Visit here for our full Amazon AWS Certified Data Engineer – Associate DEA-C01 exam dumps and practice test questions.
Question 1
A data engineer needs to design a solution to ingest streaming data from IoT devices into Amazon S3. The solution must process data in real-time and store it in a partitioned format for efficient querying. Which AWS service combination would be most appropriate?
A) Amazon Kinesis Data Streams with AWS Lambda and Amazon S3
B) Amazon SQS with Amazon EC2 and Amazon S3
C) AWS DataSync with Amazon RDS and Amazon S3
D) Amazon MQ with AWS Glue and Amazon S3
Answer: A
Explanation:
Amazon Kinesis Data Streams is specifically designed for real-time streaming data ingestion from multiple sources including IoT devices. It can handle high throughput and provides the ability to process records in real-time. When combined with AWS Lambda, you can process streaming data as it arrives, transform it, and write it to Amazon S3 in a partitioned format.
AWS Lambda functions can be triggered by Kinesis Data Streams to process each batch of records. Within the Lambda function, you can implement logic to partition data based on date, device ID, or other relevant criteria before writing to S3. This approach provides a serverless, scalable solution that automatically handles the complexity of stream processing.
Amazon SQS is a message queuing service but is not optimized for streaming data ingestion at the scale typically required for IoT applications. While it can work with EC2 instances, this approach requires more infrastructure management and is less efficient for real-time processing compared to Kinesis.
AWS DataSync is designed for data transfer and migration between on-premises storage and AWS, not for real-time streaming data ingestion. Amazon RDS is a relational database service and would not be the appropriate choice for storing large volumes of streaming IoT data that needs to be queried efficiently.
Amazon MQ is a managed message broker service for Apache ActiveMQ and RabbitMQ, which is more suited for application integration rather than high-throughput IoT data streaming. The combination of Kinesis Data Streams, Lambda, and S3 provides the most efficient and scalable architecture for this use case.
Question 2
A company stores data in Amazon S3 and needs to ensure that data is automatically transitioned to cheaper storage classes over time. Data accessed frequently in the first 30 days should be in Standard storage, data accessed occasionally in the next 60 days should move to Infrequent Access, and data older than 90 days should be archived. What is the best solution?
A) Manually move objects between storage classes using AWS CLI
B) Configure S3 Lifecycle policies to automatically transition objects
C) Use AWS Lambda to monitor object age and move objects
D) Enable S3 Versioning and delete old versions manually
Answer: B
Explanation:
S3 Lifecycle policies provide an automated, cost-effective way to manage object storage classes throughout their lifecycle. These policies allow you to define rules that automatically transition objects between different storage classes based on age or other criteria. This eliminates the need for manual intervention and ensures consistent application of storage optimization strategies.
With Lifecycle policies, you can create rules that specify transition actions. For this scenario, you would create a policy that keeps objects in S3 Standard for 30 days, then transitions them to S3 Standard-IA after 30 days, and finally moves them to S3 Glacier or S3 Glacier Deep Archive after 90 days. These transitions happen automatically without any manual effort.
Manually moving objects using AWS CLI would be time-consuming, error-prone, and not scalable. As your data volume grows, manual management becomes impractical and increases operational overhead. This approach also requires constant monitoring and scheduled tasks to check object ages.
Using AWS Lambda to monitor object age and move objects would work but introduces unnecessary complexity and cost. You would need to maintain Lambda functions, handle errors, and ensure the functions run reliably. This approach also incurs Lambda execution costs that could be avoided with native S3 Lifecycle policies.
S3 Versioning is a feature for maintaining multiple versions of objects for data protection and recovery purposes, not for managing storage class transitions. While versioning can be combined with Lifecycle policies, enabling versioning alone does not address the requirement of automatic storage class transitions.
Question 3
A data engineer needs to process large CSV files stored in Amazon S3 and load the data into Amazon Redshift for analytics. The files contain approximately 10 million rows each. What is the most efficient method to load this data?
A) Use INSERT statements from an application
B) Use the COPY command from Amazon S3
C) Read data with AWS Lambda and insert row by row
D) Export to Amazon RDS first, then to Redshift
Answer: B
Explanation:
The COPY command is the most efficient and recommended method for loading large datasets into Amazon Redshift from Amazon S3. This command is specifically optimized for bulk data loading and leverages Redshift’s parallel processing architecture. It can automatically distribute the load across multiple nodes, significantly reducing load times compared to other methods.
