| Exam Code | Data-Engineer-Associate |
| Exam Name | AWS Certified Data Engineer - Associate (DEA-C01) |
| Questions | 80 |
| Update Date | November 10,2024 |
| Price |
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The AWS Certified Data Analytics – Specialty certification, also known as the DAS-C01 Exam, is designed for individuals with experience and expertise working with AWS services to design, build, secure, and maintain analytics solutions. This guide provides a detailed overview of the exam, preparation strategies, and essential resources to help you succeed.
The AWS Certified Data Analytics – Specialty exam, coded DAS-C01, is a specialty-level certification lasting 170 minutes. It evaluates proficiency in operational aspects of data analytics solutions on AWS. The exam format includes multiple-choice and multiple-response questions, and it can be taken either at a testing center or online via a proctored exam. With a cost of USD 300, subject to change, candidates must ensure they have a deep understanding of AWS data analytics services and their operational characteristics. Success in this exam validates expertise in designing, building, securing, and maintaining analytics solutions on the AWS platform.
The exam validates the following abilities:
Determine the operational characteristics of the collection system. This involves understanding how to ingest various types of data from multiple sources efficiently.
Determine the operational characteristics of the storage and data management system. This includes choosing appropriate storage solutions and managing data effectively.
Determine the operational characteristics of the data processing solutions. Candidates must know how to process data using AWS services, ensuring data is transformed and loaded correctly.
Determine the operational characteristics of the analysis and visualization solutions. This focuses on analyzing data to extract insights and visualizing those insights using tools such as Amazon QuickSight.
Determine how to secure the analytics solutions. This involves implementing best practices for data security and ensuring compliance with various standards.
To illustrate, let’s consider a scenario where a company needs to process and analyze large volumes of data in real time for business insights. Here's how key AWS services might be used:
AWS Glue:
To extract, transform, and load data from various sources. For example, a retail company might use AWS Glue to gather sales data from different regions, clean it, and load it into a central repository.
Amazon S3:
To store raw and processed data. Continuing the retail example, all raw sales data and the processed analytics results can be stored in S3 for scalability and durability.
Amazon Redshift:
To perform complex queries and analytics. The processed data in S3 can be transferred to Amazon Redshift for performing detailed analytics to understand sales trends.
Amazon QuickSight:
To visualize the data and generate reports. Visual dashboards can be created for management to see real-time sales performance and make informed decisions.
AWS Lambda:
To automate ETL processes and run code in response to data events. Lambda functions can trigger data processing pipelines when new data arrives in S3.
Amazon Kinesis:
To ingest and process streaming data in real-time. For instance, capturing and processing real-time sales data as transactions happen.
The exam focuses on several AWS services critical for data analytics:
AWS Glue:
A fully managed ETL (extract, transform, load) service that simplifies and automates the process of discovering, preparing, and combining data for analytics.
Amazon S3:
Scalable object storage service designed for high durability, security, and availability of data. Ideal for storing large amounts of data at a low cost.
Amazon Redshift:
A fully managed data warehouse service that makes it simple and cost-effective to analyze all your data using standard SQL and existing business intelligence tools.
Amazon RDS:
Managed relational database service that makes it easy to set up, operate, and scale a relational database in the cloud. Supports several database engines including MySQL, PostgreSQL, and Oracle.
Amazon DynamoDB:
Managed NoSQL database service designed for fast and predictable performance with seamless scalability.
Amazon Kinesis:
A platform for streaming data on AWS, enabling you to build applications that continuously collect and process large streams of data records in real time.
AWS Lambda:
Serverless compute service that runs code in response to events and automatically manages the compute resources required.
Amazon QuickSight:
A business analytics service that enables you to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data.
At least 5 years of experience with data analytics technologies.
At least 2 years of hands-on experience working with AWS.
Expertise in designing, building, securing, and maintaining analytics solutions using AWS services.
The AWS Certified Data Analytics – Specialty certification is aimed at professionals who can demonstrate a deep understanding of how to use AWS services for data analytics purposes.
Study Guide and Exam Blueprint:
AWS provides a detailed exam guide and blueprint, which outlines the topics covered and the weightage of each domain. Access it here.
Training and Courses:
AWS Training: Instructor-led and digital training options are available. Explore AWS training.
AWS Certified Data Analytics - Specialty Exam Readiness: A specific course aimed at helping candidates prepare for the exam. Enroll here.
Practice Exams:
Take practice exams to familiarize yourself with the question format and identify areas where you need further study. Try the AWS Certified Data Analytics – Specialty Practice Exam.
Whitepapers and Documentation:
AWS publishes whitepapers and documentation that cover best practices, which are highly beneficial for exam preparation. Key whitepapers include:
AWS Big Data Analytics Options
Data Lake Formation
Hands-on Labs:
Practical experience is crucial. Use AWS Free Tier and other AWS services to gain hands-on experience. Try AWS's hands-on labs.
Understand the Exam Domains:
Focus on each domain's percentage weight to prioritize your study efforts. For example, if data processing has a higher weight, spend more time mastering AWS Glue and Kinesis.
Hands-on Experience:
Use AWS services extensively to gain practical knowledge. Create projects that utilize multiple AWS services to build a comprehensive understanding.
