Answer: C

Explanation: Amazon Athena is a serverless interactive query service that allows you to

analyze data in Amazon S3 using standard SQL. Athena supports various data formats,

such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equally

efficient for querying. Some data formats, such as CSV and JSON, are row-oriented,

meaning that they store data as a sequence of records, each with the same fields. Roworiented

formats are suitable for loading and exporting data, but they are not optimal for

analytical queries that often access only a subset of columns. Row-oriented formats also

do not support compression or encoding techniques that can reduce the data size and

improve the query performance.

On the other hand, some data formats, such as ORC and Parquet, are column-oriented,

meaning that they store data as a collection of columns, each with a specific data type.

Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join

data by columns. Column-oriented formats also support compression and encoding

techniques that can reduce the data size and improve the query performance. For

example, Parquet supports dictionary encoding, which replaces repeated values with

numeric codes, and run-length encoding, which replaces consecutive identical values with

a single value and a count. Parquet also supports various compression algorithms, such as

Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query

performance. Therefore, changing the data format from CSV to Parquet and applying Snappy

compression will most speed up the Athena query performance. Parquet is a columnoriented

format that allows Athena to scan only the relevant columns and skip the rest,

reducing the amount of data read from S3. Snappy is a compression algorithm that reduces

the data size without compromising the query speed, as it is splittable and does not require

decompression before reading. This solution will also reduce the cost of Athena queries, as

Athena charges based on the amount of data scanned from S3.

The other options are not as effective as changing the data format to Parquet and applying

Snappy compression. Changing the data format from CSV to JSON and applying Snappy

compression will not improve the query performance significantly, as JSON is also a roworiented

format that does not support columnar access or encoding techniques.

Compressing the CSV files by using Snappy compression will reduce the data size, but it

will not improve the query performance significantly, as CSV is still a row-oriented format

that does not support columnar access or encoding techniques. Compressing the CSV files

by using gzjg compression will reduce the data size, but it willdegrade the query

performance, as gzjg is not a splittable compression algorithm and requires decompression

before reading. References:

Amazon Athena

Choosing the Right Data Format

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide,

Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena

Answer: A

Explanation:

Amazon Athena is a serverless, interactive query service that enables you to analyze data

in Amazon S3 using standard SQL. AWS Lake Formation is a service that helps you build,

secure, and manage data lakes on AWS. You can use AWS Lake Formation to create data

filters that define the level of access for different IAM roles based on the columns, rows, or

tags of the data. By using Amazon Athena to query the data and AWS Lake Formation to

create data filters, the company can meet the requirements of ensuring that user groups

can access only the PII that they require with the least effort. The solution is to use Amazon

Athena to query the data in the data lake that is in Amazon S3. Then, set up AWS Lake

Formation and create data filters to establish levels of access for the company’s IAM roles.

For example, a data filter can allow a user group to access only the columns that contain

the PII that they need, such as name and email address, and deny access to the columns

that contain the PII that they do not need, such as phone number and social security

number. Finally, assign each user to the IAM role that matches the user’s PII access

requirements. This way, the user groups can access the data in the data lake securely and

efficiently. The other options are either not feasible or not optimal. Using Amazon

QuickSight to access the data (option B) would require the company to pay for the

QuickSight service and to configure the column-level security features for each user.

Building a custom query builder UI that will run Athena queries in the background to access

the data (option C) would require the company to develop and maintain the UI and to

integrate it with Amazon Cognito. Creating IAM roles that have different levels of granular

access (option D) would require the company to manage multiple IAM roles and policies and to ensure that they are aligned with the data schema. References:

Amazon Athena

AWS Lake Formation

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide,

Chapter 4: Data Analysis and Visualization, Section 4.3: Amazon Athena

Answer: C

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 canprotect 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

Answer: C

Explanation: The AWS Glue Data Catalog is an Apache Hive metastore-compatible

catalog that provides a central metadata repository for various data sources and formats.