When using the COPY command, Redshift reads data directly from S3 in parallel, utilizing all available compute resources in the cluster. For a file with 10 million rows, this parallel loading mechanism can complete the operation in minutes rather than hours. The COPY command also supports various file formats including CSV, JSON, and Parquet, and can handle compressed files to reduce data transfer costs.
Using INSERT statements from an application would be extremely inefficient for 10 million rows. Each INSERT statement would require a separate transaction and network round trip, resulting in very slow performance. This approach would take hours or even days to complete and would put unnecessary load on both the application and the database.
AWS Lambda has execution time limits and memory constraints that make it unsuitable for processing large files with millions of rows. Loading data row by row through Lambda would be slow, expensive, and would likely hit Lambda’s timeout limits. This approach also does not take advantage of Redshift’s parallel processing capabilities.
Exporting data to Amazon RDS first adds unnecessary complexity and cost. RDS is designed for transactional workloads, not as an intermediate staging area for data warehouse loading. This approach would require additional data transfer steps and would not provide any performance benefits over loading directly from S3.
Question 4
A company uses AWS Glue to run ETL jobs that process data from multiple S3 buckets. The jobs are failing intermittently with out-of-memory errors. What should the data engineer do to resolve this issue?
A) Increase the number of DPUs allocated to the Glue job
B) Reduce the dataset size by deleting old data
C) Switch to AWS Lambda for data processing
D) Move data to Amazon RDS before processing
Answer: A
Explanation:
AWS Glue uses Data Processing Units to allocate compute resources for ETL jobs. Each DPU provides a certain amount of processing capacity including memory and CPU. When Glue jobs encounter out-of-memory errors, it typically indicates that the allocated resources are insufficient for the data volume being processed. Increasing the number of DPUs directly addresses this issue by providing more memory and processing power.
By allocating additional DPUs, you provide your Glue job with more memory to handle larger datasets and more complex transformations. Glue allows you to specify the number of DPUs when creating or updating a job, and you can adjust this based on your workload requirements. Monitoring job metrics can help determine the optimal DPU allocation.
Reducing dataset size by deleting old data is not a viable solution if all the data is needed for business purposes. Data retention requirements often mandate keeping historical data for compliance or analytical reasons. This approach also does not address the underlying resource allocation problem and would only provide temporary relief.
AWS Lambda has a maximum execution time of 15 minutes and limited memory options up to 10 GB, making it unsuitable for large-scale ETL workloads that process data from multiple S3 buckets. Complex transformations and large datasets that cause out-of-memory errors in Glue would face similar or worse issues in Lambda’s more constrained environment.
Moving data to Amazon RDS before processing adds unnecessary complexity and cost. RDS is designed for transactional databases, not as a staging area for ETL processing. This approach would not resolve the memory issues and would introduce additional data transfer and storage costs without providing any benefits.
Question 5
A data engineer needs to implement a solution to track changes to data in an Amazon DynamoDB table and process those changes in near real-time. Which AWS service should be used?
A) DynamoDB Streams with AWS Lambda
B) Amazon Kinesis Data Firehose
C) AWS Database Migration Service
D) Amazon CloudWatch Events
Answer: A
Explanation:
DynamoDB Streams captures a time-ordered sequence of item-level modifications in a DynamoDB table and stores this information for up to 24 hours. When combined with AWS Lambda, you can create event-driven architectures that automatically process changes as they occur. This combination provides near real-time change data capture and processing capabilities with minimal infrastructure management.
Lambda functions can be configured to automatically trigger when new records appear in a DynamoDB Stream. Each Lambda invocation receives a batch of stream records containing information about the changes, including the before and after images of modified items. This enables various use cases such as data replication, audit logging, aggregation, and triggering downstream workflows.
Amazon Kinesis Data Firehose is designed for delivering streaming data to destinations like S3, Redshift, or Elasticsearch. While it can be used in data pipelines, it does not directly integrate with DynamoDB for change data capture. You would need additional components to extract changes from DynamoDB before sending them to Firehose.
AWS Database Migration Service is primarily used for migrating databases to AWS or between different database engines. While it supports ongoing replication, it is not designed for real-time change processing and event-driven architectures. DMS is more suitable for migration scenarios rather than operational change data capture.
