Amazon Data-Engineer-Associate Exam Dumps

AWS Certified Data Engineer - Associate (DEA-C01)

80 Questions & Answers with Explanation

Update Date : July 15, 2024

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Amazon Data-Engineer-Associate Sample Questions

Question # 1

A data engineer needs Amazon Athena queries to finish faster. The data engineer noticesthat all the files the Athena queries use are currently stored in uncompressed .csv format.The data engineer also notices that users perform most queries by selecting a specificcolumn.Which solution will MOST speed up the Athena query performance?

A. Change the data format from .csvto JSON format. Apply Snappy compression.
B. Compress the .csv files by using Snappy compression.
C. Change the data format from .csvto Apache Parquet. Apply Snappy compression.
D. Compress the .csv files by using gzjg compression.

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

Question # 2

A company stores data in a data lake that is in Amazon S3. Some data that the company stores in the data lake contains personally identifiable information (PII). Multiple usergroups need to access the raw data. The company must ensure that user groups canaccess only the PII that they require.Which solution will meet these requirements with the LEAST effort?

A. Use Amazon Athena to query the data. Set up AWS Lake Formation and create datafilters to establish levels of access for the company's IAM roles. Assign each user to theIAM role that matches the user's PII access requirements.
B. Use Amazon QuickSight to access the data. Use column-level security features inQuickSight to limit the PII that users can retrieve from Amazon S3 by using AmazonAthena. Define QuickSight access levels based on the PII access requirements of theusers.
C. Build a custom query builder UI that will run Athena queries in the background to accessthe data. Create user groups in Amazon Cognito. Assign access levels to the user groupsbased on the PII access requirements of the users.
D. Create IAM roles that have different levels of granular access. Assign the IAM roles toIAM user groups. Use an identity-based policy to assign access levels to user groups at thecolumn level.

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

Question # 3

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

A. Use an AWS CloudHSM cluster to store the encryption keys. Configure the process thatwrites 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.
B. Use server-side encryption with customer-provided keys (SSE-C) to encrypt the objectsthat contain customer information. Restrict access to the keys that encrypt the objects.
C. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects thatcontain customer information. Configure an IAM policy that restricts access to the KMSkeys that encrypt the objects.
D. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt theobjects that contain customer information. Configure an IAM policy that restricts access tothe Amazon S3 managed keys that encrypt the objects.

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

Question # 4

A data engineer needs to maintain a central metadata repository that users access throughAmazon EMR and Amazon Athena queries. The repository needs to provide the schemaand properties of many tables. Some of the metadata is stored in Apache Hive. The dataengineer needs to import the metadata from Hive into the central metadata repository.Which solution will meet these requirements with the LEAST development effort?

A. Use Amazon EMR and Apache Ranger.
B. Use a Hive metastore on an EMR cluster.
C. Use the AWS Glue Data Catalog.
D. Use a metastore on an Amazon RDS for MySQL DB instance.

Question # 5

A company is planning to use a provisioned Amazon EMR cluster that runs Apache Sparkjobs to perform big data analysis. The company requires high reliability. A big data teammust follow best practices for running cost-optimized and long-running workloads onAmazon EMR. The team must find a solution that will maintain the company's current levelof performance.Which combination of resources will meet these requirements MOST cost-effectively?(Choose two.)

A. Use Hadoop Distributed File System (HDFS) as a persistent data store.
B. Use Amazon S3 as a persistent data store.
C. Use x86-based instances for core nodes and task nodes.
D. Use Graviton instances for core nodes and task nodes.
E. Use Spot Instances for all primary nodes.

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]

Question # 6

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

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

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