The AWS Certified Data Engineer – Associate (DEA-C01) certification is designed for professionals who build, deploy, and manage data pipelines and data engineering solutions on AWS. As organizations increasingly rely on data-driven insights, this certification is highly valuable. To effectively prepare for the DEA-C01 exam, incorporating DEA-C01 mock tests into your study strategy is essential. These practice exams are specifically structured to align with the DEA-C01 exam guide, covering critical domains like data ingestion and transformation; data store management; data operations and monitoring; and data security and governance.
Utilizing AWS Data Engineer Associate practice exams provides a realistic simulation of the actual test, helping you get accustomed to the question formats and the complexity of data engineering scenarios on AWS. You’ll be tested on your ability to use core AWS data services such as AWS Glue, Kinesis, S3, Redshift, EMR, Lake Formation, and DynamoDB to design and implement robust data solutions. These mock tests are invaluable for identifying your strengths and weaknesses, allowing you to focus your learning on specific services or data engineering concepts. Regularly attempting DEA-C01 practice questions will sharpen your skills in data modeling, ETL development, and data pipeline automation.
Beyond knowledge validation, these practice exams build your confidence and improve your time management for the actual exam. Familiarizing yourself with the types of problems and the depth of understanding required will reduce exam-day stress. A strong AWS DEA-C01 preparation plan involves not just learning about AWS data services but also understanding how to integrate them into efficient and secure data workflows. Start leveraging DEA-C01 mock tests today to refine your data engineering expertise and significantly increase your likelihood of passing the AWS Certified Data Engineer – Associate exam.
Understanding the AWS Cloud is a valuable asset in today’s tech landscape. For detailed information about the certification, you can always refer to the official AAWS Certified Data Engineer – Associate (DEA-C01) page.
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This is a timed quiz. You will be given 10800 seconds to answer all questions. Are you ready?
Which S3 feature allows you to define rules to automatically transition objects to different storage classes or delete them after a specified period to manage costs and data retention?
S3 Lifecycle configuration enables you to define rules to manage your objects' lifecycle. You can transition objects to other storage classes (e.g., S3 Standard-IA, S3 Glacier) or expire (delete) objects after a certain time.
A data engineer needs to transfer 100 TB of data from an on-premises data center to Amazon S3. The internet connection is slow and unreliable. Which AWS service provides a physical appliance for this type of large-scale offline data transfer?
AWS Snowball is a petabyte-scale data transport solution that uses secure appliances to transfer large amounts of data into and out of the AWS Cloud. It's ideal for situations with limited network bandwidth.
A data engineer needs to transform JSON data into a columnar format like Apache Parquet for efficient analytical querying. Which statement is TRUE regarding this transformation?
Columnar formats like Parquet are optimized for analytical queries because they allow query engines to read only the necessary columns, reducing I/O and improving performance compared to row-based formats like JSON for analytical workloads.
Which AWS Glue component is responsible for discovering the schema of your data and creating metadata tables in the AWS Glue Data Catalog?
AWS Glue crawlers connect to your data store, progress through a prioritized list of classifiers to determine the schema for your data, and then create metadata tables in your Data Catalog.
What is the purpose of AWS Glue triggers?
AWS Glue triggers can start ETL jobs based on a schedule or an event. This allows for the automation and orchestration of data pipelines.
Which AWS service provides a way to query data directly in Amazon S3 using standard SQL, without needing to load the data into a database or data warehouse?
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
When designing a DynamoDB table, what is the significance of choosing an appropriate partition key?
The partition key determines how data is distributed across partitions in DynamoDB. A well-chosen partition key distributes data evenly, preventing hot spots and ensuring scalable performance.
A data engineer needs to process a large dataset using a custom MapReduce application. Which AWS service provides a managed Hadoop framework for this purpose?
Amazon EMR provides a managed Hadoop framework that makes it easy, fast, and cost-effective to process vast amounts of data across dynamically scalable Amazon EC2 instances. You can also run other popular distributed frameworks such as Apache Spark, HBase, Presto, and Flink in EMR.
What is 'idempotency' in the context of data pipeline operations, and why is it important?
An idempotent operation is one that can be applied multiple times without changing the result beyond the initial application. In data pipelines, idempotency is important for retry mechanisms, ensuring that re-running a failed step doesn't lead to duplicate data or incorrect state.
