Visit here for our full Amazon AWS Certified Data Engineer – Associate DEA-C01 exam dumps and practice test questions.
Question 21
A data pipeline processes files from S3 using AWS Glue. The pipeline occasionally fails due to corrupt or malformed input files. What approach should be used to handle these errors gracefully?
A) Implement error handling in Glue job with try-except blocks
B) Delete all input files and restart
C) Increase Glue DPU allocation
D) Switch to AWS Lambda for processing
Answer: A
Explanation:
AWS Glue jobs can include error handling logic using try-except blocks in Python or similar constructs in Scala. This allows you to catch exceptions that occur when processing corrupt files and handle them appropriately. You can log errors, skip problematic records, or move bad files to an error bucket for later investigation.
Implementing robust error handling ensures that your pipeline continues processing valid files even when encountering bad data. You can design your Glue job to process files individually or in batches, catching exceptions for each unit of work. Failed files can be tracked and reported while successful files complete processing normally.
A common pattern is to implement a dead-letter mechanism where corrupt files are moved to a separate S3 location with metadata about the error. This allows data engineers to investigate issues without stopping the entire pipeline. You can also implement retry logic with exponential backoff for transient errors.
Deleting all input files and restarting would result in permanent data loss and does not solve the underlying problem of handling corrupt files. This approach also requires manual intervention every time an error occurs, making it operationally unsustainable.
Increasing Glue DPU allocation provides more compute resources but does not help with data quality issues. Corrupt or malformed files will still cause errors regardless of available resources. DPU scaling addresses performance issues, not data validation problems.
Switching to AWS Lambda does not inherently solve the error handling problem. Lambda faces the same challenges with corrupt data and has additional constraints like execution time limits. The error handling logic would still need to be implemented regardless of the compute platform.
Question 22
A company wants to analyze data from their Amazon RDS MySQL database using Amazon Redshift. What is the recommended approach to get data from RDS to Redshift?
A) Use AWS Glue to extract data from RDS and load into Redshift
B) Manually export CSV files daily
C) Use Amazon Kinesis to stream data
D) Enable Redshift read replicas of RDS
Answer: A
Explanation:
AWS Glue provides JDBC connectivity to Amazon RDS databases, allowing you to extract data directly from MySQL and load it into Redshift. Glue can read data from RDS tables, perform transformations as needed, and use the Redshift COPY command or direct connection to load data efficiently. This approach can be scheduled to run regularly or triggered by events.
Glue handles the complexities of reading from RDS in parallel, managing connection pools, and optimizing data transfer. You can configure Glue to perform incremental loads by tracking changes since the last extraction, reducing the amount of data transferred. Glue also provides built-in retry and error handling mechanisms.
The Glue visual ETL editor allows you to build the pipeline without extensive coding, though you can also write custom Python or Scala code for complex transformations. Glue integrates with other AWS services for monitoring and logging, providing visibility into the data movement process.
Manually exporting CSV files daily is operationally intensive, error-prone, and does not scale well. This approach requires manual intervention, lacks automation, and introduces delays in data availability. Manual processes are also difficult to monitor and troubleshoot compared to automated ETL pipelines.
Amazon Kinesis is designed for streaming real-time data and is not the optimal choice for batch extraction from RDS. While you could potentially stream database changes using Kinesis, this requires additional components and complexity compared to using Glue for periodic batch loads.
Redshift read replicas of RDS do not exist as a feature. These are separate database services with different architectures. You cannot create a Redshift read replica of an RDS database, as they use different storage and query engines.
Question 23
A data engineer needs to implement a data quality framework to validate incoming data before processing. The validation should check for null values, data type mismatches, and value ranges. Which AWS service provides built-in data quality features?
A) AWS Glue Data Quality
B) Amazon CloudWatch Logs
C) AWS Config
D) Amazon Inspector
Answer: A
Explanation:
AWS Glue Data Quality provides a comprehensive framework for defining and evaluating data quality rules. You can create rules that check for null values, validate data types, ensure values fall within expected ranges, and verify referential integrity. These rules are defined using a declarative syntax and can be attached to Glue jobs or run independently.
