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Amazon-DEA-C01 Exam Info
Amazon-DEA-C01 Exam Questions

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Amazon-DEA-C01 Exam Questions

Unlock Your Data Engineering Future: Master Amazon AWS Certified Data Engineer - Associate Amazon-DEA-C01

Ready to transform your career and become a sought-after data engineering expert? Our comprehensive Amazon-DEA-C01 practice questions are your secret weapon for acing the AWS Certified Data Engineer - Associate exam. Crafted by industry veterans, our materials go beyond mere memorization, diving deep into real-world scenarios you'll face in the field. Whether you prefer studying on-the-go with our PDF version, enjoy interactive learning through our web-based platform, or want the robust features of our desktop software, we've got you covered. Don't let imposter syndrome hold you back – join thousands of successful candidates who've leveraged our resources to land dream roles at top tech companies. With the booming demand for AWS data engineering skills, there's never been a better time to invest in your future. Act now and save 20% on our limited-time bundle offer!

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Question 1

A company is building a data stream processing application. The application runs in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The application stores processed data in an Amazon DynamoDB table.

The company needs the application containers in the EKS cluster to have secure access to the DynamoDB table. The company does not want to embed AWS credentials in the containers.

Which solution will meet these requirements?

AStore the AWS credentials in an Amazon S3 bucket. Grant the EKS containers access to the S3 bucket to retrieve the credentials.

BAttach an IAM role to the EKS worker nodes. Grant the IAM role access to DynamoDB. Use the IAM role to set up IAM roles service accounts (IRSA) functionality.

CCreate an IAM user that has an access key to access the DynamoDB table. Use environment variables in the EKS containers to store the IAM user access key data.

DCreate an IAM user that has an access key to access the DynamoDB table. Use Kubernetes secrets that are mounted in a volume of the EKS cluster nodes to store the user access key data.

Correct : B

In this scenario, the company is using Amazon Elastic Kubernetes Service (EKS) and wants secure access to DynamoDB without embedding credentials inside the application containers. The best practice is to use IAM roles for service accounts (IRSA), which allows assigning IAM roles to Kubernetes service accounts. This lets the EKS pods assume specific IAM roles securely, without the need to store credentials in containers.

IAM Roles for Service Accounts (IRSA):

With IRSA, each pod in the EKS cluster can assume an IAM role that grants access to DynamoDB without needing to manage long-term credentials. The IAM role can be attached to the service account associated with the pod.

This ensures least privilege access, improving security by preventing credentials from being embedded in the containers.

Alternatives Considered:

A (Storing AWS credentials in S3): Storing AWS credentials in S3 and retrieving them introduces security risks and violates the principle of not embedding credentials.

C (IAM user access keys in environment variables): This also embeds credentials, which is not recommended.

D (Kubernetes secrets): Storing user access keys as secrets is an option, but it still involves handling long-term credentials manually, which is less secure than using IRSA.

IAM Best Practices for Amazon EKS

Secure Access to DynamoDB from EKS

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AStore the AWS credentials in an Amazon S3 bucket. Grant the EKS containers access to the S3 bucket to retrieve the credentials.

BAttach an IAM role to the EKS worker nodes. Grant the IAM role access to DynamoDB. Use the IAM role to set up IAM roles service accounts (IRSA) functionality.

CCreate an IAM user that has an access key to access the DynamoDB table. Use environment variables in the EKS containers to store the IAM user access key data.

DCreate an IAM user that has an access key to access the DynamoDB table. Use Kubernetes secrets that are mounted in a volume of the EKS cluster nodes to store the user access key data.

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Question 2

A technology company currently uses Amazon Kinesis Data Streams to collect log data in real time. The company wants to use Amazon Redshift for downstream real-time queries and to enrich the log data.

Which solution will ingest data into Amazon Redshift with the LEAST operational overhead?

ASet up an Amazon Data Firehose delivery stream to send data to a Redshift provisioned cluster table.

BSet up an Amazon Data Firehose delivery stream to send data to Amazon S3. Configure a Redshift provisioned cluster to load data every minute.

CConfigure Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to send data directly to a Redshift provisioned cluster table.

DUse Amazon Redshift streaming ingestion from Kinesis Data Streams and to present data as a materialized view.

Correct : D

The most efficient and low-operational-overhead solution for ingesting data into Amazon Redshift from Amazon Kinesis Data Streams is to use Amazon Redshift streaming ingestion. This feature allows Redshift to directly ingest streaming data from Kinesis Data Streams and process it in real-time.

Amazon Redshift Streaming Ingestion:

Redshift supports native streaming ingestion from Kinesis Data Streams, allowing real-time data to be queried using materialized views.

This solution reduces operational complexity because you don't need intermediary services like Amazon Kinesis Data Firehose or S3 for batch loading.

Alternatives Considered:

A (Data Firehose to Redshift): This option is more suitable for batch processing but incurs additional operational overhead with the Firehose setup.

B (Firehose to S3): This involves an intermediate step, which adds complexity and delays the real-time requirement.

C (Managed Service for Apache Flink): This would work but introduces unnecessary complexity compared to Redshift's native streaming ingestion.

Amazon Redshift Streaming Ingestion from Kinesis

Materialized Views in Redshift

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ASet up an Amazon Data Firehose delivery stream to send data to a Redshift provisioned cluster table.

BSet up an Amazon Data Firehose delivery stream to send data to Amazon S3. Configure a Redshift provisioned cluster to load data every minute.

CConfigure Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to send data directly to a Redshift provisioned cluster table.

