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Exam Code
Data-Engineer-Associate
Exam Name
AWS Certified Data Engineer - Associate (DEA-C01)
Update Date
27 Jul, 2024
Total Questions
80 Questions Answers With Explanation
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Question # 1
A company has five offices in different AWS Regions. Each office has its own humanresources (HR) department that uses a unique IAM role. The company stores employeerecords in a data lake that is based on Amazon S3 storage. A data engineering team needs to limit access to the records. Each HR department shouldbe able to access records for only employees who are within the HR department's Region.Which combination of steps should the data engineering team take to meet thisrequirement with the LEAST operational overhead? (Choose two.)
A. Use data filters for each Region to register the S3 paths as data locations. B. Register the S3 path as an AWS Lake Formation location. C. Modify the IAM roles of the HR departments to add a data filter for each department'sRegion. D. Enable fine-grained access control in AWS Lake Formation. Add a data filter for eachRegion. E. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3access. Restrict access based on Region.
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
Question # 2
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health datafrom wearable devices, hospital equipment, and patient records.A data engineer needs to find a solution to process the streaming data. The data engineerneeds to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.Which solution will meet these requirements with the LEAST operational overhead?
A. Load data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift. B. Use the streaming ingestion feature of Amazon Redshift. C. Load the data into Amazon S3. Use the COPY command to load the data into AmazonRedshift. D. Use the Amazon Aurora zero-ETL integration with Amazon Redshift.
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
Question # 3
A company is migrating a legacy application to an Amazon S3 based data lake. A dataengineer reviewed data that is associated with the legacy application. The data engineerfound that the legacy data contained some duplicate information.The data engineer must identify and remove duplicate information from the legacyapplication data.Which solution will meet these requirements with the LEAST operational overhead?
A. Write a custom extract, transform, and load (ETL) job in Python. Use theDataFramedrop duplicatesf) function by importingthe Pandas library to perform datadeduplication. B. Write an AWS Glue extract, transform, and load (ETL) job. Usethe FindMatchesmachine learning(ML) transform to transform the data to perform data deduplication. C. Write a custom extract, transform, and load (ETL) job in Python. Import the Pythondedupe library. Use the dedupe library to perform data deduplication. D. Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupelibrary. Use the dedupe library to perform data deduplication.
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
Question # 4
A company needs to build a data lake in AWS. The company must provide row-level dataaccess and column-level data access to specific teams. The teams will access the data byusing Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access byrows and columns. Provide data access throughAmazon S3. B. Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR torestrict data access byrows and columns. Providedata access by using Apache Pig. C. Use Amazon Redshift for data lake storage. Use Redshift security policies to restrictdata access byrows and columns. Provide data accessby usingApache Spark and AmazonAthena federated queries. D. UseAmazon S3 for data lake storage. Use AWS Lake Formation to restrict data accessby rows and columns. Provide data access through AWS Lake Formation.
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
Question # 5
A company uses an Amazon Redshift provisioned cluster as its database. The Redshiftcluster 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%. SQLQueries that run on the node are queued. The other four nodes usually have a CPU loadunder 15% during daily operations.The data engineer wants to maintain the current number of compute nodes. The dataengineer 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 clauseof 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 WHEREclause of the SQL SELECT statement.
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
Question # 6
A company is developing an application that runs on Amazon EC2 instances. Currently, thedata that the application generates is temporary. However, the company needs to persistthe data, even if the EC2 instances are terminated.A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI)and configure the instances to preserve the data.Which solution will meet this requirement?
A. Launch new EC2 instances by using an AMI that is backed by an EC2 instance storevolume that contains the application data. Apply the default settings to the EC2 instances. B. Launch new EC2 instances by using an AMI that is backed by a root Amazon ElasticBlock Store (Amazon EBS) volume that contains the application data. Apply the defaultsettings to the EC2 instances. C. Launch new EC2 instances by using an AMI that is backed by an EC2 instance storevolume. Attach an Amazon Elastic Block Store (Amazon EBS) volume to contain theapplication data. Apply the default settings to the EC2 instances. D. Launch new EC2 instances by using an AMI that is backed by an Amazon Elastic BlockStore (Amazon EBS) volume. Attach an additional EC2 instance store volume to containthe application data. Apply the default settings to the EC2 instances.
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
Question # 7
A data engineer must ingest a source of structured data that is in .csv format into anAmazon S3 data lake. The .csv files contain 15 columns. Data analysts need to runAmazon Athena queries on one or two columns of the dataset. The data analysts rarelyquery the entire file.Which solution will meet these requirements MOST cost-effectively?
A. Use an AWS Glue PySpark job to ingest the source data into the data lake in .csvformat. B. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csvstructured data source. Configure the job to ingest the data into the data lake in JSONformat.C. Use an AWS Glue PySpark job to ingest the source data into the data lake in ApacheAvro format. D. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csvstructured data source. Configure the job to write the data into the data lake in ApacheParquet format.
