Question # 1
A company uses an Amazon Redshift provisioned cluster as its database. The Redshift
cluster has five reserved ra3.4xlarge nodes and uses key distribution.
A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL
Queries that run on the node are queued. The other four nodes usually have a CPU load
under 15% during daily operations.
The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes.
Which solution will meet these requirements? | | A. Change the sort key to be the data column that is most often used in a WHERE clause
of the SQL SELECT statement. | | B. Change the distribution key to the table column that has the largest dimension. | | C. Upgrade the reserved node from ra3.4xlarqe to ra3.16xlarqe. | | D. Change the primary key to be the data column that is most often used in a WHERE
clause of the SQL SELECT statement. |
B. Change the distribution key to the table column that has the largest dimension.
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
Question # 2
A manufacturing company wants to collect data from sensors. A data engineer needs to
implement a solution that ingests sensor data in near real time.
The solution must store the data to a persistent data store. The solution must store the data
in nested JSON format. The company must have the ability to query from the data store
with a latency of less than 10 milliseconds.
Which solution will meet these requirements with the LEAST operational overhead? | | A. Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in
Amazon S3 for querying. | | B. Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for
querying. | | C. Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in
Amazon DynamoDB for querying. | | D. Use Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data. Use
AWS Glue to store the data in Amazon RDS for querying. |
C. Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in
Amazon DynamoDB for querying.
Explanation: Amazon Kinesis Data Streams is a service that enables you to collect,
process, and analyze streaming data in real time. You can use Kinesis Data Streams to
capture sensor data from various sources, such as IoT devices, web applications, or mobile
apps. You can create data streams that can scale up to handle any amount of data from
thousands of producers. You can also use the Kinesis Client Library (KCL) or the Kinesis
Data Streams API to write applications that process and analyze the data in the streams1.
Amazon DynamoDB is a fully managed NoSQL database service that provides fast and
predictable performance with seamless scalability. You can use DynamoDB to store the
sensor data in nested JSON format, as DynamoDB supports document data types, such as
lists and maps. You can also use DynamoDB to query the data with a latency of less than
10 milliseconds, as DynamoDB offers single-digit millisecond performance for any scale of
data. You can use the DynamoDB API or the AWS SDKs to perform queries on the data,
such as using key-value lookups, scans, or queries2.
The solution that meets the requirements with the least operational overhead is to use
Amazon Kinesis Data Streams to capture the sensor data and store the data in Amazon
DynamoDB for querying. This solution has the following advantages:
It does not require you to provision, manage, or scale any servers, clusters, or
queues, as Kinesis Data Streams and DynamoDB are fully managed services that
handle all the infrastructure for you. This reduces the operational complexity and
cost of running your solution.
It allows you to ingest sensor data in near real time, as Kinesis Data Streams can
capture data records as they are produced and deliver them to your applications
within seconds. You can also use Kinesis Data Firehose to load the data from the
streams to DynamoDB automatically and continuously3.
It allows you to store the data in nested JSON format, as DynamoDB supports
document data types, such as lists and maps. You can also use DynamoDB Streams to capture changes in the data and trigger actions, such as sending
notifications or updating other databases.
It allows you to query the data with a latency of less than 10 milliseconds, as
DynamoDB offers single-digit millisecond performance for any scale of data. You
can also use DynamoDB Accelerator (DAX) to improve the read performance by
caching frequently accessed data.
Option A is incorrect because it suggests using a self-hosted Apache Kafka cluster to
capture the sensor data and store the data in Amazon S3 for querying. This solution has
the following disadvantages:
It requires you to provision, manage, and scale your own Kafka cluster, either on
EC2 instances or on-premises servers. This increases the operational complexity
and cost of running your solution.
It does not allow you to query the data with a latency of less than 10 milliseconds,
as Amazon S3 is an object storage service that is not optimized for low-latency
queries. You need to use another service, such as Amazon Athena or Amazon
Redshift Spectrum, to query the data in S3, which may incur additional costs and
latency.
Option B is incorrect because it suggests using AWS Lambda to process the sensor data
and store the data in Amazon S3 for querying. This solution has the following
disadvantages:
It does not allow you to ingest sensor data in near real time, as Lambda is a
serverless compute service that runs code in response to events. You need to use
another service, such as API Gateway or Kinesis Data Streams, to trigger Lambda
functions with sensor data, which may add extra latency and complexity to your
solution.
It does not allow you to query the data with a latency of less than 10 milliseconds,
as Amazon S3 is an object storage service that is not optimized for low-latency
queries. You need to use another service, such as Amazon Athena or Amazon
Redshift Spectrum, to query the data in S3, which may incur additional costs and
latency.
