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Amazon Data-Engineer-Associate Exam Questions

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AWS Certified Data Engineer - Associate (DEA-C01)

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Amazon Data-Engineer-Associate Sample Questions – Free Practice Test & Real Exam Prep

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
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