Question # 1
A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General
Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in
its Amazon EC2 instances that will cause data loss during the migration to the upgraded
storage.
Which solution will meet these requirements with the LEAST operational overhead? | | A. Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots.
Attach the new gp3 volumes to the EC2 instances. | | B. Create new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the
transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2
volumes. | | C. Change the volume type of the existing gp2 volumes to gp3. Enter new values for
volume size, IOPS, and throughput. | | D. Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2
volumes to the new gp3 volumes. |
C. Change the volume type of the existing gp2 volumes to gp3. Enter new values for
volume size, IOPS, and throughput.
Explanation: Changing the volume type of the existing gp2 volumes to gp3 is the easiest
and fastest way to migrate to the new storage type without any downtime or data loss. You
can use the AWS Management Console, the AWS CLI, or the Amazon EC2 API to modify
the volume type, size, IOPS, and throughput of your gp2 volumes. The modification takes effect immediately, and you can monitor the progress of the modification using
CloudWatch. The other options are either more complex or require additional steps, such
as creating snapshots, transferring data, or attaching new volumes, which can increase the
operational overhead and the risk of errors.
References:
Migrating Amazon EBS volumes from gp2 to gp3 and save up to 20% on
costs (Section: How to migrate from gp2 to gp3)
Switching from gp2 Volumes to gp3 Volumes to Lower AWS EBS Costs (Section:
How to Switch from GP2 Volumes to GP3 Volumes)
Modifying the volume type, IOPS, or size of an EBS volume - Amazon Elastic
Compute Cloud (Section: Modifying the volume type)
Question # 2
A company implements a data mesh that has a central governance account. The company
needs to catalog all data in the governance account. The governance account uses AWS
Lake Formation to centrally share data and grant access permissions.
The company has created a new data product that includes a group of Amazon Redshift
Serverless tables. A data engineer needs to share the data product with a marketing team.
The marketing team must have access to only a subset of columns. The data engineer
needs to share the same data product with a compliance team. The compliance team must
have access to a different subset of columns than the marketing team needs access to.
Which combination of steps should the data engineer take to meet these requirements?
(Select TWO.) | | A. Create views of the tables that need to be shared. Include only the required columns. | | B. Create an Amazon Redshift data than that includes the tables that need to be shared. | | C. Create an Amazon Redshift managed VPC endpoint in the marketing team's account.
Grant the marketing team access to the views. | | D. Share the Amazon Redshift data share to the Lake Formation catalog in the governance
account. | | E. Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in
the marketing team's account. |
A. Create views of the tables that need to be shared. Include only the required columns.
E. Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in
the marketing team's account.
Explanation: The company is using a data mesh architecture with AWS Lake Formation
for governance and needs to share specific subsets of data with different teams (marketing
and compliance) using Amazon Redshift Serverless.
Option A: Create views of the tables that need to be shared. Include only the
required columns.Creatingviewsin Amazon Redshift that include only the
necessary columns allows for fine-grained access control. This method ensures
that each team has access to only the data they are authorized to view.
Option E: Share the Amazon Redshift data share to the Amazon Redshift
Serverless workgroup in the marketing team's account.Amazon Redshift data
sharingenables live access to data across Redshift clusters or Serverless workgroups. By sharing data with specific workgroups, you can ensure that the
marketing team and compliance team each access the relevant subset of data
based on the views created.
Option B(creating a Redshift data share) is close but does not address the finegrained
column-level access.
Option C(creating a managed VPC endpoint) is unnecessary for sharing data with
specific teams.
Option D(sharing with the Lake Formation catalog) is incorrect because Redshift
data shares do not integrate directly with Lake Formation catalogs; they are
specific to Redshift workgroups.
References:
Amazon Redshift Data Sharing
AWS Lake Formation Documentation
Question # 3
A company has a data lake in Amazon S3. The company collects AWS CloudTrail logs for
multiple applications. The company stores the logs in the data lake, catalogs the logs in
AWS Glue, and partitions the logs based on the year. The company uses Amazon Athena
to analyze the logs.
Recently, customers reported that a query on one of the Athena tables did not return any data. A data engineer must resolve the issue.