Amazon CloudWatch Events can monitor AWS resources and trigger actions, but it does not provide item-level change tracking for DynamoDB tables. CloudWatch Events operates at a higher level, monitoring API calls and service events, rather than individual data modifications within tables.
Question 6
A data engineering team needs to query data stored in Amazon S3 using SQL without loading it into a database. The data is in Parquet format and organized in a partitioned structure. Which AWS service should they use?
A) Amazon Athena
B) Amazon RDS
C) Amazon ElastiCache
D) Amazon Neptune
Answer: A
Explanation:
Amazon Athena is a serverless interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL. It is specifically designed for this use case and natively supports various file formats including Parquet, JSON, and CSV. Athena works particularly well with partitioned data, as it can leverage partition pruning to scan only relevant data and reduce query costs.
Athena integrates with AWS Glue Data Catalog, which stores metadata about your data including schema and partition information. This integration allows Athena to understand the structure of your data and optimize query execution. For Parquet files, Athena can take advantage of columnar storage and predicate pushdown to further improve performance and reduce costs.
Since Athena is serverless, you do not need to manage any infrastructure or provision compute resources. You simply point Athena at your S3 data, define the schema, and start querying. You pay only for the amount of data scanned by your queries, making it a cost-effective solution for ad-hoc analysis and exploration of data in S3.
Amazon RDS is a managed relational database service that requires you to load data into database tables before querying. This approach adds complexity, storage costs, and data loading time. It does not meet the requirement of querying data directly in S3 without loading it into a database.
Amazon ElastiCache is an in-memory caching service used to improve application performance by caching frequently accessed data. It is not designed for querying files in S3 and would not be suitable for this use case.
Amazon Neptune is a fully managed graph database service designed for applications that need to store and query highly connected data. It does not provide SQL querying capabilities for data stored in S3.
Question 7
A company needs to encrypt data at rest in Amazon S3 using their own encryption keys that they manage in their on-premises hardware security module (HSM). Which encryption option should they use?
A) SSE-S3 (Server-Side Encryption with S3-managed keys)
B) SSE-KMS (Server-Side Encryption with AWS KMS)
C) SSE-C (Server-Side Encryption with Customer-provided keys)
D) Client-side encryption with AWS Encryption SDK
Answer: C
Explanation:
SSE-C allows customers to provide their own encryption keys when uploading objects to S3. With this option, Amazon S3 performs the encryption and decryption operations, but you manage the encryption keys yourself. When using SSE-C, you provide the encryption key in the upload request, and S3 uses that key to encrypt the data before storing it. For retrieval, you must provide the same encryption key.
This approach is ideal when organizations have regulatory requirements to manage their own encryption keys or want to use keys generated from their own HSM. The customer maintains full control over the key management lifecycle, including key generation, rotation, and destruction. S3 does not store the encryption keys; it only uses them temporarily during encryption and decryption operations.
SSE-S3 uses encryption keys that are managed entirely by AWS. While this provides automatic encryption at rest, it does not give customers control over the encryption keys. Organizations that need to manage their own keys for compliance or security reasons cannot use this option.
SSE-KMS uses keys stored in AWS Key Management Service. While this provides more control than SSE-S3, the keys are still managed within AWS, not in the customer’s on-premises HSM. This option would not meet the requirement of using keys from an on-premises hardware security module.
Client-side encryption with AWS Encryption SDK requires the application to encrypt data before uploading to S3. While this gives customers full control over encryption, it places the encryption burden on the client application and requires more complex implementation compared to server-side encryption with customer-provided keys.
Question 8
A data engineer needs to design a data lake architecture on AWS. The solution should support both structured and unstructured data, provide metadata management, and enable data discovery. Which AWS service should be used for metadata management?
A) AWS Glue Data Catalog
B) Amazon RDS
C) Amazon DynamoDB
D) AWS Systems Manager Parameter Store
Answer: A
Explanation:
AWS Glue Data Catalog is a centralized metadata repository designed specifically for data lake architectures. It stores metadata about data sources, transformations, and targets, making it easy to discover and manage data across your data lake. The Data Catalog is fully integrated with other AWS analytics services like Athena, EMR, and Redshift Spectrum.