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
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, creating an AWS Glue extract, transform, and load (ETL) job to read from the
.csv structured data source and writing the data into the data lake in Apache Parquet
format will meet the requirements most cost-effectively. AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data
cataloging, and data loading. AWS Glue ETL jobs allow you to transform and load data
from various sources into various targets, using either a graphical interface (AWS Glue
Studio) or a code-based interface (AWS Glue console or AWS Glue API). By using AWS
Glue ETL jobs, you can easily convert the data from CSV to Parquet format, without having
to write or manage any code. Parquet is a column-oriented format that allows Athena to
scan only the relevant columns and skip the rest, reducing the amount of data read from
S3. 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 cost-effective as creating an AWS Glue ETL job to write the
data into the data lake in Parquet format. Using an AWS Glue PySpark job to ingest the
source data into the data lake in .csv format will not improve the query performance or
reduce the query cost, as .csv is a row-oriented format that does not support columnar
access or compression. Creating an AWS Glue ETL job to ingest the data into the data
lake in JSON format will not improve the query performance or reduce the query cost, as
JSON is also a row-oriented format that does not support columnar access or compression.
Using an AWS Glue PySpark job to ingest the source data into the data lake in Apache
Avro format will improve the query performance, as Avro is a column-oriented format that
supports compression and encoding, but it will require more operational effort, as you will
need to write and maintain PySpark code to convert the data from CSV to Avro format.
References:
Amazon Athena
Choosing the Right Data Format
AWS Glue
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide],
Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena
Question # 8
A data engineer uses Amazon Redshift to run resource-intensive analytics processes onceevery month. Every month, the data engineer creates a new Redshift provisioned cluster.The data engineer deletes the Redshift provisioned cluster after the analytics processesare complete every month. Before the data engineer deletes the cluster each month, thedata engineer unloads backup data from the cluster to an Amazon S3 bucket.The data engineer needs a solution to run the monthly analytics processes that does notrequire the data engineer to manage the infrastructure manually.Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon Step Functions to pause the Redshift cluster when the analytics processesare complete and to resume the cluster to run new processes every month. B. Use Amazon Redshift Serverless to automatically process the analytics workload. C. Use the AWS CLI to automatically process the analytics workload. D. Use AWS CloudFormation templates to automatically process the analytics workload.
Answer: B
Explanation: Amazon Redshift Serverless is a new feature of Amazon Redshift that
enables you to run SQL queries on data in Amazon S3 without provisioning or managing
any clusters. You can use Amazon Redshift Serverless to automatically process the
analytics workload, as it scales up and down the compute resources based on the query
demand, and charges you only for the resources consumed. This solution will meet the
requirements with the least operational overhead, as it does not require the data engineer
to create, delete, pause, or resume any Redshift clusters, or to manage any infrastructure
manually. You can use the Amazon Redshift Data API to run queries from the AWS CLI,
AWS SDK, or AWS Lambda functions12.
The other options are not optimal for the following reasons:
A. Use Amazon Step Functions to pause the Redshift cluster when the analytics
processes are complete and to resume the cluster to run new processes every
month. This option is not recommended, as it would still require the data engineer
to create and delete a new Redshift provisioned cluster every month, which can
incur additional costs and time. Moreover, this option would require the data
engineer to use Amazon Step Functions to orchestrate the workflow of pausing
and resuming the cluster, which can add complexity and overhead.
C. Use the AWS CLI to automatically process the analytics workload. This option
is vague and does not specify how the AWS CLI is used to process the analytics
workload. The AWS CLI can be used to run queries on data in Amazon S3 using
Amazon Redshift Serverless, Amazon Athena, or Amazon EMR, but each of these
services has different features and benefits. Moreover, this option does not
address the requirement of not managing the infrastructure manually, as the data
engineer may still need to provision and configure some resources, such as
Amazon EMR clusters or Amazon Athena workgroups.
D. Use AWS CloudFormation templates to automatically process the analytics
workload. This option is also vague and does not specify how AWS
CloudFormation templates are used to process the analytics workload. AWS
CloudFormation is a service that lets you model and provision AWS resources
using templates. You can use AWS CloudFormation templates to create and
delete a Redshift provisioned cluster every month, or to create and configure other
AWS resources, such as Amazon EMR, Amazon Athena, or Amazon Redshift
Serverless. However, this option does not address the requirement of not
managing the infrastructure manually, as the data engineer may still need to write
and maintain the AWS CloudFormation templates, and to monitor the status and
performance of the resources.