Option D is incorrect because it suggests using Amazon Simple Queue Service (Amazon
SQS) to buffer incoming sensor data and use AWS Glue to store the data in Amazon RDS
for querying. This solution has the following disadvantages:
It does not allow you to ingest sensor data in near real time, as Amazon SQS is a
message queue service that delivers messages in a best-effort manner. You need
to use another service, such as Lambda or EC2, to poll the messages from the
queue and process them, which may add extra latency and complexity to your
solution.
It does not allow you to store the data in nested JSON format, as Amazon RDS is
a relational database service that supports structured data types, such as tables
and columns. You need to use another service, such as AWS Glue, to transform
the data from JSON to relational format, which may add extra cost and overhead
to your solution.
References:
1: Amazon Kinesis Data Streams - Features
2: Amazon DynamoDB - Features
3: Loading Streaming Data into Amazon DynamoDB - Amazon Kinesis Data
Firehose
[4]: Capturing Table Activity with DynamoDB Streams - Amazon DynamoDB
[5]: Amazon DynamoDB Accelerator (DAX) - Features
[6]: Amazon S3 - Features
[7]: AWS Lambda - Features
[8]: Amazon Simple Queue Service - Features
[9]: Amazon Relational Database Service - Features
[10]: Working with JSON in Amazon RDS - Amazon Relational Database Service
[11]: AWS Glue - Features
Question # 3
A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A
data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs
to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data
engineer also must orchestrate the data pipeline.
Which AWS service or feature will meet these requirements MOST cost-effectively? | | A. AWS Step Functions | | B. AWS Glue workflows | | C. AWS Glue Studio | | D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) |
B. AWS Glue workflows
Explanation: AWS Glue workflows are a cost-effective way to orchestrate complex ETL
jobs that involve multiple crawlers, jobs, and triggers. AWS Glue workflows allow you to
visually monitor the progress and dependencies of your ETL tasks, and automatically
handle errors and retries. AWS Glue workflows also integrate with other AWS services,
such as Amazon S3, Amazon Redshift, and AWS Lambda, among others, enabling you to
leverage these services for your data processing workflows. AWS Glue workflows are
serverless, meaning you only pay for the resources you use, and you don’t have to manage
any infrastructure.
AWS Step Functions, AWS Glue Studio, and Amazon MWAA are also possible options for
orchestrating ETL pipelines, but they have some drawbacks compared to AWS Glue
workflows. AWS Step Functions is a serverless function orchestrator that can handle
different types of data processing, such as real-time, batch, and stream processing.
However, AWS Step Functions requires you to write code to define your state machines,
which can be complex and error-prone. AWS Step Functions also charges you for every
state transition, which can add up quickly for large-scale ETL pipelines.
AWS Glue Studio is a graphical interface that allows you to create and run AWS Glue ETL
jobs without writing code. AWS Glue Studio simplifies the process of building, debugging,
and monitoring your ETL jobs, and provides a range of pre-built transformations and
connectors. However, AWS Glue Studio does not support workflows, meaning you cannot
orchestrate multiple ETL jobs or crawlers with dependencies and triggers. AWS Glue
Studio also does not support streaming data sources or targets, which limits its use cases
for real-time data processing.
Amazon MWAA is a fully managed service that makes it easy to run open-source versions
of Apache Airflow on AWS and build workflows to run your ETL jobs and data pipelines.
Amazon MWAA provides a familiar and flexible environment for data engineers who are
familiar with Apache Airflow, and integrates with a range of AWS services such as Amazon
EMR, AWS Glue, and AWS Step Functions. However, Amazon MWAA is not serverless,
meaning you have to provision and pay for the resources you need, regardless of your
usage. Amazon MWAA also requires you to write code to define your DAGs, which can be
challenging and time-consuming for complex ETL pipelines.
References:
AWS Glue Workflows
AWS Step Functions
AWS Glue Studio
Amazon MWAA
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
Question # 4
A data engineer must orchestrate a data pipeline that consists of one AWS Lambda
function and one AWS Glue job. The solution must integrate with AWS services.
Which solution will meet these requirements with the LEAST management overhead? | | A. Use an AWS Step Functions workflow that includes a state machine. Configure the state
machine to run the Lambda function and then the AWS Glue job. | | B. Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a
directed acyclic graph (DAG) in which the first task is to call the Lambda function and the
second task is to call the AWS Glue job. | | C. Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job. | | D. Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service
(Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the
Lambda function and the second task is to call the AWS Glue job. |
A. Use an AWS Step Functions workflow that includes a state machine. Configure the state
machine to run the Lambda function and then the AWS Glue job.