You can use the AWS Glue Data Catalog as an external Hive metastore for Amazon EMR

and Amazon Athena queries, and import metadata from existing Hive metastores into the Data Catalog. This solution requires the least development effort, as you can use AWS

Glue crawlers to automatically discover and catalog the metadata from Hive, and use the

AWS Glue console, AWS CLI, or Amazon EMR API to configure the Data Catalog as the

Hive metastore. The other options are either more complex or require additional steps,

such as setting up Apache Ranger for security, managing a Hive metastore on an EMR

cluster or an RDS instance, or migrating the metadata manually. References:

Using the AWS Glue Data Catalog as the metastore for Hive (Section: Specifying

AWS Glue Data Catalog as the metastore)

Metadata Management: Hive Metastore vs AWS Glue (Section: AWS Glue Data

Catalog)

AWS Glue Data Catalog support for Spark SQL jobs (Section: Importing metadata

from an existing Hive metastore)

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

(Chapter 5, page 131)

Answer: B,D

Explanation: The best combination of resources to meet the requirements of high

reliability, cost-optimization, and performance for running Apache Spark jobs on Amazon

EMR is to use Amazon S3 as a persistent data store and Graviton instances for core nodes

and task nodes.

Amazon S3 is a highly durable, scalable, and secure object storage service that can store

any amount of data for a variety of use cases, including big data analytics1. Amazon S3 is

a better choice than HDFS as a persistent data store for Amazon EMR, as it decouples the

storage from the compute layer, allowing for more flexibility and cost-efficiency. Amazon S3

also supports data encryption, versioning, lifecycle management, and cross-region

replication1. Amazon EMR integrates seamlessly with Amazon S3, using EMR File System

(EMRFS) to access data stored in Amazon S3 buckets2. EMRFS also supports consistent

view, which enables Amazon EMR to provide read-after-write consistency for Amazon S3

objects that are accessed through EMRFS2.

Graviton instances are powered by Arm-based AWS Graviton2 processors that deliver up

to 40% better price performance over comparable current generation x86-based

instances3. Graviton instances are ideal for running workloads that are CPU-bound,

memory-bound, or network-bound, such as big data analytics, web servers, and opensource

databases3. Graviton instances are compatible with Amazon EMR, and can beused

for both core nodes and task nodes. Core nodes are responsible for running the data processing frameworks, such as Apache Spark, and storing data in HDFS or the local file

system. Task nodes are optional nodes that can be added to a cluster to increase the

processing power and throughput. By using Graviton instances for both core nodes and

task nodes, you can achieve higher performance and lower cost than using x86-based

instances.

Using Spot Instances for all primary nodes is not a good option, as it can compromise the

reliability and availability of the cluster. Spot Instances are spare EC2 instances that are

available at up to 90% discount compared to On-Demand prices, but they can be

interrupted by EC2 with a two-minute notice when EC2 needs the capacity back. Primary

nodes are the nodes that run the cluster software, such as Hadoop, Spark, Hive, and Hue,

and are essential for the cluster operation. If a primary node is interrupted by EC2, the

cluster will fail or become unstable. Therefore, it is recommended to use On-Demand

Instances or Reserved Instances for primary nodes, and use Spot Instances only for task

nodes that can tolerate interruptions. References:

Amazon S3 - Cloud Object Storage

EMR File System (EMRFS)

AWS Graviton2 Processor-Powered Amazon EC2 Instances

[Plan and Configure EC2 Instances]

[Amazon EC2 Spot Instances]

[Best Practices for Amazon EMR]

Answer: C

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. References:

Amazon Redshift streaming ingestion

Creating an external schema for Amazon Kinesis Data Streams

Creating a materialized view for Amazon Kinesis Data Streams

Answer: D

Explanation: This solution meets the requirement of logging all writes to the S3 bucket

into another S3 bucket with the least operational effort. AWS CloudTrail is a service that

records the API calls made to AWS services, including Amazon S3. By creating a trail of

data events, you can capture the details of the requests that are made to the transactions

S3 bucket, such as the requester, the time, the IP address, and the response elements. By

specifying an empty prefix and write-only events, you can filter the data events to only

include the ones that write to the bucket. By specifying the logs S3 bucket as the

destination bucket, you can store the CloudTrail logs in another S3 bucket that is in the

same AWS Region. This solution does not require any additional coding or configuration,

and it is more scalable and reliable than using S3 Event Notifications and Lambda

functions. References:

Logging Amazon S3 API calls using AWS CloudTrail

Creating a trail for data events

Enabling Amazon S3 server access logging

Answer: B

Explanation: Option B is the best solution to meet the requirements with the least

operational overhead because S3 Select is a feature that allows you to retrieve only a

subset of data from an S3 object by using simple SQL expressions. S3 Select works on

objects stored in CSV, JSON, or Parquet format. By using S3 Select, you can avoid the

need to download and process the entire S3 object, which reduces the amount of data

transferred and the computation time. S3 Select is also easy to use and does not require

any additional services or resources.