DUse Amazon Redshift streaming ingestion from Kinesis Data Streams and to present data as a materialized view.

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Question 3

A banking company uses an application to collect large volumes of transactional dat

a. The company uses Amazon Kinesis Data Streams for real-time analytics. The company's application uses the PutRecord action to send data to Kinesis Data Streams.

A data engineer has observed network outages during certain times of day. The data engineer wants to configure exactly-once delivery for the entire processing pipeline.

Which solution will meet this requirement?

ADesign the application so it can remove duplicates during processing by embedding a unique ID in each record at the source.

BUpdate the checkpoint configuration of the Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) data collection application to avoid duplicate processing of events.

CDesign the data source so events are not ingested into Kinesis Data Streams multiple times.

DStop using Kinesis Data Streams. Use Amazon EMR instead. Use Apache Flink and Apache Spark Streaming in Amazon EMR.

Correct : A

For exactly-once delivery and processing in Amazon Kinesis Data Streams, the best approach is to design the application so that it handles idempotency. By embedding a unique ID in each record, the application can identify and remove duplicate records during processing.

Exactly-Once Processing:

Kinesis Data Streams does not natively support exactly-once processing. Therefore, idempotency should be designed into the application, ensuring that each record has a unique identifier so that the same event is processed only once, even if it is ingested multiple times.

This pattern is widely used for achieving exactly-once semantics in distributed systems.

Alternatives Considered:

B (Checkpoint configuration): While updating the checkpoint configuration can help with some aspects of duplicate processing, it is not a full solution for exactly-once delivery.

C (Design data source): Ensuring events are not ingested multiple times is ideal, but network outages can make this difficult, and it doesn't guarantee exactly-once delivery.

D (Using EMR): While using EMR with Flink or Spark could work, it introduces unnecessary complexity compared to handling idempotency at the application level.

Amazon Kinesis Best Practices for Exactly-Once Processing

Achieving Idempotency with Amazon Kinesis

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ADesign the application so it can remove duplicates during processing by embedding a unique ID in each record at the source.

CDesign the data source so events are not ingested into Kinesis Data Streams multiple times.

DStop using Kinesis Data Streams. Use Amazon EMR instead. Use Apache Flink and Apache Spark Streaming in Amazon EMR.

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Question 4

A company saves customer data to an Amazon S3 bucket. The company uses server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the bucket. The dataset includes personally identifiable information (PII) such as social security numbers and account details.

Data that is tagged as PII must be masked before the company uses customer data for analysis. Some users must have secure access to the PII data during the preprocessing phase. The company needs a low-maintenance solution to mask and secure the PII data throughout the entire engineering pipeline.

Which combination of solutions will meet these requirements? (Select TWO.)

AUse AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask the PII data before analysis.

BUse Amazon GuardDuty to monitor access patterns for the PII data that is used in the engineering pipeline.

CConfigure an Amazon Made discovery job for the S3 bucket.

DUse AWS Identity and Access Management (IAM) to manage permissions and to control access to the PII data.

EWrite custom scripts in an application to mask the PII data and to control access.

Correct : A, D

To address the requirement of masking PII data and ensuring secure access throughout the data pipeline, the combination of AWS Glue DataBrew and IAM provides a low-maintenance solution.

A . AWS Glue DataBrew for Masking:

AWS Glue DataBrew provides a visual tool to perform data transformations, including masking PII data. It allows for easy configuration of data transformation tasks without requiring manual coding, making it ideal for this use case.

D . AWS Identity and Access Management (IAM):

Using IAM policies allows fine-grained control over access to PII data, ensuring that only authorized users can view or process sensitive data during the pipeline stages.

Alternatives Considered:

B (Amazon GuardDuty): GuardDuty is for threat detection and does not handle data masking or access control for PII.

C (Amazon Macie): Macie can help discover sensitive data but does not handle the masking of PII or access control.

E (Custom scripts): Custom scripting increases the operational burden compared to a built-in solution like DataBrew.

AWS Glue DataBrew for Data Masking

IAM Policies for PII Access Control

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AUse AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask the PII data before analysis.

BUse Amazon GuardDuty to monitor access patterns for the PII data that is used in the engineering pipeline.

CConfigure an Amazon Made discovery job for the S3 bucket.

DUse AWS Identity and Access Management (IAM) to manage permissions and to control access to the PII data.

EWrite custom scripts in an application to mask the PII data and to control access.

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Question 5

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?

AInstruct 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.

BCreate an AWS Direct Connect connection to the on-premises data center. Store the service account credentials in AWS Secrets manager.

CCreate 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.

DCreate 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 resigned URLS that have one-day expiration dates.

Correct : B

For secure migration of data from an on-premises data center to AWS without using the public internet, AWS Direct Connect is the most secure and reliable method. Using Secrets Manager to store service account credentials ensures that the credentials are managed securely with automatic rotation.

AWS Direct Connect:

Direct Connect establishes a dedicated, private connection between the on-premises data center and AWS, avoiding the public internet. This is ideal for secure, high-speed data transfers.

AWS Secrets Manager:

Secrets Manager securely stores and rotates service account credentials, reducing operational overhead while ensuring security.

Alternatives Considered:

A (ECS with security groups): This does not address the need for a secure, private connection from the on-premises data center.

C (Public subnet with presigned URLs): This involves using the public internet, which does not meet the requirement.

D (Direct Connect with presigned URLs): While Direct Connect is correct, presigned URLs with short expiration dates are unnecessary for this use case.

AWS Direct Connect Documentation

AWS Secrets Manager Documentation