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DEA-C01: AWS Certified Data Engineer - Associate
Amazon
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The AWS Certified Data Analytics – Specialty (DEA-C01) exam is a crucial certification for anyone aiming to advance their career in data analytics. Our topic is your ultimate resource for DEA-C01 practice test shared by individuals who have successfully passed the exam. These practice tests provide real-world scenarios and invaluable insights to help you ace your preparation.
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Exam Number: DEA-C01
Exam Name: AWS Certified Data Analytics – Specialty
Length of Test: 130 minutes
Exam Format: Multiple-choice and multiple-response questions.
Exam Language: English
Number of Questions in the Actual Exam: Maximum of 65 questions
Passing Score: 750/1000
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Related questions
A company has a data warehouse that contains a table that is named Sales. The company stores the table in Amazon Redshift The table includes a column that is named city_name. The company wants to query the table to find all rows that have a city_name that starts with 'San' or 'El.'
Which SQL query will meet this requirement?
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A technology company currently uses Amazon Kinesis Data Streams to collect log data in real time. The company wants to use Amazon Redshift for downstream real-time queries and to enrich the log data.
Which solution will ingest data into Amazon Redshift with the LEAST operational overhead?
An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)
Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.
Use an S3 bucket that is in the same account that uses Athena to query the data.
Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.
Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.
Explanation:
Using an S3 bucket that is in the same AWS Region where the company runs Athena queries can improve query performance by reducing data transfer latency and costs. Preprocessing the .csv data to Apache Parquet format can also improve query performance by enabling columnar storage, compression, and partitioning, which can reduce the amount of data scanned and fetched by the query. These solutions can optimize the storage solution for the POC test without requiring much effort or changes to the existing data pipeline. The other solutions are not optimal or relevant for this requirement. Adding a randomized string to the beginning of the keys in Amazon S3 can improve the throughput across partitions, but it can also make the data harder to query and manage. Using an S3 bucket that is in the same account that uses Athena to query the data does not have any significant impact on query performance, as long as the proper permissions are granted. Preprocessing the .csv data to JSON format does not offer any benefits over the .csv format, as both are row-based and verbose formats that require more data scanning and fetching than columnar formats like Parquet.Reference:
Best Practices When Using Athena with AWS Glue
Optimizing Amazon S3 Performance
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3.
The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata.
Which solution will meet these requirements with the LEAST operational overhead?
Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.
Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.
Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.
Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog.
Explanation:
This solution will meet the requirements with the least operational overhead because it uses the AWS Glue Data Catalog as the central metadata repository for data sources that run in the AWS Cloud. The AWS Glue Data Catalog is a fully managed service that provides a unified view of your data assets across AWS and on-premises data sources. It stores the metadata of your data in tables, partitions, and columns, and enables you to access and query your data using various AWS services, such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. You can use AWS Glue crawlers to connect to multiple data stores, such as Amazon RDS, Amazon Redshift, and Amazon S3, and to update the Data Catalog with metadata changes. AWS Glue crawlers can automatically discover the schema and partition structure of your data, and create or update the corresponding tables in the Data Catalog.You can schedule the crawlers to run periodically to update the metadata catalog, and configure them to detect changes to the source metadata, such as new columns, tables, or partitions12.
The other options are not optimal for the following reasons:
A . Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically. This option is not recommended, as it would require more operational overhead to create and manage an Amazon Aurora database as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.
C . Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically. This option is also not recommended, as it would require more operational overhead to create and manage an Amazon DynamoDB table as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.
D . Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog. This option is not optimal, as it would require more manual effort to extract the schema for Amazon RDS and Amazon Redshift sources, and to build the Data Catalog. This option would not take advantage of the AWS Glue crawlers' ability to automatically discover the schema and partition structure of your data from various data sources, and to create or update the corresponding tables in the Data Catalog.
1: AWS Glue Data Catalog
2: AWS Glue Crawlers
: Amazon Aurora
: AWS Lambda
: Amazon DynamoDB
A company is using Amazon Redshift to build a data warehouse solution. The company is loading hundreds of tiles into a tact table that is in a Redshift cluster.