Option A is not a good solution because it involves writing custom code and configuring an

AWS Lambda function to load data from the S3 bucket into a pandas dataframe and query

the required column. This option adds complexity and latency to the data retrieval process

and requires additional resources and configuration.Moreover, AWS Lambda has

limitations on the execution time, memory, and concurrency, which may affect the

performance and reliability of the data retrieval process.

Option C is not a good solution because it involves creating and running an AWS Glue

DataBrew project to consume the S3 objects and query the required column. AWS Glue

DataBrew is a visual data preparation tool that allows you to clean, normalize, and

transform data without writing code. However, in this scenario, the data is already in

Parquet format, which is a columnar storage format that is optimized for analytics.

Therefore, there is no need to use AWS Glue DataBrew to prepare the data. Moreover,

AWS Glue DataBrew adds extra time and cost to the data retrieval process and requires

additional resources and configuration.

Option D is not a good solution because it involves running an AWS Glue crawler on the S3

objects and using a SQL SELECT statement in Amazon Athena to query the required

column. An AWS Glue crawler is a service that can scan data sources and create metadata

tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores

information about the data sources, such as schema, format, and location. Amazon Athena

is a serverless interactive query service that allows you to analyze data in S3 using

standard SQL. However, in this scenario, the schema and format of the data are already

known and fixed, so there is no need to run a crawler to discover them. Moreover, running

a crawler and using Amazon Athena adds extra time and cost to the data retrieval process

and requires additional services and configuration.

References:

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

S3 Select and Glacier Select - Amazon Simple Storage Service

AWS Lambda - FAQs

What Is AWS Glue DataBrew? - AWS Glue DataBrew

Populating the AWS Glue Data Catalog - AWS Glue What is Amazon Athena? - Amazon Athena

Answer: B

Explanation: AWS Lake Formation is a service that allows you to easily set up, secure,

and manage data lakes. One of the features of Lake Formation is row-level security, which

enables you to control access to specific rows or columns of data based on the identity or

role of the user. This feature is useful for scenarios where you need to restrict access to

sensitive or regulated data, such as customer data from different countries. By registering

the S3 bucket as a data lake location in Lake Formation, you can use the Lake Formation

console or APIs to define and apply row-level security policies to the data in the bucket.

You can also use Lake Formation blueprints to automate the ingestion and transformation

of data from various sources into the data lake. This solution requires the least operational

effort compared to the other options, as it does not involve creating or moving data, or

managing multiple tables, views, or roles. References:

AWS Lake Formation

Row-Level Security

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide,

Chapter 4: Data Lakes and Data Warehouses, Section 4.2: AWS Lake Formation

Answer: C,D

Explanation: 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.

References:

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

Answer: B,D

Explanation: AWS Lake Formation is a service that helps you build, secure, and manage

data lakes on Amazon S3. You can use AWS Lake Formation to register the S3 path as a

data lake location, and enable fine-grained access control to limit access to the records

based on the HR department’s Region. You can use data filters to specify which S3

prefixes or partitions each HR department can access, and grant permissions to the IAM

roles of the HR departments accordingly. This solution will meet the requirement with the

least operational overhead, as it simplifies the data lake management and security, and

leverages the existing IAM roles of the HR departments12.

The other options are not optimal for the following reasons:

A. Use data filters for each Region to register the S3 paths as data locations. This

option is not possible, as data filters are not used to register S3 paths as data

locations, but to grant permissions to access specific S3 prefixes or partitions

within a data location. Moreover, this option does not specify how to limit access to

the records based on the HR department’s Region.