Which combination of troubleshooting steps should the data engineer take? (Select TWO.) | | A. Confirm that Athena is pointing to the correct Amazon S3 location. | | B. Increase the query timeout duration. | | C. Use the MSCK REPAIR TABLE command. | | D. Restart Athena. | | E. Delete and recreate the problematic Athena table. |
A. Confirm that Athena is pointing to the correct Amazon S3 location.
C. Use the MSCK REPAIR TABLE command.
Explanation: The problem likely arises from Athena not being able to read from the correct
S3 location or missing partitions. The two most relevant troubleshooting steps involve
checking the S3 location and repairing the table metadata.
A. Confirm that Athena is pointing to the correct Amazon S3 location:
Reference:Amazon Athena Troubleshooting
C. Use the MSCK REPAIR TABLE command:
When new partitions are added to the S3 bucket without being reflected in the Glue Data
Catalog, Athena queries will not return data from those partitions. The MSCK REPAIR
TABLE command updates the Glue Data Catalog with the latest partitions.
Reference:MSCK REPAIR TABLE Command
Alternatives Considered:
B (Increase query timeout): Timeout issues are unrelated to missing data.
D (Restart Athena): Athena does not require restarting.
E (Delete and recreate table): This introduces unnecessary overhead when the issue can
be resolved by repairing the table and confirming the S3 location.
References:
Athena Query Fails to Return Data
Question # 4
A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark
jobs to perform big data analysis. The company requires high reliability. A big data team
must follow best practices for running cost-optimized and long-running workloads on
Amazon EMR. The team must find a solution that will maintain the company's current level
of 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. |
B. Use Amazon S3 as a persistent data store.
D. Use Graviton instances for core nodes and task nodes.
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 useAmazon 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 be
used 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.
Question # 5
A telecommunications company collects network usage data throughout each day at a rate
of several thousand data points each second. The company runs an application to process
the usage data in real time. The company aggregates and stores the data in an Amazon
Aurora DB instance.
Sudden drops in network usage usually indicate a network outage. The company must be
able to identify sudden drops in network usage so the company can take immediate
remedial actions.
Which solution will meet this requirement with the LEAST latency? | | A. Create an AWS Lambda function to query Aurora for drops in network usage. Use
Amazon EventBridge to automatically invoke the Lambda function every minute. | | B. Modify the processing application to publish the data to an Amazon Kinesis data stream.
Create an Amazon Managed Service for Apache Flink (previously known as Amazon
Kinesis Data Analytics) application to detect drops in network usage. | | C. Replace the Aurora database with an Amazon DynamoDB table. Create an AWS
Lambda function to query the DynamoDB table for drops in network usage every minute.
Use DynamoDB Accelerator (DAX) between the processing application and DynamoDB
table. | | D. Create an AWS Lambda function within the Database Activity Streams feature of Aurora
to detect drops in network usage. |
B. Modify the processing application to publish the data to an Amazon Kinesis data stream.
Create an Amazon Managed Service for Apache Flink (previously known as Amazon
Kinesis Data Analytics) application to detect drops in network usage.
Explanation: The telecommunications company needs a low-latency solution to detect
sudden drops in network usage from real-time data collected throughout the day.
Option B: Modify the processing application to publish the data to an Amazon
Kinesis data stream. Create an Amazon Managed Service for Apache Flink
(Amazon Kinesis Data Analytics) application to detect drops in network
usage.UsingAmazon KinesiswithManaged Service for Apache Flink (formerly
Kinesis Data Analytics)is ideal for real-time stream processing with minimal
latency. Flink can analyze the incoming data stream in real-time and detect
anomalies, such as sudden drops in usage, which makes it the best fit for this
scenario.
Other options (A, C, and D) either introduce unnecessary delays (e.g., querying databases)
or do not provide the same real-time, low-latency processing that is critical for this use
case.
References:
Amazon Kinesis Data Analytics for Apache Flink
Question # 6
A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The
company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB
tables that are in provisioned capacity mode, and an Amazon Redshift cluster. A data
engineering team must develop a solution that will give data scientists the ability to query
all data sources by using syntax similar to SQL.
Which solution will meet these requirements with the LEAST operational overhead? | | A. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data
Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use
PartiQL for data that is stored in JSON format. | | B. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data
Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources.
Use PartiQL for data that is stored in JSON format. | | C. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet
or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query
the original and transformed data from the S3 bucket. | | D. Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform
the data from all data sources to Apache Parquet format. Store the transformed data in an
S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data. |
A. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data
Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use
PartiQL for data that is stored in JSON format.