The company wants the data warehouse solution to achieve the greatest possible throughput. The solution must use cluster resources optimally when the company loads data into the tact table.
Which solution will meet these requirements?
A data engineer needs to onboard a new data producer into AWS. The data producer needs to migrate data products to AWS.
The data producer maintains many data pipelines that support a business application. Each pipeline must have service accounts and their corresponding credentials. The data engineer must establish a secure connection from the data producer's on-premises data center to AWS. The data engineer must not use the public internet to transfer data from an on-premises data center to AWS.
Which solution will meet these requirements?
A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?
Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access through Amazon S3.
Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access by rows and columns. Provide data access by using Apache Pig.
Use Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access by rows and columns. Provide data access by using Apache Spark and Amazon Athena federated queries.
Use Amazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.
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 row-level 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 semi-structured data, which are typical characteristics of data lakes. Amazon Redshift is also more expensive and less scalable than Amazon S3 for data lake storage.
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
A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day.
Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM. A business intelligence team queries the tables between 10 AM and 8 PM every day.
Which solution will meet these requirements in the MOST operationally efficient way?
A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size.
A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file.
Which solution will meet this requirement with the LEAST operational effort?
Create and run an Apache Spark job in an AWS Glue notebook. Configure the job to read the S3 file and calculate the number of distinct customers.
Create an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file. Run SQL queries from Amazon Athena to calculate the number of distinct customers.
Create and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.
Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.
Explanation:
AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. You can use DataBrew to create recipes that define the steps to apply to your data, such as filtering, renaming, splitting, or aggregating columns. You can also use DataBrew to run jobs that execute the recipes on your data sources, such as Amazon S3, Amazon Redshift, or Amazon Aurora.DataBrew integrates with AWS Glue Data Catalog, which is a centralized metadata repository for your data assets1.
The solution that meets the requirement with the least operational effort is to use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers. This solution has the following advantages:
It does not require you to write any code, as DataBrew provides a graphical user interface that lets you explore, transform, and visualize your data.You can use DataBrew to concatenate the columns that contain customer first names and last names, and then use the COUNT_DISTINCT aggregate function to count the number of unique values in the resulting column2.
It does not require you to provision, manage, or scale any servers, clusters, or notebooks, as DataBrew is a fully managed service that handles all the infrastructure for you.DataBrew can automatically scale up or down the compute resources based on the size and complexity of your data and recipes1.
It does not require you to create or update any AWS Glue Data Catalog entries, as DataBrew can automatically create and register the data sources and targets in the Data Catalog.DataBrew can also use the existing Data Catalog entries to access the data in S3 or other sources3.
Option A is incorrect because it suggests creating and running an Apache Spark job in an AWS Glue notebook. This solution has the following disadvantages:
It requires you to write code, as AWS Glue notebooks are interactive development environments that allow you to write, test, and debug Apache Spark code using Python or Scala. You need to use the Spark SQL or the Spark DataFrame API to read the S3 file and calculate the number of distinct customers.
It requires you to provision and manage a development endpoint, which is a serverless Apache Spark environment that you can connect to your notebook. You need to specify the type and number of workers for your development endpoint, and monitor its status and metrics.
It requires you to create or update the AWS Glue Data Catalog entries for the S3 file, either manually or using a crawler. You need to use the Data Catalog as a metadata store for your Spark job, and specify the database and table names in your code.
Option B is incorrect because it suggests creating an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file, and running SQL queries from Amazon Athena to calculate the number of distinct customers. This solution has the following disadvantages:
It requires you to create and run a crawler, which is a program that connects to your data store, progresses through a prioritized list of classifiers to determine the schema for your data, and then creates metadata tables in the Data Catalog. You need to specify the data store, the IAM role, the schedule, and the output database for your crawler.
It requires you to write SQL queries, as Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. You need to use Athena to concatenate the columns that contain customer first names and last names, and then use the COUNT(DISTINCT) aggregate function to count the number of unique values in the resulting column.