C. Modify the IAM roles of the HR departments to add a data filter for each

department’s Region. This option is not possible, as data filters are not added to

IAM roles, but to permissions granted by AWS Lake Formation. Moreover, this

option does not specify how to register the S3 path as a data lake location, or how

to enable fine-grained access control in AWS Lake Formation.

E. Create a separate S3 bucket for each Region. Configure an IAM policy to allow

S3 access. Restrict access based on Region. This option is not recommended, as

it would require more operational overhead to create and manage multiple S3

buckets, and to configure and maintain IAM policies for each HR department.

Moreover, this option does not leverage the benefits of AWS Lake Formation, such

as data cataloging, data transformation, and data governance.

References:

1: AWS Lake Formation

2: AWS Lake Formation Permissions

: AWS Identity and Access Management

: Amazon S3

Answer: B

Explanation: The streaming ingestion feature of Amazon Redshift enables you to ingest

data from streaming sources, such as Amazon Kinesis Data Streams, into Amazon

Redshift tables in near real-time. You can use the streaming ingestion feature to process

the streaming data from the wearable devices, hospital equipment, and patient records.

The streaming ingestion feature also supports incremental updates, which means you can

append new data or update existing data in the Amazon Redshift tables. This way, you can

store the data in an Amazon Redshift Serverless warehouse and support near real-time

analytics of the streaming data and the previous day’s data. This solution meets the

requirements with the least operational overhead, as it does not require any additional

services or components to ingest and process the streaming data. The other options are

either not feasible or not optimal. Loading data into Amazon Kinesis Data Firehose and

then into Amazon Redshift (option A) would introduce additional latency and cost, as well

as require additional configuration and management. Loading data into Amazon S3 and

then using the COPY command to load the data into Amazon Redshift (option C) would

also introduce additional latency and cost, as well as require additional storage space and

ETL logic. Using the Amazon Aurora zero-ETL integration with Amazon Redshift (option D)

would not work, as it requires the data to be stored in Amazon Aurora first, which is not the

case for the streaming data from the healthcare company. References:

Using streaming ingestion with Amazon Redshift

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide,

Chapter 3: Data Ingestion and Transformation, Section 3.5: Amazon Redshift

Streaming Ingestion

Answer: B

Explanation: AWS Glue is a fully managed serverless ETL service that can handle data

deduplication with minimal operational overhead. AWS Glue provides a built-in ML

transform called FindMatches, which can automatically identify and group similar records in

a dataset. FindMatches can also generate a primary key for each group of records and

remove duplicates. FindMatches does not require any coding or prior ML experience, as it

can learn from a sample of labeled data provided by the user. FindMatches can also scale

to handle large datasets and optimize the cost and performance of the ETL job.

References:

AWS Glue

FindMatches ML Transform

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Answer: D

Explanation: Option D is the best solution to meet the requirements with the least

operational overhead because AWS Lake Formation is a fully managed service that

simplifies the process of building, securing, and managing data lakes. AWS Lake Formation allows you to define granular data access policies at the row and column level

for different users and groups. AWS Lake Formation also integrates with Amazon Athena,

Amazon Redshift Spectrum, and Apache Hive on Amazon EMR, enabling these services to

access the data in the data lake through AWS Lake Formation.

Option A is not a good solution because S3 access policies cannot restrict data access by

rows and columns. S3 access policies are based on the identity and permissions of the

requester, the bucket and object ownership, and the object prefix and tags. S3 access

policies cannot enforce fine-grained data access control at the row and column level.

Option B is not a good solution because it involves using Apache Ranger and Apache Pig,

which are not fully managed services and require additional configuration and

maintenance. Apache Ranger is a framework that provides centralized security

administration for data stored in Hadoop clusters, such as Amazon EMR. Apache Ranger

can enforce row-level and column-level access policies for Apache Hive tables. However,

Apache Ranger is not a native AWS service and requires manual installation and

configuration on Amazon EMR clusters. Apache Pig is a platform that allows you to analyze

large data sets using a high-level scripting language called Pig Latin. Apache Pig can

access data stored in Amazon S3 and process it using Apache Hive. However,Apache Pig

is not a native AWS service and requires manual installation and configuration on Amazon

EMR clusters.

