Data-Engineer-Associate Exam Dumps - Amazon Web Services AWS Certified Data Engineer Questions and Answers

Option C best matches the requirement of no new code with least operational effort because it keeps the analyst’s work inside the AWS-native, visual ETL experience and produces standardized outputs that data engineers can extend. The study material emphasizes that AWS provides visual data preparation capabilities that let users “clean and normalize data without writing any code,” which is exactly what the analyst needs before advanced engineering transformations begin.

Option A requires Python and Pandas, which directly violates the “does not require writing new code” requirement and introduces dependency management and debugging overhead. Option B uses multiple services (Canvas + Data Wrangler + Glue), which increases the number of moving parts, permissions, handoffs, and operational surface area compared to a single-service preparation approach. Option D offloads the entire preparation step to engineers, which increases operational effort and delays because the analyst cannot directly implement and iterate on standardization.

Using recipe-style transformations in the Glue visual interface aligns with the documented goal of simplifying data preparation workflows while enabling the engineering team to add more complex steps later in the pipeline.

AWS Data Exchange is a service that makes it easy to find, subscribe to, and use third-party data in the cloud. It provides a secure and reliable way to access and integrate data from various sources, such as data providers, public datasets, or AWS services. Using AWS Data Exchange, you can browse and subscribe to data products that suit your needs, and then use API calls or the AWS Management Console to export the data to Amazon S3, where you can use it with your existing analytics platform. This solution minimizes the effort and time required to incorporate third-party datasets, as you do not need to set up and manage data pipelines, storage, or access controls. You also benefit from the data quality and freshness provided by the data providers, who can update their data products as frequently as needed12.

The other options are not optimal for the following reasons:

B. Use API calls to access and integrate third-party datasets from AWS. This option is vague and does not specify which AWS service or feature is used to access and integrate third-party datasets. AWS offers a variety of services and features that can help with data ingestion, processing, and analysis, but not all of them are suitable for the given scenario. For example, AWS Glue is a serverless data integration service that can help you discover, prepare, and combine data from various sources, but it requires you to create and run data extraction, transformation, and loading (ETL) jobs, which can add operational overhead3.

C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. This option is not feasible, as AWS CodeCommit is a source control service that hosts secure Git-based repositories, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is a service that enables you to capture, process, and analyze data streams in real time, such as clickstream data, application logs, or IoT telemetry. It does not support accessing and integrating data from AWS CodeCommit repositories, which are meant for storing and managing code, not data .

D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). This option is also not feasible, as Amazon ECR is a fully managed container registry service that stores, manages, and deploys container images, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams does not support accessing and integrating data from Amazon ECR, which is meant for storing and managing container images, not data .

1: AWS Data Exchange User Guide

2: AWS Data Exchange FAQs

3: AWS Glue Developer Guide

AWS CodeCommit User Guide

Amazon Kinesis Data Streams Developer Guide

Amazon Elastic Container Registry User Guide

Build a Continuous Delivery Pipeline for Your Container Images with Amazon ECR as Source

Option A is correct because when many AWS Glue jobs start at the same time in the same subnet, Glue needs to create network interfaces in that subnet. If the subnet does not have enough free private IP addresses available, jobs can fail with exactly this type of error. AWS service documentation for VPC-based managed data services consistently notes that if there is no available free IP address in a specified subnet, the service cannot create or add the required ENIs and the workload can fail or degrade.

This is a subnet-level exhaustion issue, not a VPC-wide issue. A VPC can still have free addresses in other subnets while the specific subnet chosen for the jobs is out of usable addresses. That makes D incorrect. Option B is incorrect because G.1X is a valid Glue worker type and does not inherently prevent subnet access. Option C is also incorrect because the error is about address availability, not IAM authorization. In practice, this problem is usually resolved by using a larger subnet or spreading workloads across additional subnets so enough IPs are available when multiple Glue jobs launch concurrently. This aligns with the exam’s focus on networking capacity planning for managed data services.

Option C is correct because the required access pattern is: find all completed rides for a specific driver. In DynamoDB, when you need to query data efficiently by attributes that are not part of the base table primary key, you typically create a global secondary index (GSI). AWS documentation states that a GSI can have a key schema that is different from the base table and can use top-level attributes such as DriverID and RideStatus as its partition and sort keys. That makes it possible to query directly for a given driver and then narrow the results to completed rides, without scanning the full table.

Option A is incorrect because a local secondary index (LSI) must use the same partition key as the base table. Since the table partition key is RideID, an LSI cannot be created with DriverID as the partitioning access path.

Option B uses RiderID, which does not satisfy the requirement to retrieve rides by driver. Option D is also wrong because filter expressions are applied after items are read and therefore do not avoid scanning large amounts of data. The study guide emphasizes choosing the correct data model and access pattern for the workload, which places this question in the Data Storage and Management domain.

 Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB offers two capacity modes for throughput capacity: provisioned and on-demand. In provisioned capacity mode, you specify the number of read and write capacity units per second that you expect your application to require. DynamoDB reserves the resources to meet your throughput needs with consistent performance. In on-demand capacity mode, you pay per request and DynamoDB scales the resources up and down automatically based on the actual workload. On-demand capacity mode is suitable for unpredictable workloads that can vary significantly over time1.

The solution that meets the requirements in the most cost-effective way is to use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times and lower capacity during off-peak times. This solution has the following advantages:

It allows you to optimize the cost and performance of your DynamoDB table by adjusting the provisioned capacity according to your predictable workload patterns. You can use scheduled scaling to specify the date and time for the scaling actions, and the new minimum and maximum capacity limits. For example, you can schedule higher capacity for every Monday morning and lower capacity for weekends2.