Option C is incorrect because it suggests creating and running an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers. This solution has the following disadvantages:
It requires you to write code, as Amazon EMR Serverless is a service that allows you to run Apache Spark jobs on AWS without provisioning or managing any infrastructure. You need to use the Spark SQL or the Spark DataFrame API to read the S3 file and calculate the number of distinct customers.
It requires you to create and manage an Amazon EMR Serverless cluster, which is a fully managed and scalable Spark environment that runs on AWS Fargate. You need to specify the cluster name, the IAM role, the VPC, and the subnet for your cluster, and monitor its status and metrics.
It requires you to create or update the AWS Glue Data Catalog entries for the S3 file, either manually or using a crawler. You need to use the Data Catalog as a metadata store for your Spark job, and specify the database and table names in your code.
1: AWS Glue DataBrew - Features
2: Working with recipes - AWS Glue DataBrew
3: Working with data sources and data targets - AWS Glue DataBrew
[4]: AWS Glue notebooks - AWS Glue
[5]: Development endpoints - AWS Glue
[6]: Populating the AWS Glue Data Catalog - AWS Glue
[7]: Crawlers - AWS Glue
[8]: Amazon Athena - Features
[9]: Amazon EMR Serverless - Features
[10]: Creating an Amazon EMR Serverless cluster - Amazon EMR
[11]: Using the AWS Glue Data Catalog with Amazon EMR Serverless - Amazon EMR
A company has used an Amazon Redshift table that is named Orders for 6 months. The company performs weekly updates and deletes on the table. The table has an interleaved sort key on a column that contains AWS Regions.
The company wants to reclaim disk space so that the company will not run out of storage space. The company also wants to analyze the sort key column.
Which Amazon Redshift command will meet these requirements?
VACUUM FULL Orders
VACUUM DELETE ONLY Orders
VACUUM REINDEX Orders
VACUUM SORT ONLY Orders
Explanation:
Amazon Redshift is a fully managed, petabyte-scale data warehouse service that enables fast and cost-effective analysis of large volumes of data. Amazon Redshift uses columnar storage, compression, and zone maps to optimize the storage and performance of data. However, over time, as data is inserted, updated, or deleted, the physical storage of data can become fragmented, resulting in wasted disk space and degraded query performance.To address this issue, Amazon Redshift provides the VACUUM command, which reclaims disk space and resorts rows in either a specified table or all tables in the current schema1.
The VACUUM command has four options: FULL, DELETE ONLY, SORT ONLY, and REINDEX. The option that best meets the requirements of the question is VACUUM REINDEX, which re-sorts the rows in a table that has an interleaved sort key and rewrites the table to a new location on disk. An interleaved sort key is a type of sort key that gives equal weight to each column in the sort key, and stores the rows in a way that optimizes the performance of queries that filter by multiple columns in the sort key. However, as data is added or changed, the interleaved sort order can become skewed, resulting in suboptimal query performance. The VACUUM REINDEX option restores the optimal interleaved sort order and reclaims disk space by removing deleted rows.This option also analyzes the sort key column and updates the table statistics, which are used by the query optimizer to generate the most efficient query execution plan23.
The other options are not optimal for the following reasons:
A . VACUUM FULL Orders. This option reclaims disk space by removing deleted rows and resorts the entire table. However, this option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order. Moreover, this option is the most resource-intensive and time-consuming, as it rewrites the entire table to a new location on disk.
B . VACUUM DELETE ONLY Orders. This option reclaims disk space by removing deleted rows, but does not resort the table. This option is not suitable for tables that have any sort key, as it does not improve the query performance by restoring the sort order. Moreover, this option does not analyze the sort key column and update the table statistics.
D . VACUUM SORT ONLY Orders. This option resorts the entire table, but does not reclaim disk space by removing deleted rows. This option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order. Moreover, this option does not analyze the sort key column and update the table statistics.
1: Amazon Redshift VACUUM
2: Amazon Redshift Interleaved Sorting
3: Amazon Redshift ANALYZE
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