The Data Catalog automatically discovers and catalogs metadata from various data sources through AWS Glue crawlers. These crawlers can scan data in S3, databases, and other sources to extract schema information and populate the catalog. This automatic discovery significantly reduces the manual effort required to maintain metadata and ensures your catalog stays up to date.
The Data Catalog supports both structured and unstructured data, storing information about table definitions, column names, data types, and partitioning schemes. It also maintains version history of schema changes, allowing you to track how your data structures evolve over time. Multiple AWS services can access this centralized metadata repository, ensuring consistency across your analytics ecosystem.
Amazon RDS is a relational database service designed for transactional workloads, not metadata management. While you could theoretically store metadata in RDS, it lacks the specialized features and integrations that make AWS Glue Data Catalog ideal for data lake scenarios. RDS would require custom development and would not provide automatic data discovery.
Amazon DynamoDB is a NoSQL database service that could store metadata but does not provide the specialized data cataloging features needed for a data lake. It lacks integration with analytics services and would require significant custom development to replicate Data Catalog functionality.
AWS Systems Manager Parameter Store is designed for storing configuration data and secrets, not for managing data lake metadata.
Question 9
A company is migrating their on-premises Hadoop cluster to AWS. They want to continue using their existing Hadoop and Spark applications with minimal code changes. Which AWS service should they use?
A) Amazon EMR
B) AWS Glue
C) Amazon Athena
D) AWS Lambda
Answer: A
Explanation:
Amazon EMR is a managed cluster platform that simplifies running big data frameworks including Apache Hadoop and Apache Spark on AWS. EMR provides a compatible environment for existing Hadoop applications, allowing you to migrate workloads with minimal code changes. It supports the same APIs and tools that data engineers are familiar with from on-premises Hadoop clusters.
EMR handles the provisioning, configuration, and tuning of Hadoop clusters, reducing operational overhead. You can choose from various instance types and configure cluster size based on your workload requirements. EMR also integrates with other AWS services like S3 for storage and IAM for security, allowing you to leverage cloud-native capabilities while maintaining Hadoop compatibility.
With EMR, you can continue using existing Hadoop ecosystem tools like Hive, Pig, and HBase without modification. The service supports multiple versions of Hadoop and Spark, giving you flexibility in choosing the version that matches your current environment. This compatibility ensures a smooth migration path from on-premises to AWS.
AWS Glue is a serverless ETL service that uses a different programming model based on Apache Spark. While Glue can handle similar workloads, migrating existing Hadoop applications to Glue would require significant code refactoring. Glue is better suited for new ETL development rather than migrating existing Hadoop workloads.
Amazon Athena is a query service for analyzing data in S3 using SQL. It does not support running Hadoop or Spark applications and would require complete rewriting of existing code. Athena is designed for different use cases than a full Hadoop cluster.
AWS Lambda is a serverless compute service for running functions in response to events. It is not designed for big data processing and cannot run Hadoop or Spark applications.
Question 10
A data engineer needs to implement a solution to automatically partition incoming data in Amazon S3 based on date and then update the AWS Glue Data Catalog. What is the most efficient approach?
A) Use AWS Glue crawlers to automatically detect and add new partitions
B) Manually create partitions using SQL ALTER TABLE statements
C) Use AWS Lambda to create partition folders and update catalog
D) Write a shell script to run daily and update partitions
Answer: A
Explanation:
AWS Glue crawlers are designed to automatically scan data sources, infer schemas, and detect partitions. When configured to run on a schedule, crawlers can regularly scan your S3 bucket, identify new partitions based on folder structure, and automatically update the Data Catalog. This automated approach eliminates manual maintenance and ensures your catalog stays current with incoming data.
Crawlers use built-in classifiers to identify partition patterns in your S3 folder structure. For date-based partitioning, if your data is organized with folders like year=2024/month=01/day=15, the crawler automatically recognizes these as partition columns. The crawler then creates or updates table definitions in the Data Catalog with the appropriate partition information.
Using crawlers is efficient because they only process new or changed files, not the entire dataset on every run. This incremental approach minimizes processing time and costs. Crawlers can also be configured to run on a schedule or triggered by events, providing flexibility in how often your catalog is updated.
Manually creating partitions using SQL ALTER TABLE statements is time-consuming and error-prone. As new data arrives daily, this approach requires continuous manual intervention. It does not scale well and increases the risk of human error, potentially leading to missing or incorrectly configured partitions.