Glue Data Quality automatically generates recommended rules by analyzing your data and identifying common quality checks. You can customize these rules or create your own based on business requirements. The service evaluates data against these rules and produces detailed quality metrics and reports.
When quality checks fail, you can configure actions such as stopping the job, sending notifications, or marking data with quality scores. This allows you to prevent bad data from propagating through your pipeline while maintaining visibility into data quality trends over time. Quality results are stored and can be tracked historically.
Amazon CloudWatch Logs is a logging service that stores application and system logs but does not provide data quality validation capabilities. While you could write custom code to log validation results to CloudWatch, it does not offer built-in data quality rules or evaluation frameworks.
AWS Config tracks configuration changes to AWS resources and evaluates resource compliance against rules. It is designed for infrastructure compliance, not data quality validation. Config operates at the resource level, not at the data content level.
Amazon Inspector is a security assessment service that scans for vulnerabilities and security issues in applications and infrastructure. It does not perform data quality validation or check business rules for data content.
Question 24
A company receives daily data files from partners via SFTP. They need to automatically upload these files to Amazon S3 when they arrive. What AWS service should be used?
A) AWS Transfer Family
B) AWS DataSync
C) AWS Storage Gateway
D) Amazon WorkDocs
Answer: A
Explanation:
AWS Transfer Family provides fully managed SFTP, FTPS, and FTP servers that directly integrate with Amazon S3. Partners can connect using standard SFTP clients and upload files, which are automatically stored in S3 buckets. This eliminates the need to manage SFTP server infrastructure while providing seamless integration with AWS storage.
Transfer Family allows you to configure custom authentication using AWS Directory Service, custom identity providers, or service-managed users. You can map SFTP users to specific S3 buckets or prefixes, controlling where each partner’s files are stored. The service handles encryption in transit and integrates with AWS security services.
You can configure Transfer Family to trigger Lambda functions or other workflows when files arrive, enabling automated processing pipelines. The service provides detailed logging through CloudWatch, allowing you to monitor file transfers and troubleshoot issues. Transfer Family scales automatically to handle varying upload volumes.
AWS DataSync is designed for large-scale data transfer and migration between storage systems, not for providing SFTP endpoints for partners. DataSync requires agents and is focused on scheduled or one-time transfers rather than providing continuous SFTP access.
AWS Storage Gateway provides hybrid cloud storage integration between on-premises environments and AWS, but does not offer SFTP server functionality. Storage Gateway is used for different scenarios like backup, archiving, and disaster recovery from on-premises systems.
Amazon WorkDocs is a managed document collaboration service for sharing and managing files within organizations. It is not designed for automated file transfer from external partners via SFTP and does not integrate with S3 in the same way.
Question 25
A data engineer needs to optimize an Amazon Redshift cluster that is experiencing slow query performance. Queries frequently filter on date columns. What optimization technique should be applied?
A) Define sort keys on date columns
B) Increase the number of nodes
C) Switch to provisioned IOPS storage
D) Enable query result caching only
Answer: A
Explanation:
Sort keys in Amazon Redshift determine the order in which data is stored on disk. When you define a sort key on a date column, Redshift stores data sorted by that column, which dramatically improves query performance for queries that filter or sort by date. Redshift can skip reading entire blocks of data that fall outside the query’s date range.
When queries include WHERE clauses that filter on the sort key column, Redshift uses zone maps to eliminate disk blocks that do not contain relevant data. This reduces the amount of data that needs to be scanned, significantly improving query speed and reducing I/O costs. Sort keys are particularly effective for time-series data where queries typically filter on date ranges.
You can define compound sort keys that include multiple columns or interleaved sort keys for queries that filter on different column combinations. For date-based filtering, a compound sort key with the date column as the first column typically provides the best performance. Redshift maintains these sort orders automatically.
Increasing the number of nodes adds more compute and storage capacity but does not address the root cause of slow performance when queries scan too much data. While more nodes can help with overall throughput, optimizing data layout with sort keys provides much greater performance improvements for filtered queries.
