WHERE TO START YOUR AMAZON DATA-ENGINEER-ASSOCIATE EXAM PREPARATION?

Where To Start Your Amazon Data-Engineer-Associate Exam Preparation?

Where To Start Your Amazon Data-Engineer-Associate Exam Preparation?

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Many applicants do not fulfill their dream of becoming professionals because of using outdated exam preparation material. Failure in the AWS Certified Data Engineer - Associate (DEA-C01) exam leads them to anxiety. If this situation sounds familiar, do not waste time and get your hands on Amazon Data-Engineer-Associate for exam preparation.

Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q49-Q54):

NEW QUESTION # 49
A data engineer is configuring Amazon SageMaker Studio to use AWS Glue interactive sessions to prepare data for machine learning (ML) models.
The data engineer receives an access denied error when the data engineer tries to prepare the data by using SageMaker Studio.
Which change should the engineer make to gain access to SageMaker Studio?

  • A. Add the AWSGlueServiceRole managed policy to the data engineer's IAM user.
  • B. Add a policy to the data engineer's IAM user that allows the sts:AddAssociation action for the AWS Glue and SageMaker service principals in the trust policy.
  • C. Add the AmazonSageMakerFullAccess managed policy to the data engineer's IAM user.
  • D. Add a policy to the data engineer's IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy.

Answer: D

Explanation:
This solution meets the requirement of gaining access to SageMaker Studio to use AWS Glue interactive sessions. AWS Glue interactive sessions are a way to use AWS Glue DataBrew and AWS Glue Data Catalog from within SageMaker Studio. To use AWS Glue interactive sessions, the data engineer's IAM user needs to have permissions to assume the AWS Glue service role and the SageMaker execution role. By adding a policy to the data engineer's IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy, the data engineer can grant these permissions and avoid the access denied error. The other options are not sufficient or necessary to resolve the error. References:
Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions Troubleshoot Errors - Amazon SageMaker AccessDeniedException on sagemaker:CreateDomain in AWS SageMaker Studio, despite having SageMakerFullAccess


NEW QUESTION # 50
A company is using an AWS Transfer Family server to migrate data from an on-premises environment to AWS. Company policy mandates the use of TLS 1.2 or above to encrypt the data in transit.
Which solution will meet these requirements?

  • A. Install an SSL certificate on the Transfer Family server to encrypt data transfers by using TLS 1.2.
  • B. Generate new SSH keys for the Transfer Family server. Make the old keys and the new keys available for use.
  • C. Update the security group rules for the on-premises network to allow only connections that use TLS 1.2 or above.
  • D. Update the security policy of the Transfer Family server to specify a minimum protocol version of TLS 1.2.

Answer: D

Explanation:
The AWS Transfer Family server's security policy can be updated to enforce TLS 1.2 or higher, ensuring compliance with company policy for encrypted data transfers.
AWS Transfer Family Security Policy:
AWS Transfer Family supports setting a minimum TLS version through its security policy configuration. This ensures that only connections using TLS 1.2 or above are allowed.
Reference:
Alternatives Considered:
A (Generate new SSH keys): SSH keys are unrelated to TLS and do not enforce encryption protocols like TLS 1.2.
B (Update security group rules): Security groups control IP-level access, not TLS versions.
D (Install SSL certificate): SSL certificates ensure secure connections, but the TLS version is controlled via the security policy.
AWS Transfer Family Documentation


NEW QUESTION # 51
A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

  • A. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
  • B. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
  • C. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.
  • D. Use the Amazon Redshift Data API to publish an event to Amazon EventBridqe. Configure an EventBridge rule to invoke the Lambda function.

Answer: A

Explanation:
The Amazon Redshift Data API enables you to interact with your Amazon Redshift data warehouse in an easy and secure way. You can use the Data API to run SQL commands, such as loading data into tables, without requiring a persistent connection to the cluster. The Data API also integrates with Amazon EventBridge, which allows you to monitor the execution status of your SQL commands and trigger actions based on events. By using the Data API to publish an event to EventBridge, the data engineer can invoke the Lambda function that writes the load statuses to the DynamoDB table. This solution is scalable, reliable, and cost-effective. The other options are either not possible or not optimal. You cannot use a second Lambda function to invoke the first Lambda function based on CloudWatch or CloudTrail events, as these services do not capture the load status of Redshift tables. You can use the Data API to publish a message to an SQS queue, but this would require additional configuration and polling logic to invoke the Lambda function from the queue. This would also introduce additional latency and cost. Reference:
Using the Amazon Redshift Data API
Using Amazon EventBridge with Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift


NEW QUESTION # 52
A company uses Amazon S3 to store data and Amazon QuickSight to create visualizations.
The company has an S3 bucket in an AWS account named Hub-Account. The S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The company's QuickSight instance is in a separate account named BI-Account The company updates the S3 bucket policy to grant access to the QuickSight service role. The company wants to enable cross-account access to allow QuickSight to interact with the S3 bucket.
Which combination of steps will meet this requirement? (Select TWO.)