Explanation: AWS Step Functions is a service that allows you to coordinate multiple AWS
services into serverless workflows. You can use Step Functions to create state machines
that define the sequence and logic of the tasks in your workflow. Step Functions supports
various types of tasks, such as Lambda functions, AWS Glue jobs, Amazon EMR clusters,
Amazon ECS tasks, etc. You can use Step Functions to monitor and troubleshoot your
workflows, as well as to handle errors and retries.
Using an AWS Step Functions workflow that includes a state machine to run the Lambda
function and then the AWS Glue job will meet the requirements with the leastmanagement
overhead, as it leverages the serverless and managed capabilities of Step Functions. You
do not need to write any code to orchestrate the tasks in your workflow, as you can use the
Step Functions console or the AWS Serverless Application Model (AWS SAM) to define
and deploy your state machine. You also do not need to provision or manage any servers
or clusters, as Step Functions scales automatically based on the demand.
The other options are not as efficient as using an AWS Step Functions workflow. Using an
Apache Airflow workflow that is deployed on an Amazon EC2 instance or on Amazon
Elastic Kubernetes Service (Amazon EKS) will require more management overhead, as
you will need to provision, configure, and maintain the EC2 instance or the EKS cluster, as
well as the Airflow components. You will also need to write and maintain the Airflow DAGs
to orchestrate the tasks in your workflow. Using an AWS Glue workflow to run the Lambda
function and then the AWS Glue job will not work, as AWS Glue workflows only support
AWS Glue jobs and crawlers as tasks, not Lambda functions.
References:
AWS Step Functions
AWS Glue
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide,
Chapter 6: Data Integration and Transformation, Section 6.3: AWS Step Functions
Question # 5
A company extracts approximately 1 TB of data every day from data sources such as SAP
HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some
of the data sources have undefined data schemas or data schemas that change.
A data engineer must implement a solution that can detect the schema for these data
sources. The solution must extract, transform, and load the data to an Amazon S3 bucket.
The company has a service level agreement (SLA) to load the data into the S3 bucket
within 15 minutes of data creation.
Which solution will meet these requirements with the LEAST operational overhead? | | A. Use Amazon EMR to detect the schema and to extract, transform, and load the data into
the S3 bucket. Create a pipeline in Apache Spark. | | B. Use AWS Glue to detect the schema and to extract, transform, and load the data into
the S3 bucket. Create a pipeline in Apache Spark. | | C. Create a PvSpark proqram in AWS Lambda to extract, transform, and load the data into
the S3 bucket. | | D. Create a stored procedure in Amazon Redshift to detect the schema and to extract,
transform, and load the data into a Redshift Spectrum table. Access the table from Amazon
S3. |
B. Use AWS Glue to detect the schema and to extract, transform, and load the data into
the S3 bucket. Create a pipeline in Apache Spark.
Explanation:
AWS Glue is a fully managed service that provides a serverless data integration platform.
It can automatically discover and categorize data from various sources, including SAP
HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. It can
also infer the schema of the data and store it in the AWS Glue Data Catalog, which is a
central metadata repository. AWS Glue can then use the schema information to generate
and run Apache Spark code to extract, transform, and load the data into an Amazon S3
bucket. AWS Glue can also monitor and optimize the performance and cost of the data
pipeline, and handle any schema changes that may occur in the source data. AWS Glue
can meet the SLA of loading the data into the S3 bucket within 15 minutes of data creation, as it can trigger the data pipeline based on events, schedules, or on-demand. AWS Glue
has the least operational overhead among the options, as it does not require provisioning,
configuring, or managing any servers or clusters. It also handles scaling, patching, and
security automatically.
References:
AWS Glue
[AWS Glue Data Catalog]
[AWS Glue Developer Guide]
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
Question # 6
A company has an application that uses a microservice architecture. The company hosts
the application on an Amazon Elastic Kubernetes Services (Amazon EKS) cluster.
The company wants to set up a robust monitoring system for the application. The company
needs to analyze the logs from the EKS cluster and the application. The company needs to
correlate the cluster's logs with the application's traces to identify points of failure in the
whole application request flow.
Which combination of steps will meet these requirements with the LEAST development
effort? (Select TWO.) | | A. Use FluentBit to collect logs. Use OpenTelemetry to collect traces. | | B. Use Amazon CloudWatch to collect logs. Use Amazon Kinesis to collect traces. | | C. Use Amazon CloudWatch to collect logs. Use Amazon Managed Streaming for Apache
Kafka (Amazon MSK) to collect traces. | | D. Use Amazon OpenSearch to correlate the logs and traces. | | E. Use AWS Glue to correlate the logs and traces. |