Provisioned IOPS storage is not directly available as a configuration option in Redshift. Redshift uses its own managed storage architecture. While storage performance matters, proper sort key definition has a more significant impact on query performance for filtered queries.
Query result caching helps when the same queries are run repeatedly but does not improve the performance of the initial query execution. Caching alone does not optimize how data is stored or scanned on disk.
Question 26
A company uses Amazon EMR to run Spark jobs that process data in S3. They need to reduce costs while maintaining performance. What cost optimization strategy should be implemented?
A) Use Spot Instances for task nodes
B) Keep cluster running 24/7
C) Use only on-demand instances
D) Increase instance sizes
Answer: A
Explanation:
Amazon EMR supports Spot Instances, which can provide up to 90% cost savings compared to On-Demand instances. For EMR clusters, you can use Spot Instances for task nodes, which perform compute operations but do not store data. If Spot Instances are interrupted, the work can be redistributed to other nodes without data loss.
A best practice is to use On-Demand or Reserved Instances for master and core nodes, which manage the cluster and store HDFS data, while using Spot Instances for task nodes that provide additional processing capacity. This hybrid approach balances cost savings with cluster reliability, as losing task nodes does not compromise data or cluster availability.
EMR provides features like instance fleets and allocation strategies that automatically request multiple Spot Instance types, increasing the likelihood of obtaining capacity and reducing interruptions. You can also configure EMR to automatically scale task nodes based on workload, adding Spot capacity when needed and reducing costs during idle periods.
Keeping the cluster running continuously increases costs significantly. EMR charges by the hour for each instance, so running clusters when they are not processing jobs wastes resources. Using transient clusters that start for specific jobs and terminate when complete can reduce costs by 50% or more.
Using only On-Demand instances provides maximum availability but does not take advantage of potential cost savings from Spot or Reserved Instances. For workloads that can tolerate occasional interruptions, Spot Instances offer substantial savings without compromising job completion.
Increasing instance sizes typically increases costs unless the larger instances enable faster job completion that more than offsets the higher hourly rate. Right-sizing instances based on actual resource utilization is more effective than simply increasing sizes.
Question 27
A data pipeline needs to process confidential data across multiple AWS accounts. The pipeline should not expose data to intermediate storage. What approach should be used?
A) Use IAM cross-account roles with direct access
B) Copy data to a public S3 bucket temporarily
C) Email data between accounts
D) Use unencrypted EBS snapshots
Answer: A
Explanation:
IAM cross-account roles enable secure access to resources across AWS accounts without exposing data through intermediate storage or manual transfers. You can grant specific permissions to roles in other accounts, allowing services like Glue, Lambda, or EMR to directly access S3 buckets or other resources in different accounts while maintaining security boundaries.
With cross-account roles, data remains in its original location and is accessed only by authorized principals. The data is transmitted using encrypted connections and never stored in intermediate locations. This approach maintains compliance with data protection requirements while enabling multi-account workflows.
You configure trust policies that specify which accounts can assume the role and resource policies that define what actions are permitted. This granular control ensures that only authorized services can access specific resources. All access is logged through CloudTrail, providing an audit trail for compliance purposes.
Copying data to a public S3 bucket creates severe security risks by exposing confidential data to the internet. Public buckets can be accessed by anyone and are frequently targeted by automated scanners. This approach violates basic security principles and compliance requirements for handling confidential data.
Emailing data between accounts is extremely insecure, inefficient, and non-scalable. Email systems are not designed for large data transfers and lack the security controls needed for confidential information. This approach also creates multiple copies of data that are difficult to track and secure.
Using unencrypted EBS snapshots exposes data during storage and transfer. Snapshots should always be encrypted for confidential data, and this method still requires manual intervention to share and copy data between accounts, making it operationally complex.
Question 28
A data engineer needs to schedule AWS Glue jobs to run in a specific sequence where one job starts only after the previous job completes successfully. What feature should be used?