Which AWS service provides a fully managed, petabyte-scale data warehouse service that allows you to run complex analytical queries?
Amazon Redshift is a fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools.
A data pipeline processes sensitive data. How can a data engineer ensure that intermediate data stored in Amazon S3 during ETL processing is protected?
Using server-side encryption (e.g., SSE-S3 or SSE-KMS) for S3 buckets where intermediate data is stored ensures that this data is encrypted at rest. Additionally, using IAM roles with least privilege access for ETL jobs is crucial.
A data engineer needs to ensure that data stored in an Amazon S3 data lake is encrypted at rest. Which S3 encryption option provides server-side encryption with keys managed by AWS KMS, allowing for centralized key management and auditing?
Server-Side Encryption with AWS Key Management Service (SSE-KMS) allows S3 to encrypt objects using keys managed in AWS KMS. This provides an auditable trail of key usage and allows for customer-managed keys (CMKs) or AWS-managed CMKs.
A data engineer needs to ingest streaming data from thousands of IoT devices into Amazon S3 for batch processing. The data arrives at a high velocity and volume. Which AWS service is MOST suitable for capturing and loading this streaming data into S3?
Amazon Kinesis Data Firehose is a fully managed service for delivering real-time streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk. It can capture, transform, and load streaming data.
What is a common challenge when operating data pipelines that a data engineer must address?
Data pipelines can fail due to various reasons (source data issues, code bugs, resource limits). Implementing robust error handling, retry mechanisms, and monitoring/alerting is crucial for operational stability.
A data engineer is choosing a file format for storing large datasets in Amazon S3 that will be queried by Amazon Athena. To optimize query performance and reduce costs, which type of file format is generally recommended?
Columnar file formats like Apache Parquet and Apache ORC are highly recommended for analytical querying with services like Athena because they allow the query engine to read only the necessary columns, reducing the amount of data scanned and improving performance.
A data engineer needs to monitor the number of objects and total storage size in an Amazon S3 bucket. Which AWS service provides these metrics?
Amazon CloudWatch provides metrics for S3 buckets, including `NumberOfObjects` and `BucketSizeBytes`. S3 Storage Lens also provides advanced visibility.
What is a 'data lake' on AWS typically built upon?
Amazon S3 is often the central storage repository for data lakes on AWS due to its scalability, durability, availability, and cost-effectiveness. Data can be stored in various formats and processed by different analytics services.
Which AWS service can be used to discover, classify, and protect sensitive data like PII stored in Amazon S3 buckets using machine learning?
Amazon Macie is a data security and data privacy service that uses machine learning (ML) and pattern matching to discover and help you protect your sensitive data in Amazon S3.
A company receives daily CSV files in an S3 bucket. A data engineer needs to transform this data (e.g., change data types, filter rows) and store the processed data in Parquet format in another S3 bucket for querying with Amazon Athena. Which AWS service is best suited for this ETL (Extract, Transform, Load) process?
AWS Glue is a fully managed ETL service that makes it easy to prepare and load your data for analytics. You can create and run ETL jobs with a few clicks in the AWS Management Console. AWS Glue can automatically discover your data, determine the schema, and generate ETL scripts.
A company needs to store frequently accessed, small (less than 1MB) JSON documents and requires fast, consistent read and write performance with microsecond latency for a caching layer. Which AWS service is MOST suitable?
Amazon ElastiCache for Redis is an in-memory data store that can be used as a database, cache, and message broker. It provides sub-millisecond latency and is excellent for caching frequently accessed data.
What is the purpose of sort keys in Amazon Redshift?
Sort keys in Redshift determine the order in which rows in a table are physically stored on disk. Query performance can be improved by choosing appropriate sort keys, as the query optimizer can then skip scanning large blocks of data that don't match the query predicates.
A data pipeline ingests data into Amazon S3. Downstream analytics jobs require the data to be available with strong read-after-write consistency. Which S3 consistency model applies to new object PUTS?
Amazon S3 provides strong read-after-write consistency for PUTS of new objects in your S3 bucket in all AWS Regions. After a successful write of a new object, any subsequent read request immediately receives the latest version of the object.
A data engineer needs to design a data model for a new application that requires flexible schema and will store item data with varying attributes. Which type of AWS database service is MOST suitable?