It enables you to take advantage of the lower cost per unit of provisioned capacity mode compared to on-demand capacity mode. Provisioned capacity mode charges a flat hourly rate for the capacity you reserve, regardless of how much you use. On-demand capacity mode charges for each read and write request you consume, with no minimum capacity required. For predictable workloads, provisioned capacity mode can be more cost-effective than on-demand capacity mode1.

It ensures that your application performs consistently during peak usage times by having enough capacity to handle the increased load. You can also use auto scaling to automatically adjust the provisioned capacity based on the actual utilization of your table, and set a target utilization percentage for your table or global secondary index. This way, you can avoid under-provisioning or over-provisioning your table2.

Option A is incorrect because it suggests increasing the provisioned capacity to the maximum capacity that is currently present during peak load times. This solution has the following disadvantages:

It wastes money by paying for unused capacity during off-peak times. If you provision the same high capacity for all times, regardless of the actual workload, you are over-provisioning your table and paying for resources that you don’t need1.

It does not account for possible changes in the workload patterns over time. If your peak load times increase or decrease in the future, you may need to manually adjust the provisioned capacity to match the new demand. This adds operational overhead and complexity to your application2.

Option B is incorrect because it suggests dividing the table into two tables and provisioning each table with half of the provisioned capacity of the original table. This solution has the following disadvantages:

It complicates the data model and the application logic by splitting the data into two separate tables. You need to ensure that the queries are evenly distributed across both tables, and that the data is consistent and synchronized between them. This adds extra development and maintenance effort to your application3.

It does not solve the problem of adjusting the provisioned capacity according to the workload patterns. You still need to manually or automatically scale the capacity of each table based on the actual utilization and demand. This may result in under-provisioning or over-provisioning your tables2.

Option D is incorrect because it suggests changing the capacity mode from provisioned to on-demand. This solution has the following disadvantages:

It may incur higher costs than provisioned capacity mode for predictable workloads. On-demand capacity mode charges for each read and write request you consume, with no minimum capacity required. For predictable workloads, provisioned capacity mode can be more cost-effective than on-demand capacity mode, as you can reserve the capacity you need at a lower rate1.

It may not provide consistent performance during peak usage times, as on-demand capacity mode may take some time to scale up the resources to meet the sudden increase in demand. On-demand capacity mode uses adaptive capacity to handle bursts of traffic, but it may not be able to handle very large spikes or sustained high throughput. In such cases, you may experience throttling or increased latency.

1: Choosing the right DynamoDB capacity mode - Amazon DynamoDB

2: Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB

3: Best practices for designing and using partition keys effectively - Amazon DynamoDB

[4]: On-demand mode guidelines - Amazon DynamoDB

[5]: How to optimize Amazon DynamoDB costs - AWS Database Blog

[6]: DynamoDB adaptive capacity: How it works and how it helps - AWS Database Blog

[7]: Amazon DynamoDB pricing - Amazon Web Services (AWS)

Option A is the correct design for single-digit millisecond latency with unpredictable spikes and variable attributes. The study material describes Amazon DynamoDB as a NoSQL database “designed for highly dynamic datasets with frequent read and write operations,” providing low-latency performance at any scale—which directly matches the latency and traffic-spike requirements.

DynamoDB’s key-value and document model fits “product data that has variable attributes” because items can contain different attributes without needing schema migrations typical of relational databases. The requirement to retrieve items by Product ID maps naturally to DynamoDB’s primary key access pattern. The requirement for flexible queries on Category and Brand is met by creating global secondary indexes (GSIs) on those attributes so queries can be served efficiently without scanning the whole table.

Option B (Aurora) can scale reads, but it is not typically the best fit for sustained single-digit millisecond performance during sudden spikes without careful capacity planning. Option C is optimized for search and text/query relevance rather than primary-key transactional access patterns. Option D uses Athena (interactive SQL over S3) which is not designed for millisecond-latency, high-QPS query workloads.

To enable the Lambda function to connect to the RDS DB instance privately without using the public internet, the best combination of steps is to configure the Lambda function to run in the same subnet that the DB instance uses, and attach the same security group to the Lambda function and the DB instance. This way, the Lambda function and the DB instance can communicate within the same private network, and the security group can allow traffic between them on the database port. This solution has the least operational overhead, as it does not require any changes to the public access setting, the network ACL, or the security group of the DB instance.

The other options are not optimal for the following reasons:

A. Turn on the public access setting for the DB instance. This option is not recommended, as it would expose the DB instance to the public internet, which can compromise the security and privacy of the data. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.

B. Update the security group of the DB instance to allow only Lambda function invocations on the database port. This option is not sufficient, as it would only modify the inbound rules of the security group of the DB instance, but not the outbound rules of the security group of the Lambda function. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.

E. Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port. This option is not necessary, as the network ACL of the private subnet already allows all traffic within the subnet by default. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.

1: Connecting to an Amazon RDS DB instance

2: Configuring a Lambda function to access resources in a VPC

3: Working with security groups

Network ACLs

For near real-time processing of new files in S3, event-driven ingestion is optimal. S3 Event Notifications can trigger AWS Lambda to immediately load data into Amazon Redshift, eliminating latency associated with batch scheduling.

“Event-based triggers using S3 notifications and Lambda functions are effective for near real-time ingestion pipelines into Amazon Redshift.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Option C (Glue bookmarks) is best for batch jobs, and zero-ETL applies to Aurora to Redshift, not S3.