  • A. Use the existing AWS KMS key to encrypt connections from QuickSight to the S3 bucket.
  • B. Add an IAM policy to the QuickSight service role to give QuickSight access to the KMS key that encrypts the S3 bucket.
  • C. Use AWS Resource Access Manager (AWS RAM) to share the S3 bucket with the Bl-Account account.
  • D. Add the 53 bucket as a resource that the QuickSight service role can access.
  • E. Add the KMS key as a resource that the QuickSight service role can access.

Answer: B,E

Explanation:
Problem Analysis:
The company needs cross-account access to allow QuickSight in BI-Account to interact with an S3 bucket in Hub-Account.
The bucket is encrypted with an AWS KMS key.
Appropriate permissions must be set for both S3 access and KMS decryption.
Key Considerations:
QuickSight requires IAM permissions to access S3 data and decrypt files using the KMS key.
Both S3 and KMS permissions need to be properly configured across accounts.
Solution Analysis:
Option A: Use Existing KMS Key for Encryption
While the existing KMS key is used for encryption, it must also grant decryption permissions to QuickSight.
Option B: Add S3 Bucket to QuickSight Role
Granting S3 bucket access to the QuickSight service role is necessary for cross-account access.
Option C: AWS RAM for Bucket Sharing
AWS RAM is not required; bucket policies and IAM roles suffice for granting cross-account access.
Option D: IAM Policy for KMS Access
QuickSight's service role in BI-Account needs explicit permissions to use the KMS key for decryption.
Option E: Add KMS Key as Resource for Role
The KMS key must explicitly list the QuickSight role as an entity that can access it.
Implementation Steps:
S3 Bucket Policy in Hub-Account:
Add a policy to the S3 bucket granting the QuickSight service role access:
json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": { "AWS": "arn:aws:iam::<BI-Account-ID>:role/service-role/QuickSightRole" },
"Action": "s3:GetObject",
"Resource": "arn:aws:s3:::<Bucket-Name>/*"
}
]
}
KMS Key Policy in Hub-Account:
Add permissions for the QuickSight role:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": { "AWS": "arn:aws:iam::<BI-Account-ID>:role/service-role/QuickSightRole" },
"Action": [
"kms:Decrypt",
"kms:DescribeKey"
],
"Resource": "*"
}
]
}
IAM Policy for QuickSight Role in BI-Account:
Attach the following policy to the QuickSight service role:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:GetObject",
"kms:Decrypt"
],
"Resource": [
"arn:aws:s3:::<Bucket-Name>/*",
"arn:aws:kms:<region>:<Hub-Account-ID>:key/<KMS-Key-ID>"
]
}
]
}
Reference:
Setting Up Cross-Account S3 Access
AWS KMS Key Policy Examples
Amazon QuickSight Cross-Account Access


NEW QUESTION # 53
A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. Create a materialized view to read data from the stream. Set the materialized view to auto refresh.
  • B. Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3. Use the COPY command to load the data from Amazon S3 to a table in Amazon Redshift.
  • C. Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift to make the data immediately available for real-time analysis.
  • D. Access the data from Kinesis Data Streams by using SQL queries. Create materialized views directly on top of the stream. Refresh the materialized views regularly to query the most recent stream data.

Answer: A

Explanation:
This solution meets the requirements of implementing real-time analytics capabilities with the least operational overhead. By creating an external schema in Amazon Redshift, you can access the data from Kinesis Data Streams using SQL queries without having to load the data into the cluster. By creating a materialized view on top of the stream, you can store the results of the query in the cluster and make them available for analysis. By setting the materialized view to auto refresh, you can ensure that the view is updated with the latest data from the stream at regular intervals. This way, you can derive near real-time insights by using existing BI and analytics tools. Reference:
Amazon Redshift streaming ingestion
Creating an external schema for Amazon Kinesis Data Streams
Creating a materialized view for Amazon Kinesis Data Streams


NEW QUESTION # 54
......

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