NoSQL databases, like Amazon DynamoDB (key-value and document) or Amazon DocumentDB (document), are well-suited for applications requiring flexible schemas where attributes can vary between items.
What is 'schema evolution' in the context of data pipelines and data lakes?
Schema evolution refers to the ability of a data storage system or data processing pipeline to handle changes in the structure (schema) of the data over time without breaking existing processes or queries.
A data engineer needs to ensure that data being transferred between an on-premises data center and Amazon S3 over a VPN connection is encrypted. What type of encryption addresses this requirement?
Encryption in transit protects data as it travels between locations. For VPN connections, protocols like IPsec are used to encrypt the data packets.
Which AWS service allows you to build and run Apache Spark, Hive, Presto, and other big data frameworks on a managed cluster?
Amazon EMR (Elastic MapReduce) is a cloud big data platform for running large-scale distributed data processing jobs, interactive SQL queries, and machine learning applications using open-source analytics frameworks such as Apache Spark, Apache Hive, and Presto.
A data engineer needs to manage the schema and versions of tables in their data lake stored on Amazon S3, making it discoverable by query services like Amazon Athena and Amazon Redshift Spectrum. Which AWS service should be used?
The AWS Glue Data Catalog serves as a central metadata repository. It can be populated by Glue crawlers or manually, and services like Athena, Redshift Spectrum, and EMR use it to understand the schema and location of data in S3.
Which AWS service can be used to manage and rotate database credentials, API keys, and other secrets used by applications and data pipelines?
AWS Secrets Manager helps you protect secrets needed to access your applications, services, and IT resources. The service enables you to easily rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle.
When using AWS Glue to perform ETL, what is a 'job bookmark' used for?
AWS Glue job bookmarks help AWS Glue maintain state information from your job runs and prevent the reprocessing of old data. With job bookmarks, you can process new data when it arrives in S3.
A data engineer needs to monitor the progress and status of an AWS Glue ETL job. Where can this information be found?
The AWS Glue console provides a dashboard to monitor job runs, view logs (which are typically sent to CloudWatch Logs), and see metrics related to job execution.
What is a common method for ensuring data quality in a data pipeline?
Implementing data validation checks at various stages of the pipeline (e.g., checking for null values, correct data types, valid ranges) is a common method to ensure data quality. Services like AWS Glue DataBrew or custom scripts in ETL jobs can perform these checks.
A data engineer needs to manage the lifecycle of objects in an S3 bucket, automatically transitioning them to lower-cost storage classes or deleting them after a certain period. Which S3 feature should be used?
S3 Lifecycle policies enable you to define rules to automatically transition objects to other S3 storage classes or expire (delete) objects after a specified period.
A data engineer needs to combine data from a relational database in Amazon RDS with log data from Amazon S3 for analysis. Which AWS service can be used to create an ETL job that joins these disparate data sources?
AWS Glue can connect to various data sources, including Amazon RDS and Amazon S3. An AWS Glue ETL job can be authored to read data from both, perform join and transformation operations, and write the results to a target data store.
A data engineer is configuring access for an AWS Glue ETL job to read data from an S3 bucket and write to an Amazon Redshift cluster. What is the recommended security practice for granting these permissions?
Creating an IAM role with the specific, least-privilege permissions required by the Glue job (e.g., `s3:GetObject` for the source bucket, `redshift:CopyCommand` for the target cluster) and assigning this role to the Glue job is the best practice.
When transforming data using AWS Glue, what is a 'DynamicFrame'?
A DynamicFrame is similar to an Apache Spark DataFrame, except that each record is self-describing, so no schema is required initially. DynamicFrames provide schema flexibility and support for data types that may not be present in all records.
What is 'data cleansing' or 'data scrubbing' in an ETL process?
Data cleansing is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.
Which AWS service provides managed Apache Airflow environments for orchestrating complex data workflows?
Amazon Managed Workflows for Apache Airflow (MWAA) is a managed orchestration service for Apache Airflow that makes it easier to set up and operate end-to-end data pipelines in the cloud at scale.
A data engineer needs to migrate a 50 TB on-premises Oracle database to Amazon Aurora PostgreSQL with minimal downtime. Which AWS service is specifically designed for heterogeneous database migrations like this?