Using AWS Lambda to create partition folders and update the catalog adds unnecessary complexity. While this approach can work, it requires custom code development, error handling, and maintenance. It duplicates functionality that is already provided by Glue crawlers in a more robust and integrated manner.
Writing a shell script to run daily is another manual approach that requires infrastructure to run the script, monitoring to ensure it executes successfully, and ongoing maintenance. This approach is less reliable than using native AWS services.
Question 11
A company processes IoT sensor data that arrives in JSON format. They need to convert this data to Parquet format and store it in Amazon S3 for efficient querying. Which AWS service is best suited for this transformation?
A) AWS Glue ETL jobs
B) Amazon Kinesis Data Analytics
C) AWS DataSync
D) Amazon Simple Queue Service
Answer: A
Explanation:
AWS Glue ETL jobs are specifically designed for data transformation tasks like converting data formats. Glue supports reading JSON data from S3, transforming it through various operations, and writing the output in Parquet format. Glue’s built-in support for both JSON and Parquet makes it ideal for this use case without requiring custom code for format conversion.
Glue ETL jobs can handle large-scale data transformations efficiently using Apache Spark under the hood. The service automatically parallelizes the workload across multiple workers, enabling fast processing of large datasets. Glue also provides a visual interface for building ETL pipelines and supports Python or Scala for more complex transformations.
Parquet is a columnar storage format that significantly improves query performance and reduces storage costs compared to JSON. By converting JSON to Parquet, you enable faster queries with services like Athena and Redshift Spectrum, and reduce the amount of data scanned during queries, lowering costs. Glue handles this conversion efficiently with built-in optimizations.
Amazon Kinesis Data Analytics is designed for real-time stream processing using SQL or Apache Flink. While it can process streaming data, it is not optimized for batch conversion of existing data or writing data specifically in Parquet format. Kinesis Data Analytics is better suited for real-time analytics rather than batch format conversion.
AWS DataSync is a data transfer service for moving data between on-premises storage and AWS. It does not perform data transformations or format conversions. DataSync focuses on efficient data transfer and synchronization, not on changing data formats.
Amazon Simple Queue Service is a message queuing service for decoupling application components. It does not provide data transformation capabilities and would not be appropriate for converting JSON to Parquet format.
Question 12
A data engineering team needs to implement row-level security in Amazon Redshift so that users can only see data from their own department. What feature should they use?
A) Row-level security policies in Redshift
B) VPC security groups
C) IAM policies
D) S3 bucket policies
Answer: A
Explanation:
Amazon Redshift provides native row-level security that allows you to control which rows users can access in tables based on their identity or role. This feature enables fine-grained access control within tables, ensuring users only see data they are authorized to view. RLS policies are defined using SQL and can reference user attributes or session variables to determine access.
To implement row-level security, you create security policies that define filtering conditions. For example, you could create a policy that filters rows based on a department column matching the user’s department attribute. These policies are automatically applied whenever users query the table, transparently restricting results without requiring application-level filtering.
Row-level security in Redshift is efficient because the filtering is applied at the database level during query execution. This approach ensures consistent security enforcement across all applications and query tools. The security policies are managed centrally in the database, simplifying administration and reducing the risk of security gaps.
VPC security groups control network traffic to and from resources at the network level. They do not provide row-level access control within database tables. Security groups are used to restrict which IP addresses can connect to your Redshift cluster, not to filter data within tables.
IAM policies control access to AWS services and resources but do not provide row-level filtering within Redshift tables. IAM can control who can connect to a Redshift cluster or perform administrative actions, but it cannot restrict which rows a user sees within a table.
S3 bucket policies control access to objects in S3 buckets. They are not related to row-level security in Redshift tables and cannot restrict which rows users can query.
Question 13
A company needs to process streaming data from multiple sources and write it to Amazon S3 in real-time. The solution should automatically handle data format conversion and compression. Which AWS service should be used?
A) Amazon Kinesis Data Firehose
B) Amazon Kinesis Data Streams
C) AWS Glue Streaming
D) Amazon SQS
Answer: A
Explanation:
Amazon Kinesis Data Firehose is a fully managed service that reliably loads streaming data into destinations like S3, Redshift, and Elasticsearch. Firehose can automatically convert data formats, compress data, and batch records before delivering them to S3. This built-in functionality eliminates the need for custom code to handle these common requirements.