Option C is not a good solution because Amazon Redshift is not a suitable service for data

lake storage. Amazon Redshift is a fully managed data warehouse service that allows you

to run complex analytical queries using standard SQL. Amazon Redshift can enforce rowlevel

and column-level access policies for different users and groups. However, Amazon

Redshift is not designed to store and process large volumes of unstructured or semistructured

data, which are typical characteristics of data lakes. Amazon Redshift is also

more expensive and less scalable than Amazon S3 for data lake storage.

References:

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

What Is AWS Lake Formation? - AWS Lake Formation

Using AWS Lake Formation with Amazon Athena - AWS Lake Formation

Using AWS Lake Formation with Amazon Redshift Spectrum - AWS Lake

Formation

Using AWS Lake Formation with Apache Hive on Amazon EMR - AWS Lake

Formation

Using Bucket Policies and User Policies - Amazon Simple Storage Service

Apache Ranger

Apache Pig

What Is Amazon Redshift? - Amazon Redshift

Answer: B

Explanation: Changing the distribution key to the table column that has the largest

dimension will help to balance the load more evenly across all five compute nodes. The

distribution key determines how the rows of a table are distributed among the slices of the

cluster. If the distribution key is not chosen wisely, it can cause data skew, meaning some

slices will have more data than others, resulting in uneven CPU load and query

performance. By choosing the table column that has the largest dimension, meaning the

column that has the most distinct values, as the distribution key, the data engineer can

ensure that the rows are distributed more uniformly across the slices, reducing data skew

and improving query performance.

The other options are not solutions that will meet the requirements. Option A, changing the

sort key to be the data column that is most often used in a WHERE clause of the SQL

SELECT statement, will not affect the data distribution or the CPU load. The sort key

determines the order in which the rows of a table are stored on disk, which can improve the

performance of range-restricted queries, but not the load balancing. Option C, upgrading

the reserved node from ra3.4xlarge to ra3.16xlarge, will not maintain the current number of

compute nodes, as it will increase the cost and the capacity of the cluster. Option D,

changing the primary key to be the data column that is most often used in a WHERE

clause of the SQL SELECT statement, will not affect the data distribution or the CPU load

either. The primary key is a constraint that enforces the uniqueness of the rows in a table,

but it does not influence the data layout or the query optimization. References:

Choosing a data distribution style

Choosing a data sort key

Working with primary keys

Answer: C

Explanation: Amazon EC2 instances can use two types of storage volumes: instance

store volumes and Amazon EBS volumes. Instance store volumes are ephemeral, meaning

they are only attached to the instance for the duration of its life cycle. If the instance is

stopped, terminated, or fails, the data on the instance store volume is lost. Amazon EBS

volumes are persistent, meaning they can be detached from the instance and attached to

another instance, and the data on the volume is preserved. To meet the requirement of

persisting the data even if the EC2 instances are terminated, the data engineer must use

Amazon EBS volumes to store the application data. The solution is to launch new EC2

instances by using an AMI that is backed by an EC2 instance store volume, which is the

default option for most AMIs. Then, the data engineer must attach an Amazon EBS volume

to each instance and configure the application to write the data to the EBS volume. This

way, the data will be saved on the EBS volume and can be accessed by another instance if

needed. The data engineer can apply the default settings to the EC2 instances, as there is

no need to modify the instance type, security group, or IAM role for this solution. The other

options are either not feasible or not optimal. Launching new EC2 instances by using an

AMI that is backed by an EC2 instance store volume that contains the application data

(option A) or by using an AMI that is backed by a root Amazon EBS volume that contains

the application data (option B) would not work, as the data on the AMI would be outdated

and overwritten by the new instances. Attaching an additional EC2 instance store volume

to contain the application data (option D)would not work, as the data on the instance store

volume would be lost if the instance is terminated. References:

Amazon EC2 Instance Store

Amazon EBS Volumes

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide,

Chapter 2: Data Store Management, Section 2.1: Amazon EC2

Answer: D

Explanation: Amazon Athena is a serverless interactive query service that allows you to

analyze data in Amazon S3 using standard SQL. Athena supports various data formats,

such as CSV,JSON, ORC, Avro, and Parquet. However, not all data formats are equally