References:
1: Amazon Redshift Serverless
2: Amazon Redshift Data API
: Amazon Step Functions
: AWS CLI
: AWS CloudFormation
Question # 9
A financial company wants to use Amazon Athena to run on-demand SQL queries on apetabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue jobthat runs during non-business hours updates the dataset once every day. The BIapplication has a standard data refresh frequency of 1 hour to comply with companypolicies. A data engineer wants to cost optimize the company's use of Amazon Athena withoutadding any additional infrastructure costs.Which solution will meet these requirements with the LEAST operational overhead?
A. Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archivestorage class after 1 day B. Use the query result reuse feature of Amazon Athena for the SQL queries. C. Add an Amazon ElastiCache cluster between the Bl application and Athena. D. Change the format of the files that are in the dataset to Apache Parquet.
Answer: B
Explanation: The best solution to cost optimize the company’s use of Amazon Athena
without adding any additional infrastructure costs is to use the query result reuse feature of
AmazonAthena for the SQL queries. This feature allows you to run the same query multiple
times without incurring additional charges, as long as the underlying data has not changed
and the query results are still in the query result location in Amazon S31. This feature is
useful for scenarios where you have a petabyte-scale dataset that is updated infrequently,
such as once a day, and you have a BI application that runs the same queries repeatedly,
such as every hour. By using the query result reuse feature, you can reduce the amount of
data scanned by your queries and save on the cost of running Athena. You can enable or
disable this feature at the workgroup level or at the individual query level1.
Option A is not the best solution, as configuring an Amazon S3 Lifecycle policy to move
data to the S3 Glacier Deep Archive storage class after 1 day would not cost optimize the
company’s use of Amazon Athena, but rather increase the cost and complexity. Amazon
S3 Lifecycle policies are rules that you can define to automatically transition objects
between different storage classes based on specified criteria, such as the age of the
object2. S3 Glacier Deep Archive is the lowest-cost storage class in Amazon S3, designed
for long-term data archiving that is accessed once or twice in a year3. While moving data to
S3 Glacier Deep Archive can reduce the storage cost, it would also increase the retrieval
cost and latency, as it takes up to 12 hours to restore the data from S3 Glacier Deep
Archive3. Moreover, Athena does not support querying data that is in S3 Glacier or S3
Glacier Deep Archive storage classes4. Therefore, using this option would not meet the
requirements of running on-demand SQL queries on the dataset.
Option C is not the best solution, as adding an Amazon ElastiCache cluster between the BI
application and Athena would not cost optimize the company’s use of Amazon Athena, but
rather increase the cost and complexity. Amazon ElastiCache is a service that offers fully
managed in-memory data stores, such as Redis and Memcached, that can improve the
performance and scalability of web applications by caching frequently accessed data.
While using ElastiCache can reduce the latency and load on the BI application, it would not
reduce the amount of data scanned by Athena, which is the main factor that determines the
cost of running Athena. Moreover, using ElastiCache would introduce additional infrastructure costs and operational overhead, as you would have to provision, manage,
and scale the ElastiCache cluster, and integrate it with the BI application and Athena.
Option D is not the best solution, as changing the format of the files that are in the dataset
to Apache Parquet would not cost optimize the company’s use of Amazon Athena without
adding any additional infrastructure costs, but rather increase the complexity. 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 schemes. However,changing the format of the files that are in
the dataset to Apache Parquet would require additional processing and transformation
steps, such as using AWS Glue or Amazon EMR to convert the files from their original
format to Parquet, and storing the converted files in a separate location in Amazon S3. This
would increase the complexity and the operational overhead of the data pipeline, and also
incur additional costs for using AWS Glue or Amazon EMR. References:
Query result reuse
Amazon S3 Lifecycle
S3 Glacier Deep Archive
Storage classes supported by Athena
[What is Amazon ElastiCache?]
[Amazon Athena pricing]
[Columnar Storage Formats]
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
Question # 10
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wantsto scale read and write capacity to meet demand. A data engineer needs to identify asolution that will turn on concurrency scaling.Which solution will meet this requirement?
A. Turn on concurrency scaling in workload management (WLM) for Redshift Serverlessworkgroups. B. Turn on concurrency scaling at the workload management (WLM) queue level in theRedshift cluster. C. Turn on concurrency scaling in the settings duringthe creation of andnew Redshiftcluster. D. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.
Answer: B
Explanation: Concurrency scaling is a feature that allows you to support thousands of
concurrent users and queries, with consistently fast query performance. When you turn on
concurrency scaling, Amazon Redshift automatically adds query processing power in
seconds to process queries without any delays. You can manage which queries are sent to
the concurrency-scaling cluster by configuring WLM queues. To turn on concurrency
scaling for a queue, set the Concurrency Scaling mode value to auto. The other options are
either incorrect or irrelevant, as they do not enable concurrency scaling for the existing
Redshift cluster on RA3 nodes. References:
Working with concurrency scaling - Amazon Redshift
Amazon Redshift Concurrency Scaling - Amazon Web Services
Zahra Faizan - Jul 27, 2024
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