and region than Company B's account Companies A and B are not in the same Snowflake organization.
How can Company A share data with Company B? (Select TWO).

A. Create a separate database within Company A's account to contain only those data sets they wish to share with Company B Create a share within Company A's account and add all the objects within this separate database to the share Add Company B's account as a recipient of the share
B. Use database replication to replicate Company A's data into Company B's account Create a share within Company B's account and grant users within Company B's account access to the share
C. Create a share within Company A's account, and create a reader account that is a recipient of the share Grant Company B access to the reader account
D. Create a new account within Company A's organization in the same cloud provider and region as Company B's account Use database replication to replicate Company A's data to the new account Create a share within the new account and add Company B's account as a recipient of that share
E. Create a share within Company A's account and add Company B's account as a recipient of that share

Answer: A,E

Explanation:
Explanation
The ways that Company A can share data with Company B are:
Create a share within Company A's account and add Company B's account as a recipient of that share:
This is a valid way to share data between different accounts on different cloud platforms and regions.
Snowflake supports cross-cloud and cross-region data sharing, which allows users to create shares and grant access to other accounts regardless of their cloud platform or region. However, this option may incur additional costs for network transfer and storage replication.
Create a separate database within Company A's account to contain only those data sets they wish to share with Company B Create a share within Company A'saccount and add all the objects within this separate database to the share Add Company B's account as a recipient of the share: This is also a valid way to share data between different accounts on different cloud platforms and regions. This option is similar to the previous one, except that it uses a separate database to isolate the data sets that need to be shared. This can improve security and manageability of the shared data. The other options are not valid because:
Create a share within Company A's account, and create a reader account that is a recipient of the share Grant Company B access to the reader account: This option is not valid because reader accounts are not supported for cross-cloud or cross-region data sharing. Reader accounts are Snowflake accounts that can only consume data from shares created by their provider account. Reader accounts must be on the same cloud platform and region as their provider account.
Use database replication to replicate Company A's data into Company B's account Create a share within Company B's account and grant users within Company B's account access to the share: This option is not valid because database replication cannot be used for cross-cloud or cross-region data sharing.
Database replication is a feature in Snowflake that allows users to copy databases across accounts within the same cloud platform and region. Database replication cannot copy databases across different cloud platforms or regions.
Create a new account within Company A's organization in the same cloud provider and region as Company B's account Use database replication to replicate Company A's data to the new account Create a share within the new account and add Company B's account as a recipient of that share: This option is not valid because it involves creating a new account within Company A's organization, which may not be feasible or desirable for Company A. Moreover, this option is unnecessary, as Company A can directly share data with Company B without creating an intermediate account.

NEW QUESTION # 106
A company needs to use an Aws Glue PySpark job to read specific data from an Amazon DynamoDB table. The company knows the partition key values for the required records. The existing processing logic of the AWS Glue PySpark job requires the data to be in DynamicFrame format. The company needs a solution to ensure that the job reads only the specified data. Which solution will meet this requirement with the MINIMUM number of read capacity units (RCUs)?

A. Perform a query on the DynamoDB table in the AWS Glue job. Use the partition key in the key condition expression. Put the data into a DynamicFrame.
B. Perform a query on the DynamoDB table in the AWS Glue job by using only the sort key in the key condition expression. Load the data into a DynamicFrame.
C. Use the AWS Glue DynamoDB ETL connector to read the DynamoDB table. Use the filter option to read the required partition key.
D. Perform a scan on the DynamoDB table in the Aws Glue job. Put the data into a DynamicFrame.
Filter the DynamicFrame on the partition key.

Answer: A

Explanation:
A DynamoDB Query that uses the partition key in the key condition expression reads only the items in the specified partition(s), which minimizes consumed RCUs compared with any Scan- based approach. The job can then convert the query results into a DynamicFrame to satisfy the existing processing logic.

NEW QUESTION # 107
A company has AWS resources in multiple AWS Regions. The company has an Amazon EFS file system in each Region where the company operates. The company's data science team operates within only a single Region. The data that the data science team works with must remain within the team's Region.
A data engineer needs to create a single dataset by processing files that are in each of the company's Regional EFS file systems. The data engineer wants to use an AWS Step Functions state machine to orchestrate AWS Lambda functions to process the data.
Which solution will meet these requirements with the LEAST effort?

A. Peer the VPCs that host the EFS file systems in each Region with the VPC that is in the data science team's Region. Enable EFS file locking.
Configure the Lambda functions in the data science team's Region to mount each of the Region specific file systems. Use the Lambda functions to process the data.
B. Configure each of the Regional EFS file systems to replicate data to the data science team's Region. In the data science team's Region, configure the Lambda functions to mount the replica file systems. Use the Lambda functions to process the data.
C. Deploy the Lambda functions to each Region. Mount the Regional EFS file systems to the Lambda functions. Use the Lambda functions to process the data. Store the output in an Amazon S3 bucket in the data science team's Region.
D. Use AWS DataSync to transfer files from each of the Regional EFS files systems to the file system that is in the data science team's Region.Configure the Lambda functions in the data science team's Region to mount the file system that is in the same Region. Use the Lambda functions to process the data.

Answer: D

Explanation:
AWS DataSync is designed for transferring data between AWS storage services across regions, such as EFS, S3, and other storage systems. By using DataSync, you can efficiently transfer data from each of the Regional EFS file systems to the EFS file system in the data science team's Region.
This allows the data to remain within the data science team's Region while being processed, which meets the requirement that data cannot leave the Region.
Once the data is transferred, Lambda functions in the same Region as the data science team can mount the local EFS file system and process the data, minimizing complexity and the risk of network-related latency or failures from cross-region data processing.
This approach ensures minimal operational effort because DataSync automates the transfer process and works seamlessly with EFS, while keeping the data localized for processing by Lambda functions in the same Region.

NEW QUESTION # 108
A company stores customer data that contains personally identifiable information (PII) in an Amazon Redshift cluster. The company's marketing, claims, and analytics teams need to be able to access the customer data.
The marketing team should have access to obfuscated claim information but should have full access to customer contact information. The claims team should have access to customer information for each claim that the team processes. The analytics team should have access only to obfuscated PII data.
Which solution will enforce these data access requirements with the LEAST administrative overhead?

A. Create a separate Redshift cluster for each team. Load only the required data for each team.
Restrict access to clusters based on the teams.
B. Create views that include required fields for each of the data requirements. Grant the teams access only to the view that each team requires.
C. Create a separate Amazon Redshift database role for each team. Define masking policies that apply for each team separately. Attach appropriate masking policies to each team role.
D. Move the customer data to an Amazon S3 bucket. Use AWS Lake Formation to create a data lake. Use fine-grained security capabilities to grant each team appropriate permissions to access the data.

Answer: C

NEW QUESTION # 109
......

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