AWS Database Migration Service (DMS) helps you migrate databases to AWS quickly and securely. It supports homogeneous migrations as well as heterogeneous migrations between different database platforms, such as Oracle to Amazon Aurora.
When managing a data pipeline, what is the benefit of using version control (e.g., Git with AWS CodeCommit) for ETL scripts and infrastructure-as-code templates?
Version control allows tracking changes, collaboration among team members, rollback to previous versions, and maintaining a history of modifications, which is crucial for managing data pipelines effectively.
Which Amazon S3 storage class is designed for data that is accessed less frequently but requires rapid access when needed, offering lower storage costs than S3 Standard?
Amazon S3 Standard-Infrequent Access (S3 Standard-IA) is for data that is accessed less frequently but requires rapid access when needed. S3 Standard-IA offers the high durability, high throughput, and low latency of S3 Standard, with a low per GB storage price and per GB retrieval fee.
A data engineer needs to audit all API calls made to their Amazon Redshift cluster, including login attempts and queries executed. Which AWS service should be configured to capture this information?
AWS CloudTrail captures AWS API calls as events. For Redshift, you can enable audit logging which sends logs to S3, and CloudTrail can capture management API calls related to the Redshift cluster itself.
What is a 'distribution key' in Amazon Redshift used for?
The distribution key for a table determines how its data is distributed across the compute nodes in a Redshift cluster. Choosing an appropriate distribution key is crucial for query performance by minimizing data movement between nodes.
Which AWS service is commonly used to orchestrate complex ETL workflows that involve multiple AWS Glue jobs, AWS Lambda functions, and other AWS services?
AWS Step Functions lets you coordinate multiple AWS services into serverless workflows. You can design and run workflows that stitch together services such as AWS Glue, AWS Lambda, Amazon SQS, and more, making it ideal for orchestrating complex ETL pipelines.
Which AWS service helps you centrally manage permissions and fine-grained access control for your data lake stored in Amazon S3, integrating with services like AWS Glue, Amazon Athena, and Amazon Redshift Spectrum?
AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. It helps you collect and catalog data from databases and object storage, move the data into your new S3 data lake, clean and classify data using machine learning algorithms, and secure access to your sensitive data with fine-grained controls.
A data engineer needs to ensure that an AWS Glue ETL job only processes new files added to an S3 bucket since the last job run. Which AWS Glue feature should be utilized?
AWS Glue job bookmarks track data that has already been processed during a previous run of an ETL job by persisting state information from the job run. This prevents reprocessing of old data and allows jobs to process only new data when run again.
When using Kinesis Data Firehose to deliver data to Amazon S3, what feature allows you to batch, compress, and encrypt the data before it is stored in S3?
Kinesis Data Firehose can batch records together to increase S3 PUT efficiency, compress data (e.g., GZIP, Snappy) to save storage space, and encrypt data using AWS KMS before writing it to S3.
A data engineer needs to ingest data from hundreds of application log files generated on EC2 instances into Amazon Kinesis Data Streams. Which agent can be installed on the EC2 instances to achieve this?
The Amazon Kinesis Agent is a stand-alone Java software application that offers an easy way to collect and send data to Kinesis Data Streams. The agent continuously monitors a set of files and sends new data to your stream.
A company needs to ensure that all data written to a specific S3 bucket is encrypted using SSE-KMS with a specific customer-managed key (CMK). How can a data engineer enforce this?
A bucket policy can be configured to deny any S3 PUT object requests that do not include the `x-amz-server-side-encryption` header specifying `aws:kms` and the correct KMS key ARN.
A company wants to capture changes from a relational database (Change Data Capture - CDC) and stream these changes to other data stores or analytics services in near real-time. Which AWS service is commonly used for CDC in migrations and ongoing replication?
AWS Database Migration Service (DMS) can be used for ongoing replication with CDC, capturing changes from a source database and applying them to a target. This allows for near real-time data synchronization.
A data engineer needs to automate a daily ETL job that runs an AWS Glue script. Which AWS service can be used to schedule this job?
AWS Glue triggers can be scheduled (cron-like expressions) or event-driven (e.g., S3 PUT event). Amazon EventBridge can also be used to schedule Glue jobs and orchestrate more complex workflows.