A) AWS Glue Workflows
B) CloudWatch Events only
C) Manual triggering
D) Separate cron jobs
Answer: A
Explanation:
AWS Glue Workflows allow you to orchestrate multiple jobs, crawlers, and triggers into a single automated pipeline. You can define dependencies between jobs so that each job starts only when its prerequisites complete successfully. Workflows provide visual representation of the pipeline and centralized monitoring of execution status.
Workflows support complex dependencies including parallel execution of independent jobs and conditional branching based on job outcomes. You can configure different actions for success and failure conditions, implementing error handling and retry logic. This makes workflows ideal for complex ETL pipelines with multiple stages.
Glue Workflows integrate with EventBridge for external triggering and provide built-in error handling and notification capabilities. You can start workflows on a schedule, in response to events, or manually. The workflow tracks the state of all components and provides detailed execution history for troubleshooting.
Using CloudWatch Events alone requires managing individual job triggers and does not provide dependency management or workflow orchestration. You would need to implement custom logic to check job completion status and trigger subsequent jobs, adding complexity and maintenance burden.
Manual triggering requires human intervention and does not scale for production pipelines that need to run regularly. This approach is error-prone and cannot guarantee consistent execution order or handle failures gracefully. Manual processes also lack the audit trail and repeatability needed for production systems.
Separate cron jobs for each step of the pipeline cannot reliably enforce dependencies or handle errors. If one job fails, subsequent jobs may still run on schedule, processing incomplete or incorrect data. This approach requires complex scripting to check job status and is difficult to maintain.
Question 29
A company needs to replicate data from their on-premises MySQL database to Amazon S3 in near real-time for analytics. What AWS service should be used?
A) AWS Database Migration Service with ongoing replication
B) Manual exports every hour
C) AWS Snowball
D) Amazon Route 53
Answer: A
Explanation:
AWS Database Migration Service supports continuous data replication from on-premises databases to AWS destinations including S3. DMS captures changes from the source database using change data capture and applies them to the target with minimal latency. This enables near real-time analytics on current data without impacting the production database.
For MySQL sources, DMS uses binary logs to capture changes including inserts, updates, and deletes. These changes are continuously replicated to S3 in formats like CSV or Parquet, which are optimized for analytics. DMS handles schema changes automatically and provides monitoring dashboards to track replication lag and throughput.
You can configure DMS to partition data in S3 by date or other attributes, making it efficient to query with services like Athena. DMS also supports data transformations during replication, allowing you to filter, rename columns, or modify data types as needed. The service automatically handles connection management and retries for reliability.
Manual exports every hour create significant delays in data availability and require operational overhead. Hourly exports also put periodic load on the production database and do not provide true near real-time replication. This approach cannot capture changes that occur between export windows.
AWS Snowball is a physical device for one-time bulk data transfers and does not support continuous replication or near real-time data synchronization. Snowball is designed for migration scenarios, not ongoing operational replication.
Amazon Route 53 is a DNS service and has no relation to database replication or data transfer to S3. Route 53 is used for routing traffic to applications, not for moving data between systems.
Question 30
A data engineer needs to ensure that queries against Amazon Athena tables return consistent results even as new data is added to S3. What approach should be used?
A) Create external tables with explicit partitions
B) Query data directly without tables
C) Use random sampling
D) Disable all data ingestion during queries
Answer: A
Explanation:
Creating external tables in Athena with explicitly defined partitions allows you to control which data is included in query results. By partitioning data and only querying specific partitions, you ensure consistency even as new data arrives. New data added to unqueried partitions does not affect ongoing query results, providing stable snapshots of data.
Athena external tables reference data in S3 without moving or modifying it. When you define partitions, Athena only scans the relevant S3 prefixes for each partition included in the query. You can add new partitions for incoming data while queries against existing partitions remain unaffected by subsequent data additions.
For time-series data, partitioning by date allows queries to specify exact date ranges that do not change as new data arrives. You can add new date partitions incrementally while historical partitions remain stable. This approach also improves query performance by reducing the amount of data scanned.