Review AWS Whitepapers:
These are crucial for understanding best practices and architectural patterns. Focus on whitepapers related to data lakes, big data, and analytics on AWS.
Join Study Groups:
Engage with the community through forums or study groups to exchange knowledge and resources. Join platforms like Reddit AWS Certification or AWS Developer Forums.
Time Management:
Practice managing your time during the exam to ensure you can complete all questions. Use timed practice exams to build this skill.
Interactive Content:
Take advantage of interactive elements such as quizzes, practice questions, and interactive study guides available on various educational platforms.
Community Engagement:
Join online communities and forums to discuss topics, share resources, and get support from peers and professionals who have already taken the exam.
Call-to-Actions (CTAs):
Sign up for our newsletter to receive the latest updates on AWS certifications and study tips.
Enroll in our AWS Certified Data Analytics – Specialty training course today!
Download our free study guide to kickstart your exam preparation.
Diagrams and Infographics:
Create diagrams showing how AWS services like Glue, S3, Redshift, and QuickSight interact in a data pipeline.
Infographics that break down the exam domains, weights, and key services.
Screenshots:
Include screenshots of AWS Management Console steps for setting up services like Glue jobs or Redshift clusters.
Data Collection:
Use Amazon Kinesis to collect streaming data from e-commerce transactions in real time.
Example: Configure a Kinesis data stream to capture transaction data as it happens.
Data Storage:
Store the raw streaming data in Amazon S3.
Example: Set up an S3 bucket with appropriate permissions and lifecycle policies to manage data storage efficiently.
Data Processing:
Use AWS Glue to transform the raw data into a structured format suitable for analysis.
Example: Create a Glue job to clean and enrich transaction data, such as adding customer details from another data source.
Data Analysis:
Load the processed data into Amazon Redshift for querying.
Example: Set up Redshift and configure ETL jobs to load data from S3 into Redshift tables.
Data Visualization:
Use Amazon QuickSight to create dashboards and reports based on the processed data in Redshift.
Example: Design interactive dashboards in QuickSight to visualize sales trends, customer behavior, and inventory levels.
Security:
Implement security best practices, including encryption, IAM roles, and access control.
Example: Use AWS Key Management Service (KMS) to encrypt data at rest in S3 and Redshift. Configure IAM roles to restrict access to the data based on user roles.
Blogs and Articles:
Follow AWS Big Data Blog for updates and insights on data analytics services.
Books:
"AWS Certified Big Data Specialty Study Guide: Specialty (DAS-C01) Exam" by Asif Abbasi: A comprehensive resource for the certification exam.
Online Courses:
Courses on platforms like Udemy, Coursera, and A Cloud Guru specifically tailored for AWS Certified Data Analytics – Specialty.
Webinars and Workshops:
Participate in AWS webinars and hands-on workshops to stay updated with the latest features and best practices.
By following these guidelines and using the provided resources, you can prepare effectively for the AWS Certified Data Analytics – Specialty exam and achieve certification. This certification will validate your skills and potentially enhance your professional credentials in the field of data analytics. Good luck with your preparation!
Question 1
A data engineer needs Amazon Athena queries to finish faster. The data engineer noticesthat all the files the Athena queries use are currently stored in uncompressed .csv format.The data engineer also notices that users perform most queries by selecting a specificcolumn.Which solution will MOST speed up the Athena query performance?
A. Change the data format from .csvto JSON format. Apply Snappy compression.
B. Compress the .csv files by using Snappy compression.
C. Change the data format from .csvto Apache Parquet. Apply Snappy compression.
D. Compress the .csv files by using gzjg compression.
Question 2
A company stores data in a data lake that is in Amazon S3. Some data that the company stores in the data lake contains personally identifiable information (PII). Multiple usergroups need to access the raw data. The company must ensure that user groups canaccess only the PII that they require.Which solution will meet these requirements with the LEAST effort?
A. Use Amazon Athena to query the data. Set up AWS Lake Formation and create datafilters to establish levels of access for the company's IAM roles. Assign each user to theIAM role that matches the user's PII access requirements.
B. Use Amazon QuickSight to access the data. Use column-level security features inQuickSight to limit the PII that users can retrieve from Amazon S3 by using AmazonAthena. Define QuickSight access levels based on the PII access requirements of theusers.
C. Build a custom query builder UI that will run Athena queries in the background to accessthe data. Create user groups in Amazon Cognito. Assign access levels to the user groupsbased on the PII access requirements of the users.
D. Create IAM roles that have different levels of granular access. Assign the IAM roles toIAM user groups. Use an identity-based policy to assign access levels to user groups at thecolumn level.
Question 3
A company receives call logs as Amazon S3 objects that contain sensitive customerinformation. The company must protect the S3 objects by using encryption. The companymust also use encryption keys that only specific employees can access.Which solution will meet these requirements with the LEAST effort?
A. Use an AWS CloudHSM cluster to store the encryption keys. Configure the process thatwrites to Amazon S3 to make calls to CloudHSM to encrypt and decrypt the objects.Deploy an IAM policy that restricts access to the CloudHSM cluster.
