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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q78-Q83):

NEW QUESTION # 78
A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule. Every Monday, there is an immediate increase in activity early in the morning. The application has very low usage during weekends.
The company must ensure that the application performs consistently during peak usage times.
Which solution will meet these requirements in the MOST cost-effective way?

• A. Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times. Schedule lower capacity during off-peak times.
• B. Increase the provisioned capacity to the maximum capacity that is currently present during peak load times.
• C. Change the capacity mode from provisioned to on-demand. Configure the table to scale up and scale down based on the load on the table.
• D. Divide the table into two tables. Provision each table with half of the provisioned capacity of the original table. Spread queries evenly across both tables.

Answer: A

Explanation:
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.
Reference:
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)

 

NEW QUESTION # 79
A company receives call logs as Amazon S3 objects that contain sensitive customer information. The company must protect the S3 objects by using encryption. The company must also use encryption keys that only specific employees can access.
Which solution will meet these requirements with the LEAST effort?

• A. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the KMS keys that encrypt the objects.
• B. Use server-side encryption with customer-provided keys (SSE-C) to encrypt the objects that contain customer information. Restrict access to the keys that encrypt the objects.
• C. Use an AWS CloudHSM cluster to store the encryption keys. Configure the process that writes to Amazon S3 to make calls to CloudHSM to encrypt and decrypt the objects. Deploy an IAM policy that restricts access to the CloudHSM cluster.
• D. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the Amazon S3 managed keys that encrypt the objects.

Answer: A

Explanation:
Option C is the best solution to meet the requirements with the least effort because server-side encryption with AWS KMS keys (SSE-KMS) is a feature that allows you to encrypt data at rest in Amazon S3 using keys managed by AWS Key Management Service (AWS KMS). AWS KMS is a fully managed service that enables you to create and manage encryption keys for your AWS services and applications. AWS KMS also allows you to define granular access policies for your keys, such as who can use them to encrypt and decrypt data, and under what conditions. By using SSE-KMS, you can protect your S3 objects by using encryption keys that only specific employees can access, without having to manage the encryption and decryption process yourself.
Option A is not a good solution because it involves using AWS CloudHSM, which is a service that provides hardware security modules (HSMs) in the AWS Cloud. AWS CloudHSM allows you to generate and use your own encryption keys on dedicated hardware that is compliant with various standards and regulations.
However, AWS CloudHSM is not a fully managed service and requires more effort to set up and maintain than AWS KMS. Moreover, AWS CloudHSM does not integrate with Amazon S3, so you have to configure the process that writes to S3 to make calls to CloudHSM to encrypt and decrypt the objects, which adds complexity and latency to the data protection process.
Option B is not a good solution because it involves using server-side encryption with customer-provided keys (SSE-C), which is a feature that allows you to encrypt data at rest in Amazon S3 using keys that you provide and manage yourself. SSE-C requires you to send your encryption key along with each request to upload or retrieve an object. However, SSE-C does not provide any mechanism to restrict access to the keys that encrypt the objects, so you have to implement your own key management and access control system, which adds more effort and risk to the data protection process.
Option D is not a good solution because it involves using server-side encryption with Amazon S3 managed keys (SSE-S3), which is a feature that allows you to encrypt data at rest in Amazon S3 using keys that are managed by Amazon S3. SSE-S3 automatically encrypts and decrypts your objects as they are uploaded and downloaded from S3. However, SSE-S3 does not allow you to control who can access the encryption keys or under what conditions. SSE-S3 uses a single encryption key for each S3 bucket, which is shared by all users who have access to the bucket. This means that you cannot restrict access to the keys that encrypt the objects by specific employees, which does not meet the requirements.
References:
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
* Protecting Data Using Server-Side Encryption with AWS KMS-Managed Encryption Keys (SSE- KMS) - Amazon Simple Storage Service
* What is AWS Key Management Service? - AWS Key Management Service
* What is AWS CloudHSM? - AWS CloudHSM
* Protecting Data Using Server-Side Encryption with Customer-Provided Encryption Keys (SSE-C) - Amazon Simple Storage Service
* Protecting Data Using Server-Side Encryption with Amazon S3-Managed Encryption Keys (SSE-S3) - Amazon Simple Storage Service

 

NEW QUESTION # 80
A data engineer is using an AWS Glue ETL job to remove outdated customer records from a table that contains customer account information. The data engineer is using the following SQL command:
MERGE INTO accounts t USING monthly_accounts_update s
ON t.customer = s.customer
WHEN MATCHED THEN DELETE
What will happen when the data engineer runs the SQL command?