What is a key characteristic of Amazon DynamoDB that makes it suitable for applications requiring high availability and scalability?
DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. It automatically spreads data and traffic for your tables over a sufficient number of servers to handle your throughput and storage requirements, while maintaining consistent, low-latency performance.
What is 'data masking' in the context of data security and governance?
Data masking is a data security technique that creates a structurally similar but inauthentic version of an organization's data. This can be used for purposes like software testing and user training, where real sensitive data is not required.
A data engineer is designing a system to store application state for a highly available web application. The data requires fast key-based lookups and must be durable. Which AWS service is a good fit?
Amazon DynamoDB is a fully managed NoSQL database that provides fast, predictable performance with seamless scalability and durability. It's well-suited for key-value lookups for application state.
A data engineer is using AWS DMS to migrate an on-premises MySQL database to Amazon RDS for MySQL. What is the role of the 'replication instance' in AWS DMS?
The replication instance is an EC2 instance that AWS DMS provisions to perform the actual data migration tasks. It connects to the source and target data stores, reads data from the source, formats it for the target, and loads it into the target.
A data engineer needs to troubleshoot a failed Amazon EMR job. Which EMR feature provides detailed logs about the steps and tasks within the job?
Amazon EMR logs various details about the cluster and job execution, including step logs, task logs, and Hadoop/Spark logs. These logs are typically stored in Amazon S3 and can be accessed via the EMR console or directly from S3 for troubleshooting.
What is 'data partitioning' in the context of storing data in Amazon S3 for analytics, and why is it beneficial?
Partitioning data in S3 (e.g., by year, month, day) organizes data into separate directories. Query engines like Amazon Athena and Amazon Redshift Spectrum can use these partitions to prune data, scanning only relevant partitions, which improves query performance and reduces costs.
A data engineer needs to ensure that only authorized users and services can access an AWS Glue Data Catalog and the underlying data in Amazon S3. Which AWS service is primarily used to define and manage these permissions?
AWS Identity and Access Management (IAM) is used to manage access to AWS services and resources securely. You create IAM roles and policies to grant permissions to users, groups, and services (like AWS Glue) to access resources like the Data Catalog and S3 buckets.
What is the primary purpose of Amazon Redshift Spectrum?
Amazon Redshift Spectrum allows you to run SQL queries against exabytes of data in Amazon S3 without having to load or transform the data. It extends the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 data lake.
A data engineer is using Amazon AppFlow to transfer data from Salesforce to Amazon S3. What is a key benefit of using Amazon AppFlow for this type of integration?
Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications like Salesforce, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift, in just a few clicks.
When designing a data lake on Amazon S3, what is a common best practice for organizing data to optimize query performance with services like Amazon Athena?
Partitioning data (e.g., by date) and using columnar file formats (e.g., Apache Parquet or ORC) are key best practices for optimizing query performance and cost with Athena and other S3 query engines.
A data engineer is designing a solution to ingest data from an on-premises file server to Amazon S3. The files are updated frequently, and the transfer needs to be automated and efficient over a WAN connection. Which AWS service is MOST suitable for this ongoing synchronization?
AWS DataSync is an online data transfer service that simplifies, automates, and accelerates moving data between on-premises storage systems and AWS Storage services, as well as between AWS Storage services.
When designing a data ingestion pipeline for real-time clickstream data from a website, which characteristic of Amazon Kinesis Data Streams makes it suitable for this use case?
Kinesis Data Streams is designed for real-time data ingestion and processing. It allows for multiple consumers to process the data concurrently and provides ordered, replayable records.
What is the purpose of S3 Object Lock?
S3 Object Lock enables you to store objects using a write-once-read-many (WORM) model. It can help you prevent objects from being deleted or overwritten for a fixed amount of time or indefinitely, which is useful for compliance and data retention requirements.
When using AWS Lake Formation to secure a data lake, what is a 'data filter' used for?
In Lake Formation, data filters allow you to implement column-level, row-level, and cell-level security by defining filter conditions that restrict access to specific portions of data in your data lake tables for different principals.
What is the primary purpose of the AWS Glue Data Catalog?
The AWS Glue Data Catalog is a central metadata repository for all your data assets, regardless of where they are located. It contains references to data that is used as sources and targets of your ETL jobs in AWS Glue.
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