B. Use server-side encryption with customer-provided keys (SSE-C) to encrypt the objectsthat contain customer information. Restrict access to the keys that encrypt the objects.
C. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects thatcontain customer information. Configure an IAM policy that restricts access to the KMSkeys that encrypt the objects.
D. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt theobjects that contain customer information. Configure an IAM policy that restricts access tothe Amazon S3 managed keys that encrypt the objects.
Question 4
A data engineer needs to maintain a central metadata repository that users access throughAmazon EMR and Amazon Athena queries. The repository needs to provide the schemaand properties of many tables. Some of the metadata is stored in Apache Hive. The dataengineer needs to import the metadata from Hive into the central metadata repository.Which solution will meet these requirements with the LEAST development effort?
A. Use Amazon EMR and Apache Ranger.
B. Use a Hive metastore on an EMR cluster.
C. Use the AWS Glue Data Catalog.
D. Use a metastore on an Amazon RDS for MySQL DB instance.
Question 5
A company is planning to use a provisioned Amazon EMR cluster that runs Apache Sparkjobs to perform big data analysis. The company requires high reliability. A big data teammust follow best practices for running cost-optimized and long-running workloads onAmazon EMR. The team must find a solution that will maintain the company's current levelof 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.
Question 6
A company wants to implement real-time analytics capabilities. The company wants to useAmazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming dataat the rate of several gigabytes per second. The company wants to derive near real-timeinsights by using existing business intelligence (BI) and analytics tools.Which solution will meet these requirements with the LEAST operational overhead?
A. Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command toload data from Amazon S3 directly into Amazon Redshift to make the data immediatelyavailable for real-time analysis.
B. Access the data from Kinesis Data Streams by using SQL queries. Create materializedviews directly on top of the stream. Refresh the materialized views regularly to query themost recent stream data.
C. Create an external schema in Amazon Redshift to map the data from Kinesis DataStreams to an Amazon Redshift object. Create a materialized view to read data from thestream. Set the materialized view to auto refresh.
D. Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis DataFirehose to stage the data in Amazon S3. Use the COPY command to load the data fromAmazon S3 to a table in Amazon Redshift.
Question 7
A company stores details about transactions in an Amazon S3 bucket. The company wantsto log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.Which solution will meet this requirement with the LEAST operational effort?
A. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket toinvoke an AWS Lambda function. Program the Lambda function to write the event toAmazon Kinesis Data Firehose. Configure Kinesis Data Firehose to write the event to thelogs S3 bucket.
B. Create a trail of management events in AWS CloudTraiL. Configure the trail to receivedata from the transactions S3 bucket. Specify an empty prefix and write-only events.Specify the logs S3 bucket as the destination bucket.
C. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket toinvoke an AWS Lambda function. Program the Lambda function to write the events to thelogs S3 bucket.
D. Create a trail of data events in AWS CloudTraiL. Configure the trail to receive data fromthe transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logsS3 bucket as the destination bucket.
Question 8
A data engineer has a one-time task to read data from objects that are in Apache Parquetformat in an Amazon S3 bucket. The data engineer needs to query only one column of thedata.Which solution will meet these requirements with the LEAST operational overhead?
A. Confiqure an AWS Lambda function to load data from the S3 bucket into a pandasdataframe- Write a SQL SELECT statement on the dataframe to query the requiredcolumn.
B. Use S3 Select to write a SQL SELECT statement to retrieve the required column fromthe S3 objects.
C. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.
D. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in AmazonAthena to query the required column.
Question 9
A retail company has a customer data hub in an Amazon S3 bucket. Employees from manycountries use the data hub to support company-wide analytics. A governance team mustensure that the company's data analysts can access data only for customers who arewithin the same country as the analysts.Which solution will meet these requirements with the LEAST operational effort?
A. Create a separate table for each country's customer data. Provide access to eachanalyst based on the country that the analyst serves.
B. Register the S3 bucket as a data lake location in AWS Lake Formation. Use the LakeFormation row-level security features to enforce the company's access policies.
C. Move the data to AWS Regions that are close to the countries where the customers are.Provide access to each analyst based on the country that the analyst serves.
D. Load the data into Amazon Redshift. Create a view for each country. Create separate1AM roles for each country to provide access to data from each country. Assign theappropriate roles to the analysts.
Question 10
A company uses Amazon RDS to store transactional data. The company runs an RDS DBinstance in a private subnet. A developer wrote an AWS Lambda function with defaultsettings to insert, update, or delete data in the DB instance.The developer needs to give the Lambda function the ability to connect to the DB instanceprivately without using the public internet.Which combination of steps will meet this requirement with the LEAST operationaloverhead? (Choose two.)
A. Turn on the public access setting for the DB instance.
B. Update the security group of the DB instance to allow only Lambda function invocationson the database port.
C. Configure the Lambda function to run in the same subnet that the DB instance uses.
D. Attach the same security group to the Lambda function and the DB instance. Include aself-referencing rule that allows access through the database port.
E. Update the network ACL of the private subnet to include a self-referencing rule thatallows access through the database port.
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Amazon Data-Engineer-Associate Exam Sample Questions