• A. Only customer records that are present in both tables will be retained in the customer accounts table.
• B. All customer records that exist in both the customer accounts table and the monthly_accounts_update table will be deleted from the accounts table.
• C. The monthly_accounts_update table will be deleted.
• D. No records will be deleted because the command syntax is not valid in AWS Glue.

Answer: B

Explanation:
In AWS Glue's SQL implementation (Spark SQL-compatible), the MERGE INTO statement supports conditional actions.
The clause WHEN MATCHED THEN DELETE deletes matching records from the target table (accounts) where the join condition is true.
"A MERGE INTO statement can perform updates, inserts, or deletes based on the match condition between source and target tables."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

 

NEW QUESTION # 81
A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.
Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.
Which factors could have caused the reordering system to receive duplicated data? (Select TWO.)

• A. The producer experienced network-related timeouts.
• B. The stream's value for the IteratorAgeMilliseconds metric was too high.
• C. The max_records configuration property was set to a number that was too high.
• D. There was a change in the number of shards, record processors, or both.
• E. The AggregationEnabled configuration property was set to true.

Answer: A,D

Explanation:
Problem Analysis:
The company uses Kinesis Data Streams for both inventory management and reordering.
The Kinesis Producer Library (KPL) publishes data, and the Kinesis Client Library (KCL) consumes data.
Duplicate records were observed in the inventory reordering system.
Key Considerations:
Kinesis streams are designed for durability but may produce duplicates under certain conditions.
Factors such as network timeouts, shard splits, or changes in record processors can cause duplication.
Solution Analysis:
Option A: Network-Related Timeouts
If the producer (KPL) experiences network timeouts, it retries data submission, potentially causing duplicates.
Option B: High IteratorAgeMilliseconds
High iterator age suggests delays in processing but does not directly cause duplication.
Option C: Changes in Shards or Processors
Changes in the number of shards or record processors can lead to re-processing of records, causing duplication.
Option D: AggregationEnabled Set to True
AggregationEnabled controls the aggregation of multiple records into one, but it does not cause duplication.
Option E: High max_records Value
A high max_records value increases batch size but does not lead to duplication.
Final Recommendation:
Network-related timeouts and changes in shards or processors are the most likely causes of duplicate data in this scenario.
Reference:
Amazon Kinesis Data Streams Best Practices
Kinesis Producer Library (KPL) Overview
Kinesis Client Library (KCL) Overview

 

NEW QUESTION # 82
A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3.
The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata.
Which solution will meet these requirements with the LEAST operational overhead?

• A. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.
• B. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.
• C. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.
• D. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog.

Answer: A

Explanation:
This solution will meet the requirements with the least operational overhead because it uses the AWS Glue Data Catalog as the central metadata repository for data sources that run in the AWS Cloud. The AWS Glue Data Catalog is a fully managed service that provides a unified view of your data assets across AWS and on- premises data sources. It stores the metadata of your data in tables, partitions, and columns, and enables you to access and query your data using various AWS services, such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. You can use AWS Glue crawlers to connect to multiple data stores, such as Amazon RDS, Amazon Redshift, and Amazon S3, and to update the Data Catalog with metadata changes.
AWS Glue crawlers can automatically discover the schema and partition structure of your data, and create or update the corresponding tables in the Data Catalog. You can schedule the crawlers to run periodically to update the metadata catalog, and configure them to detect changes to the source metadata, such as new columns, tables, or partitions12.
The other options are not optimal for the following reasons:
* A. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically. This option is not recommended, as it would require more operational overhead to create and manage an Amazon Aurora database as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.
* C. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically. This option is also not recommended, as it would require more operational overhead to create and manage an Amazon DynamoDB table as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.
* D. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog. This option is not optimal, as it would require more manual effort to extract the schema for Amazon RDS and Amazon Redshift sources, and to build the Data Catalog. This option would not take advantage of the AWS Glue crawlers' ability to automatically discover the schema and partition structure of your data from various data sources, and to create or update the corresponding tables in the Data Catalog.
References:
* 1: AWS Glue Data Catalog
* 2: AWS Glue Crawlers
* : Amazon Aurora
* : AWS Lambda
* : Amazon DynamoDB

 

NEW QUESTION # 83
......

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