Querying data directly without tables requires specifying S3 paths and schemas in each query, which is less consistent and does not provide the partition management benefits of external tables. This approach makes it harder to control which data is included and can lead to inconsistent results.
Using random sampling does not ensure consistency and may miss important data. Sampling is useful for exploratory analysis but does not address the requirement of getting consistent results as the underlying dataset changes. Sample results will vary across query executions.
Disabling all data ingestion during queries is operationally impractical and creates unnecessary downtime for data pipelines. This approach does not scale and would severely impact business operations that require continuous data ingestion and processing.
Question 31
A company processes clickstream data using Amazon Kinesis Data Streams. They need to ensure that events from the same user session are processed by the same consumer. What feature should be used?
A) Partition keys based on session ID
B) Random partition assignment
C) Single shard configuration
D) Broadcast all events to all consumers
Answer: A
Explanation:
Kinesis Data Streams uses partition keys to determine which shard receives each record. When you use a consistent partition key like session ID, all events with the same session ID are guaranteed to go to the same shard and be processed in order by the same consumer. This ensures that related events are processed together, maintaining session context.
Partition keys are hashed to determine shard assignment, and the same partition key always hashes to the same shard. This deterministic routing enables stateful processing where consumers can maintain session state across multiple events. For clickstream analysis, this allows you to track user journeys and session metrics accurately.
By distributing sessions across shards based on partition keys, you achieve both ordering guarantees within sessions and parallelization across sessions. Different sessions are processed by different shards and consumers, providing scalability while maintaining the correctness of session-based analytics.
Random partition assignment would distribute events from the same session across multiple shards, potentially processing them out of order by different consumers. This breaks session continuity and makes it impossible to maintain accurate session state or ensure correct ordering of events within a session.
Using a single shard configuration eliminates parallelization and severely limits throughput. While this would ensure all events are processed by one consumer in order, it cannot scale to handle high-volume clickstream data and creates a bottleneck that degrades performance.
Broadcasting all events to all consumers is not supported by Kinesis Data Streams and would be extremely inefficient. This approach would require every consumer to process every event, wasting resources and preventing effective partitioning of work across consumers.
Question 32
A data pipeline uses AWS Lambda to process files from S3. The Lambda function occasionally times out when processing large files. What is the best solution?
A) Increase Lambda timeout and memory settings
B) Process everything in one execution
C) Reduce file size requirements
D) Disable timeout limits
Answer: A
Explanation:
AWS Lambda allows you to configure execution timeout up to 15 minutes and memory allocation up to 10 GB. Increasing these settings provides more time and resources for processing large files. Memory allocation also affects CPU performance, so increasing memory can speed up processing and help complete within timeout limits.
For Lambda functions processing large files, you should analyze execution metrics to determine optimal settings. CloudWatch Logs show execution duration and memory usage, helping you identify whether timeout or memory constraints are causing failures. Start with conservative increases and adjust based on actual performance.
If files are too large to process within Lambda’s 15-minute limit even with maximum resources, consider alternative architectures like using AWS Glue for batch processing or breaking files into smaller chunks. For most scenarios, properly configured Lambda settings can handle typical file processing workloads efficiently.
Trying to process everything in one execution without adjusting timeout settings does not solve the problem. Lambda will still timeout if the function cannot complete within the configured limit. You must increase the timeout setting to allow longer execution times.
Reducing file size requirements may not be feasible if file sizes are determined by external systems or business requirements. While this could help, it does not address the architectural issue of properly configuring Lambda for your workload.
Lambda timeout cannot be disabled as it is a hard limit designed to prevent runaway executions. The maximum timeout is 15 minutes, which should be sufficient for most file processing tasks when combined with adequate memory allocation.
Question 33
A data engineer needs to optimize storage costs for historical data in Amazon S3 that is rarely accessed but must be retained for compliance. Which storage class should be used?