Firehose supports data transformation through AWS Lambda integration, allowing you to modify records before delivery. It can automatically convert JSON data to Parquet or ORC format, which are columnar formats optimized for analytics. Firehose also supports GZIP, Snappy, and ZIP compression to reduce storage costs in S3.
The service handles buffering and batching automatically, optimizing delivery based on size and time intervals you configure. This batching reduces the number of S3 PUT requests and associated costs. Firehose also provides built-in error handling and can deliver failed records to a separate S3 location for analysis.
Amazon Kinesis Data Streams is a real-time data streaming service but does not provide automatic format conversion or compression. You would need to implement a consumer application, often using Lambda or a separate application, to read from the stream and handle format conversion before writing to S3.
AWS Glue Streaming is designed for ETL processing of streaming data but requires more configuration and management compared to Firehose. While Glue can perform format conversion, Firehose provides a simpler, more managed solution specifically designed for delivery to destinations like S3.
Amazon SQS is a message queuing service that does not provide direct integration with S3 or automatic format conversion capabilities.
Question 14
A data engineer needs to migrate a 10 TB Oracle database to Amazon Redshift with minimal downtime. Which AWS service is most appropriate for this task?
A) AWS Database Migration Service
B) AWS DataSync
C) AWS Snowball
D) AWS Transfer Family
Answer: A
Explanation:
AWS Database Migration Service is specifically designed for migrating databases to AWS with minimal downtime. DMS supports heterogeneous migrations between different database engines, including Oracle to Redshift. It can perform an initial full load of data and then continuously replicate ongoing changes until you are ready to switch over to the target database.
DMS uses change data capture to replicate ongoing changes from the source Oracle database to the target Redshift cluster. This allows you to minimize downtime by keeping the target database synchronized with the source until you are ready to cut over. The service handles schema conversion and data type mapping between Oracle and Redshift.
For large databases like 10 TB, DMS can be configured to use multiple replication tasks running in parallel to speed up the migration. You can also use AWS Schema Conversion Tool to assess and convert database schemas before migration. DMS monitors the migration process and provides detailed metrics and logs.
AWS DataSync is designed for transferring files between storage systems, not for database migrations. It does not understand database structures or handle change data capture. DataSync is more suitable for moving files to S3 or EFS rather than migrating databases.
AWS Snowball is a physical device for transferring large amounts of data into AWS. While it could be used to transfer a database backup, it does not provide the change data capture and continuous replication capabilities needed for minimal downtime migrations. Snowball is better suited for one-time bulk data transfers.
AWS Transfer Family provides managed SFTP, FTPS, and FTP services for transferring files to S3. It is not designed for database migrations and does not support the complex operations required for migrating Oracle to Redshift.
Question 15
A company stores application logs in Amazon S3 and needs to analyze them using SQL queries. The logs are in JSON format with nested structures. Which approach should be used to query this data efficiently?
A) Use Amazon Athena with nested JSON support
B) Load data into Amazon RDS first
C) Convert all JSON to CSV manually
D) Use Amazon DynamoDB for storage
Answer: A
Explanation:
Amazon Athena provides native support for querying JSON data stored in S3, including nested and complex data structures. Athena can parse JSON files and allows you to access nested fields using dot notation or array indexing in SQL queries. This capability eliminates the need to flatten or preprocess JSON data before analysis.
When querying JSON with Athena, you can define table schemas that map to your JSON structure, including arrays and nested objects. Athena’s Presto-based query engine understands JSON data types and can efficiently extract and process nested fields. You can also use functions like json_extract to work with complex JSON paths.
For better performance with large JSON datasets, you can use AWS Glue to convert JSON to Parquet format while preserving nested structures. Parquet’s columnar format significantly improves query performance and reduces costs compared to scanning JSON files. This conversion can be done periodically as new log files arrive.
Loading data into Amazon RDS would require flattening the nested JSON structures into relational tables, which adds complexity and may lose the flexibility of the original data structure. RDS is also more expensive for storing and querying large volumes of log data compared to the S3 and Athena combination.
Manually converting JSON to CSV would lose the nested structure of the data and require significant manual effort. CSV format cannot represent nested or complex data structures effectively, and this approach would not scale as log volume grows.