A. Use FluentBit to collect logs. Use OpenTelemetry to collect traces.
D. Use Amazon OpenSearch to correlate the logs and traces.
Question # 7
A financial services company stores financial data in Amazon Redshift. A data engineer
wants to run real-time queries on the financial data to support a web-based trading
application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead? | | A. Establish WebSocket connections to Amazon Redshift. | | B. Use the Amazon Redshift Data API. | | C. Set up Java Database Connectivity (JDBC) connections to Amazon Redshift. | | D. Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the
queries. |
B. Use the Amazon Redshift Data API.
Explanation: The Amazon Redshift Data API is a built-in feature that allows you to run
SQL queries on Amazon Redshift data with web services-based applications, such as AWS
Lambda, Amazon SageMaker notebooks, and AWS Cloud9. The Data API does not require
a persistent connection to your database, and it provides a secure HTTP endpoint and
integration with AWS SDKs. You can use the endpoint to run SQL statements without
managing connections. The Data API also supports both Amazon Redshift provisioned
clusters and Redshift Serverless workgroups. The Data API is the best solution for running
real-time queries on the financial data from within the trading application, as it has the least
operational overhead compared to the other options.
Option A is not the best solution, as establishing WebSocket connections to Amazon
Redshift would require more configuration and maintenance than using the Data API.
WebSocket connections are also not supported by Amazon Redshift clusters or serverless
workgroups.
Option C is not the best solution, as setting up JDBC connections to Amazon Redshift
would also require more configuration and maintenance than using the Data API. JDBC
connections are also not supported by Redshift Serverless workgroups.
Option D is not the best solution, as storing frequently accessed data in Amazon S3 and
using Amazon S3 Select to run the queries would introduce additional latency and
complexity than using the Data API. Amazon S3 Select is also not optimized for real-time
queries, as it scans the entire object before returning the results.
References:
Using the Amazon Redshift Data API
Calling the Data API
Amazon Redshift Data API Reference
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
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Here is what you need to know: - Target Audience: This exam is geared towards data engineers with 2-3 years of experience and 1-2 years of hands-on experience with AWS.
- Exam Format: You will face 65 questions (50 scored and 15 unscored) in a pass/fail format. AWS uses unscored questions to gauge the effectiveness of future exams.
Exam Content: The exam covers a broad range of topics, including:- We design and implement data pipelines using AWS services like Glue, Lambda, and Step Functions.
- Choosing the right data store (S3, DynamoDB, Redshift, etc.) based on data characteristics and access patterns.
- We are designing data models and ensuring data quality throughout the pipeline.
- We are monitoring and troubleshooting data pipelines for optimal performance and cost efficiency.
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To ace the exam, a comprehensive study plan is crucial. Here are some valuable resources:
Online communities: Join online forums and communities dedicated to AWS data engineering to connect with other aspiring data engineers and exchange study tips and resources.
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What is the purpose of the AWS Certified Data Engineer - Associate (DEA-C01) Exam?
The exam is designed to validate skills in designing, building,
securing, and maintaining analytics solutions on AWS for individuals
with experience in data engineering roles.
What domains does the AWS Certified Data Engineer - Associate exam cover?
The exam covers various domains related to data engineering on AWS,
including data collection, storage, processing, and visualization,
utilizing services like Amazon S3, Amazon Redshift, Amazon DynamoDB,
Amazon EMR, AWS Glue, Amazon Kinesis, and more.
Are there any prerequisites for taking the AWS Certified Data Engineer - Associate exam?
While there are no mandatory prerequisites, candidates must have at
least two years of experience with AWS technology, proficiency in
programming languages, and familiarity with AWS security best practices.
What is the AWS Certified Data Engineer - Associate exam format?
The exam consists of multiple-choice and multiple-answer questions,
assessing candidates' ability to apply AWS data services to derive
insights from data.
How can candidates prepare for the AWS Certified Data Engineer - Associate exam?
Candidates can prepare using resources provided by AWS, such as
training courses, whitepapers, FAQs, and documentation. Practice exams
and study guides are also available to help understand the exam format.
How long is the AWS Certified Data Engineer - Associate certification valid?
The certification is valid for three years from the date of issuance.
How can professionals maintain their AWS Certified Data Engineer - Associate certification?
To maintain certification status, professionals must recertify by
either passing a recertification exam or advancing to a higher level of
certification.
Who can benefit from obtaining the AWS Certified Data Engineer - Associate certification?
Data engineers seeking to prove their skills in cloud data
engineering and advance their career opportunities can benefit from
obtaining this certification.
What critical AWS services are covered in the AWS Certified Data Engineer - Associate exam?
Services such as Amazon S3, Amazon Redshift, Amazon DynamoDB, Amazon
EMR, AWS Glue, and Amazon Kinesis are covered in the exam.
What skills does the AWS Certified Data Engineer - Associate certification demonstrate?
The certification demonstrates expertise in designing, building,
securing, and maintaining analytics solutions on AWS that are efficient,
cost-effective, and scalable.
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