A) S3 Glacier Deep Archive
B) S3 Standard
C) S3 Intelligent-Tiering
D) S3 One Zone-IA
Answer: A
Explanation:
S3 Glacier Deep Archive provides the lowest storage cost for data that is rarely accessed and has long-term retention requirements. It is designed for data that may be accessed once or twice per year and can tolerate retrieval times of 12 to 48 hours. This makes it ideal for compliance archives and long-term backup data.
Deep Archive costs significantly less than other storage classes, typically 75% less than S3 Glacier and over 95% less than S3 Standard. For data that must be retained but is unlikely to be accessed, these cost savings compound over years of storage. The storage class maintains the same durability as other S3 classes.
Data in Deep Archive is immediately available for restore requests, though the retrieval process takes hours rather than minutes. For compliance scenarios where immediate access is not required, this retrieval time is acceptable. You can initiate bulk retrieval for large amounts of data or expedited retrieval when faster access is needed.
S3 Standard is designed for frequently accessed data and is the most expensive storage class. Using Standard for rarely accessed historical data wastes significant budget that could be optimized by using archive storage classes. Standard provides millisecond access but this performance is unnecessary for compliance archives.
S3 Intelligent-Tiering automatically moves data between access tiers but adds a small monitoring fee per object. For data that is known to be rarely accessed, explicitly choosing Deep Archive avoids monitoring fees and provides predictable, lower costs. Intelligent-Tiering is better for data with unknown or changing access patterns.
S3 One Zone-IA stores data in a single availability zone, reducing costs compared to Standard-IA but providing lower durability and availability. For compliance data that must be retained reliably, using a storage class that replicates across multiple availability zones is more appropriate despite slightly higher costs.
Question 34
A company needs to build a data warehouse on AWS that supports complex analytical queries and can scale to petabytes of data. Which service should they use?
A) Amazon Redshift
B) Amazon RDS
C) Amazon DynamoDB
D) Amazon ElastiCache
Answer: A
Explanation:
Amazon Redshift is a fully managed petabyte-scale data warehouse service designed specifically for analytical workloads. It uses columnar storage and parallel query execution to deliver fast performance on complex queries across large datasets. Redshift can scale from gigabytes to petabytes and supports standard SQL for analytics.
Redshift’s architecture is optimized for aggregations, joins, and analytical functions commonly used in business intelligence and reporting. The service uses massively parallel processing to distribute query execution across multiple nodes, enabling complex queries to complete in seconds even on large datasets. This makes it ideal for data warehouse use cases.
Redshift integrates with BI tools like Tableau, PowerBI, and Amazon QuickSight through standard database connections. It supports advanced analytics including window functions, user-defined functions, and machine learning integration with SageMaker. Redshift also provides features like concurrency scaling to handle varying query workloads.
Amazon RDS is designed for transactional databases and has size limits that make it unsuitable for petabyte-scale data warehousing. RDS is optimized for row-based storage and OLTP workloads rather than analytical queries on large datasets. RDS does not provide the parallel processing architecture needed for data warehouse performance.
Amazon DynamoDB is a NoSQL database optimized for key-value and document access patterns with single-digit millisecond latency. It does not support complex SQL analytical queries or the join operations common in data warehouse workloads. DynamoDB is designed for different use cases than data warehousing.
Amazon ElastiCache is an in-memory caching service used to improve application performance by caching frequently accessed data. It is not a data warehouse and cannot store petabytes of data or serve as a primary analytical database.
Question 35
A data pipeline needs to validate JSON data against a schema before processing. Invalid data should be rejected and logged. Which approach should be used in AWS Glue?
A) Implement schema validation in Glue script with error handling
B) Process all data without validation
C) Manually check data before upload
D) Use only file size validation
Answer: A
Explanation:
AWS Glue scripts can implement JSON schema validation by loading the schema definition and validating each record against it. You can use Python libraries to parse JSON and check that required fields exist, data types match, and values meet defined constraints. Invalid records can be filtered out and written to an error bucket for investigation.
Implementing validation in the Glue script provides automated, consistent checking of all incoming data. You can define validation rules based on business requirements and technical constraints, ensuring only clean data proceeds through the pipeline. Error handling can log detailed information about validation failures for troubleshooting.