Amazon DynamoDB is designed for transactional workloads with key-value or document access patterns, not for analytical queries using SQL. While DynamoDB can store JSON documents, it does not provide SQL query capabilities for log analysis.
Question 16
A data pipeline needs to process files as soon as they arrive in an Amazon S3 bucket. Which AWS service combination provides the most efficient event-driven architecture?
A) S3 Event Notifications with AWS Lambda
B) CloudWatch Events with EC2 instances
C) AWS Batch with manual triggers
D) Scheduled AWS Glue jobs running every minute
Answer: A
Explanation:
S3 Event Notifications can automatically trigger AWS Lambda functions whenever objects are created in an S3 bucket. This provides a true event-driven architecture where processing begins immediately upon file arrival, without any polling or delays. Lambda functions can then process the files, transform data, or trigger additional workflows as needed.
The combination of S3 Event Notifications and Lambda is serverless and highly scalable. Multiple files arriving simultaneously will automatically trigger multiple Lambda function instances to process them in parallel. This architecture scales automatically based on workload without requiring capacity planning or infrastructure management.
S3 Event Notifications can be filtered based on object key prefixes and suffixes, allowing you to trigger different Lambda functions for different types of files or folder structures. This flexibility enables sophisticated routing logic without complex code. You pay only for the Lambda execution time used, making this approach cost-effective.
CloudWatch Events can monitor S3 API calls through CloudTrail, but this introduces additional latency and complexity compared to direct S3 Event Notifications. Using EC2 instances would require managing compute infrastructure and implementing polling logic to check for new files, which is less efficient than event-driven triggers.
AWS Batch is designed for long-running batch processing jobs, not for immediate event-driven processing. Batch jobs require manual triggers or scheduling and do not provide the instant response to file arrivals that S3 Event Notifications offer.
Scheduled AWS Glue jobs running every minute would introduce up to one minute of delay before files are processed. This approach also wastes resources by running jobs even when no files arrive, and it does not scale efficiently with varying workloads.
Question 17
A data engineer needs to implement a solution where data from multiple AWS accounts can be queried using Amazon Athena. The data should remain in the original accounts. What approach should be used?
A) Configure cross-account S3 bucket access with IAM roles
B) Copy all data to a central S3 bucket
C) Use AWS DataSync to synchronize data
D) Create database replicas in each account
Answer: A
Explanation:
Cross-account S3 bucket access allows Athena in one account to query data stored in S3 buckets in other AWS accounts. This is achieved by configuring IAM roles and bucket policies that grant the necessary permissions. The data remains in its original location, eliminating the need for data duplication and associated costs.
To implement this, you create an IAM role in the account where Athena runs that can assume roles in the accounts containing the data. The S3 bucket policies in the data accounts grant read permissions to this role. Athena can then query data across multiple accounts as if it were in a single location, using the AWS Glue Data Catalog to maintain metadata.
This approach maintains data governance and ownership boundaries while enabling centralized analytics. Each account retains control over its data and can modify access permissions independently. The solution scales efficiently as you add more accounts without requiring architectural changes.
Copying all data to a central S3 bucket creates data duplication, which increases storage costs and introduces data consistency challenges. Any updates to source data require synchronization to the central bucket, adding complexity and potential delays. This approach also concentrates data ownership in a single account, which may violate organizational boundaries.
AWS DataSync is designed for one-time or scheduled data transfers, not for maintaining a unified query interface across multiple accounts. Continuous synchronization would incur unnecessary costs and still suffer from the data duplication issues mentioned above.
Creating database replicas in each account does not solve the problem of querying data from multiple accounts in a unified manner. This approach would require separate queries to each account and complex logic to combine results.
Question 18
A company needs to ensure that data in Amazon S3 is protected against accidental deletion. What combination of features should be implemented?
A) Enable S3 Versioning and S3 Object Lock
B) Use IAM policies only
C) Create manual backups daily
D) Enable S3 Transfer Acceleration
Answer: A
Explanation:
S3 Versioning maintains multiple versions of each object in a bucket, allowing you to recover from accidental deletions or overwrites. When versioning is enabled, deleting an object creates a delete marker rather than permanently removing it. You can retrieve previous versions at any time, providing a safety net against data loss.