Explanation: The best solution to meet the requirements of giving data scientists the ability
to query all data sources by using syntax similar to SQL with the least operational overhead
is to use AWS Glue to crawl the data sources, store metadata in the AWS Glue Data
Catalog, use Amazon Athena to query the data, use SQL for structured data sources, and
use PartiQL for data that is stored in JSON format.
AWS Glue is a serverless data integration service that makes it easy to prepare, clean,
enrich, and move data between data stores1. AWS Glue crawlers are processes that
connect to a data store, progress through a prioritized list of classifiers to determine the
schema for your data, and then create metadata tables in the Data Catalog2. The Data
Catalog is a persistent metadata store that contains table definitions, job definitions, and
other control information to help you manage your AWS Glue components3. You can use
AWS Glue to crawl the data sources, such as Amazon S3, Amazon RDS for Microsoft SQL
Server, and Amazon DynamoDB, and store the metadata in the Data Catalog.
Amazon Athena is a serverless, interactive query service that makes it easy to analyze
data directly in Amazon S3 using standard SQL or Python4. Amazon Athena also supports
PartiQL, a SQL-compatible query language that lets you query, insert, update, and delete
data from semi-structured and nested data, such as JSON. You can use Amazon Athena to
query the data from the Data Catalog using SQL for structured data sources, such as .csv
files and relational databases, and PartiQL for data that is stored in JSON format. You can
also use Athena to query data from other data sources, such as Amazon Redshift, using
federated queries.
Using AWS Glue and Amazon Athena to query all data sources by using syntax similar to
SQL is the least operational overhead solution, as you do not need to provision, manage,
or scale any infrastructure, and you pay only for the resources you use. AWS Glue charges
you based on the compute time and the data processed by your crawlers and ETL jobs1.
Amazon Athena charges you based on the amount of data scanned byyour queries. You
can also reduce the cost and improve the performance of your queries by using
compression, partitioning, and columnar formats for your data in Amazon S3.
Option B is not the best solution, as using AWS Glue to crawl the data sources, store
metadata in the AWS Glue Data Catalog, and use Redshift Spectrum to query the data,
would incur more costs and complexity than using Amazon Athena. Redshift Spectrum is a
feature of Amazon Redshift, a fully managed data warehouse service, that allows you to
query and join data across your data warehouse and your data lake using standard SQL.
While Redshift Spectrum is powerful and useful for many data warehousing scenarios, it is
not necessary or cost-effective for querying all data sources by using syntax similar to
SQL. Redshift Spectrum charges you based on the amount of data scanned by your
queries, which is similar to Amazon Athena, but it also requires you to have an Amazon
Redshift cluster, which charges you based on the node type, the number of nodes, and the
duration of the cluster5. These costs can add up quickly, especially if you have large
volumes of data and complex queries. Moreover, using Redshift Spectrum would introduce
additional latency and complexity, as you would have to provision and manage the cluster,
and create an external schema and database for the data in the Data Catalog, instead of
querying it directly from Amazon Athena.
Option C is not the best solution, as using AWS Glue to crawl the data sources, store
metadata in the AWS Glue Data Catalog, use AWS Glue jobs to transform data that is in
JSON format to Apache Parquet or .csv format, store the transformed data in an S3 bucket,
and use Amazon Athena to query the original and transformed data from the S3 bucket,
would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Glue
jobs are ETL scripts that you can write in Python or Scala to transform your data and load it
to your target data store. Apache Parquet is a columnar storage format that can improve
the performance of analytical queries by reducing the amount of data that needs to be
scanned and providing efficient compression and encoding schemes6. While using AWS
Glue jobs and Parquet can improve the performance and reduce the cost of your queries,
they would also increase the complexity and the operational overhead of the data pipeline,
as you would have to write, run, and monitor the ETL jobs, and store the transformed data
in a separate location in Amazon S3. Moreover, using AWS Glue jobs and Parquet would
introduce additional latency, as you would have to wait for the ETL jobs to finish before
querying the transformed data.
Option D is not the best solution, as using AWS Lake Formation to create a data lake, use
Lake Formation jobs to transform the data from all data sources to Apache Parquet format,
store the transformed data in an S3 bucket, and use Amazon Athena or Redshift Spectrum
to query the data, would incur more costs and complexity than using Amazon Athena with
PartiQL. AWS Lake Formation is a service that helps you centrally govern, secure, and
globally share data for analytics and machine learning7. Lake Formation jobs are ETL jobs
that you can create and run using the Lake Formation console or API. While using Lake
Formation and Parquet can improve the performance and reduce the cost ofyour queries,
they would also increase the complexity and the operational overhead of the data pipeline,
as you would have to create, run, and monitor the Lake Formation jobs, and store the
transformed data in a separate location in Amazon S3. Moreover, using Lake Formation
and Parquet would introduce additional latency, as you would have to wait for the Lake
Formation jobs to finish before querying the transformed data. Furthermore, using Redshift
Spectrum to query the data would also incur the same costs and complexity as mentioned
in option B.