This approach allows you to implement multiple validation strategies including schema structure validation, data type checking, range validation, and business rule verification. Failed records can be counted and reported through CloudWatch metrics, providing visibility into data quality trends over time.
Processing all data without validation risks propagating errors through your pipeline, potentially corrupting downstream systems and reports. Bad data can cause processing failures, incorrect analytics results, and loss of trust in data products. Validation at ingestion prevents these issues from affecting downstream consumers.
Manually checking data before upload does not scale and introduces delays in data availability. Manual processes are error-prone and cannot provide the consistent, automated validation needed for production pipelines. This approach also creates operational bottlenecks as data volumes grow.
Using only file size validation checks a very limited aspect of data quality and does not ensure that JSON structure, data types, or values are correct. Files can be the right size but contain completely invalid data. Comprehensive validation requires checking the actual data content.
Question 36
A data engineer needs to copy 500 TB of data from an on-premises data center to Amazon S3. The network connection has limited bandwidth. What AWS service should be used?
A) AWS Snowball
B) AWS DataSync over internet
C) AWS Direct Connect only
D) AWS Transfer Family
Answer: A
Explanation:
AWS Snowball is a physical data transfer device designed for moving large amounts of data into AWS when network transfer is impractical. For 500 TB of data over a limited bandwidth connection, network transfer could take weeks or months. Snowball can transfer the same data in days by shipping a physical device to your location.
You load data onto the Snowball device in your data center, then ship it back to AWS where the data is transferred to S3. Snowball devices provide 80 TB of usable storage, so multiple devices can be used in parallel to speed up large transfers. The devices include encryption and tracking for security.
Snowball is cost-effective for large data migrations because you avoid the costs and time associated with consuming bandwidth for weeks. The service includes the device rental, shipping, and data transfer into S3 in a predictable price. This makes budget planning easier compared to uncertain network transfer costs.
AWS DataSync over internet would struggle with 500 TB of data on a limited bandwidth connection. Depending on available bandwidth, the transfer could take many weeks and consume the connection continuously, impacting other applications. Network interruptions would also disrupt the transfer process.
AWS Direct Connect provides dedicated network connectivity between your data center and AWS but requires time to provision and monthly costs for the connection. While Direct Connect improves transfer speed and reliability, for a one-time migration of 500 TB, Snowball is more cost-effective and faster to implement.
AWS Transfer Family provides SFTP/FTP endpoints for S3 but does not solve the bandwidth limitation problem. Transferring 500 TB through Transfer Family would still require weeks of network transfer time and would be limited by the same bandwidth constraints.
Question 37
A company stores sensor data in Amazon S3 and needs to run SQL queries that join this data with reference data in Amazon RDS. What service enables querying both sources together?
A) Amazon Redshift Spectrum
B) Amazon Athena alone
C) AWS Glue DataBrew
D) Amazon QuickSight only
Answer: A
Explanation:
Amazon Redshift Spectrum allows you to query data in S3 directly from Redshift while also joining it with tables stored in Redshift. You can load reference data from RDS into Redshift tables, then use Spectrum to query sensor data in S3 and join it with the reference tables in a single SQL query.
Spectrum extends Redshift’s query capability to exabytes of data in S3 without requiring data to be loaded into Redshift. The service automatically scales compute resources to execute S3 queries in parallel while leveraging Redshift’s query optimizer to efficiently process joins between S3 and Redshift tables.
This approach allows you to keep historical sensor data cost-effectively in S3 while maintaining frequently accessed reference data in Redshift for fast access. Queries can seamlessly combine data from both sources, providing a unified view without complex ETL processes to consolidate everything in one location.
Amazon Athena can query S3 data but does not directly connect to or join with data in Amazon RDS. While you could potentially use federated queries, Athena is not designed for joining large volumes of S3 data with transactional data in RDS. This would require additional complexity compared to the Spectrum approach.