S3 Object Lock provides WORM capabilities, preventing objects from being deleted or overwritten for a specified retention period. This feature is particularly important for compliance requirements where data must be immutable. Object Lock can be configured in governance mode or compliance mode depending on your retention requirements.
Together, versioning and Object Lock provide comprehensive protection. Versioning enables recovery from accidental changes, while Object Lock ensures that even administrators cannot delete or modify protected objects during the retention period. This dual-layer approach addresses both accidental and intentional data loss scenarios.
Using IAM policies alone can restrict who can delete objects, but they do not protect against accidental deletions by authorized users. IAM policies control access but do not preserve previous versions of objects or prevent authorized users from making mistakes.
Creating manual backups daily is operationally intensive and does not provide continuous protection. Objects deleted or modified between backup windows would be lost. This approach also requires additional storage and management overhead compared to built-in S3 features.
S3 Transfer Acceleration improves upload speeds for files transferred over long distances but does not provide any data protection capabilities. It is designed for performance optimization, not data durability or recovery.
Question 19
A data engineer needs to analyze streaming clickstream data to identify user behavior patterns in real-time. The solution should support SQL queries on streaming data. Which AWS service should be used?
A) Amazon Kinesis Data Analytics
B) Amazon Athena
C) AWS Glue
D) Amazon QuickSight
Answer: A
Explanation:
Amazon Kinesis Data Analytics enables real-time analytics on streaming data using SQL or Apache Flink. For clickstream analysis, you can write SQL queries that process data as it flows through Kinesis Data Streams or Kinesis Data Firehose. The service supports windowing functions, aggregations, and pattern detection on streaming data.
Kinesis Data Analytics can perform continuous queries that update results in real-time as new data arrives. You can detect trends, calculate metrics over time windows, and identify anomalies in user behavior as events occur. The service automatically scales to handle varying data volumes and provides exactly-once processing semantics.
The SQL-based approach makes it accessible to analysts familiar with SQL without requiring knowledge of complex stream processing frameworks. You can join streaming data with reference data stored in S3 to enrich clickstream events with user or product information. Results can be sent to various destinations for visualization or further processing.
Amazon Athena is designed for querying static data in S3, not streaming data. While Athena is powerful for ad-hoc analysis of historical clickstream data, it cannot process data in real-time as events occur. Athena queries run on demand against existing data rather than continuously processing incoming streams.
AWS Glue is an ETL service that can process streaming data but requires more complex setup with Apache Spark Structured Streaming. While capable, Glue is not optimized for SQL-based real-time analytics and requires more programming knowledge compared to Kinesis Data Analytics.
Amazon QuickSight is a business intelligence visualization tool that consumes data from various sources but does not directly process streaming data or perform real-time analytics on clickstreams. QuickSight is used for visualization and dashboarding after data has been processed.
Question 20
A company stores sensitive customer data in Amazon Redshift and needs to mask certain columns when non-privileged users query the data. What feature should be implemented?
A) Dynamic data masking
B) Encryption at rest
C) VPC endpoints
D) S3 bucket policies
Answer: A
Explanation:
Dynamic data masking in Amazon Redshift allows you to control how sensitive data is displayed to users based on their privileges. With masking policies, you can hide, partially mask, or redact sensitive information like credit card numbers, email addresses, or personal identifiers. The actual data remains unchanged in storage but appears masked to unauthorized users in query results.
Masking policies are defined at the column level and can apply different masking functions based on user roles or permissions. For example, you might show the last four digits of a credit card number to customer service representatives while completely hiding the number from analysts. This approach provides fine-grained control over data visibility.
Dynamic data masking is applied at query time, meaning there is no performance overhead for data loading or storage. The masking is transparent to users and applications, requiring no changes to queries or application code. Administrators can modify masking policies centrally without affecting the underlying data.
Encryption at rest protects data stored on disk from unauthorized access at the storage level but does not control how data is displayed to authorized users who can decrypt it. Encryption is important for security but does not address the requirement of masking data in query results.
VPC endpoints provide secure network connectivity between your VPC and AWS services without traversing the public internet. They improve security and performance but do not control how data is displayed to users within queries.
S3 bucket policies control access to objects in S3 and are not related to masking data in Redshift query results. Bucket policies cannot implement column-level masking or control how data appears to different users.