References:
What is Amazon Athena?
Data Catalog and crawlers in AWS Glue
AWS Glue Data Catalog
Columnar Storage Formats
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
AWS Glue Schema Registry
What is AWS Glue?
Amazon Redshift Serverless
Amazon Redshift provisioned clusters
[Querying external data using Amazon Redshift Spectrum]
[Using stored procedures in Amazon Redshift]
[What is AWS Lambda?]
[PartiQL for Amazon Athena]
[Federated queries in Amazon Athena]
[Amazon Athena pricing]
[Top 10 performance tuning tips for Amazon Athena]
[AWS Glue ETL jobs]
[AWS Lake Formation jobs]
Question # 7
A retail company uses Amazon Aurora PostgreSQL to process and store live transactional
data. The company uses an Amazon Redshift cluster for a data warehouse.
An extract, transform, and load (ETL) job runs every morning to update the Redshift cluster
with new data from the PostgreSQL database. The company has grown rapidly and needs
to cost optimize the Redshift cluster.
A data engineer needs to create a solution to archive historical data. The data engineer
must be able to run analytics queries that effectively combine data from live transactional
data in PostgreSQL, current data in Redshift, and archived historical data. The solution
must keep only the most recent 15 months of data in Amazon Redshift to reduce costs.
Which combination of steps will meet these requirements? (Select TWO.) | | A. Configure the Amazon Redshift Federated Query feature to query live transactional data
that is in the PostgreSQL database. | | B. Configure Amazon Redshift Spectrum to query live transactional data that is in the
PostgreSQL database. | | C. Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by
using the UNLOAD command. Delete the old data from the Redshift cluster. Configure
Amazon Redshift Spectrum to access historical data in Amazon S3. | | D. Schedule a monthly job to copy data that is older than 15 months to Amazon S3 Glacier
Flexible Retrieval by using the UNLOAD command. Delete the old data from the Redshift
duster. Configure Redshift Spectrum to access historical data from S3 Glacier Flexible
Retrieval. | | E. Create a materialized view in Amazon Redshift that combines live, current, and historical
data from different sources. |
A. Configure the Amazon Redshift Federated Query feature to query live transactional data
that is in the PostgreSQL database.
C. Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by
using the UNLOAD command. Delete the old data from the Redshift cluster. Configure
Amazon Redshift Spectrum to access historical data in Amazon S3.
Explanation: The goal is to archive historical data from an Amazon Redshift data
warehouse while combining live transactional data from Amazon Aurora PostgreSQL with
current andhistorical data in a cost-efficient manner. The company wants to keep only the
last 15 months of data in Redshift to reduce costs.
Option A: "Configure the Amazon Redshift Federated Query feature to query live
transactional data that is in the PostgreSQL database."Redshift Federated
Queryallows querying live transactional data directly from Aurora PostgreSQL
without having to move it into Redshift, thereby enabling seamless integration of
the current data in Redshift and live data in PostgreSQL. This is a cost-effective
approach, as it avoids unnecessary data duplication.
Option C: "Schedule a monthly job to copy data that is older than 15 months to
Amazon S3 by using the UNLOAD command. Delete the old data from the
Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in
Amazon S3."This option usesAmazon Redshift Spectrum, which enables Redshift
to query data directly in S3 without moving it into Redshift. By unloading older data
(older than 15 months) to S3, and then using Spectrum to access it, this approach
reduces storage costs significantly while still allowing the data to be queried when
necessary.
Option B (Redshift Spectrum for live PostgreSQL data)is not applicable, as
Redshift Spectrum is intended for querying data in Amazon S3, not live
transactional data in Aurora.
Option D (S3 Glacier Flexible Retrieval)is not suitable because Glacier is designed
for long-term archival storage with infrequent access, and querying data in Glacier
for analytics purposes would incur higher retrieval times and costs.
Option E (materialized views)would not meet the need to archive data or combine
it from multiple sources; it is best suited for combining frequently accessed data
already in Redshift.
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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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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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