AWS Glue DataBrew is a visual data preparation tool for cleaning and normalizing data. It does not provide SQL query capabilities for joining data from multiple sources in real-time. DataBrew is used for data transformation tasks before loading data into analytical systems.
Amazon QuickSight is a visualization tool that can connect to multiple data sources but relies on underlying query engines. QuickSight would need an engine like Redshift Spectrum to actually execute queries that join S3 and RDS data.
Question 38
A data pipeline processes real-time financial transactions and must guarantee exactly-once processing to prevent duplicate transactions. Which AWS service combination provides this capability?
A) Amazon Kinesis Data Streams with checkpoint tracking
B) Amazon SQS Standard
C) Amazon SNS only
D) Amazon EventBridge without deduplication
Answer: A
Explanation:
Amazon Kinesis Data Streams provides sequence numbers and checkpoint tracking that enable exactly-once processing semantics when properly implemented. The Kinesis Client Library automatically manages checkpoints, tracking which records have been processed. If a failure occurs, processing resumes from the last checkpoint, preventing duplicates.
For financial transactions where accuracy is critical, you combine Kinesis checkpointing with idempotent processing logic. Each transaction has a unique identifier, and your processing logic checks for duplicates before applying changes. This combination of streaming infrastructure and application design ensures each transaction is processed exactly once.
Kinesis maintains record ordering within shards and provides strong durability guarantees by replicating data across multiple availability zones. The service retains records for up to 365 days, giving you time to recover from failures without data loss. Enhanced fan-out consumers receive dedicated throughput for reliable processing.
Amazon SQS Standard provides at-least-once delivery, meaning messages may be delivered multiple times. For financial transactions, this could result in duplicate processing unless extensive deduplication logic is implemented at the application level. SQS FIFO queues provide exactly-once processing but have lower throughput limits than Kinesis.
Amazon SNS is a pub-sub messaging service that does not provide exactly-once delivery guarantees. SNS is designed for fan-out scenarios where messages are delivered to multiple subscribers, but it does not track processing state or prevent duplicate deliveries to ensure exactly-once processing.
Amazon EventBridge without explicit deduplication configuration provides at-least-once delivery. While EventBridge can detect duplicate events, this requires configuration and is not automatic. For critical financial processing, Kinesis provides more robust exactly-once processing capabilities.
Question 39
A data engineer needs to monitor AWS Glue job performance and receive alerts when job duration exceeds normal thresholds. What should be configured?
A) CloudWatch alarms on Glue job metrics
B) Manual log review daily
C) Email reports from team members
D) No monitoring needed
Answer: A
Explanation:
AWS Glue publishes job metrics to CloudWatch including job duration, DPU hours, and success/failure status. You can create CloudWatch alarms that trigger when job duration exceeds defined thresholds, sending notifications through SNS to email, SMS, or other endpoints. This automated monitoring ensures immediate awareness of performance issues.
CloudWatch alarms can be configured with dynamic thresholds based on historical performance or static values based on SLA requirements. Anomaly detection can identify when job duration deviates from normal patterns, catching performance degradation before it impacts downstream systems. Alarms can also trigger automated responses like Lambda functions to investigate or remediate issues.
By monitoring multiple metrics simultaneously, you gain comprehensive visibility into job health. Track not just duration but also data processed, errors encountered, and resource utilization. This holistic view helps identify whether performance issues stem from data volume changes, code problems, or infrastructure constraints.
Manual log review daily introduces significant delays in identifying problems and does not scale as the number of jobs grows. By the time daily review occurs, jobs may have been failing or underperforming for hours, impacting downstream processes. Manual review also requires dedicated staff time that could be better spent on proactive improvements.
Email reports from team members are subjective, inconsistent, and do not provide the automated, objective monitoring needed for production systems. This approach depends on individuals remembering to check and report, creating gaps in coverage. It also lacks the detailed metrics needed to diagnose specific performance issues.
Operating without monitoring is unacceptable for production data pipelines. Without monitoring, you have no visibility into job performance, cannot detect failures promptly, and cannot identify trends that indicate emerging problems. Monitoring is essential for maintaining reliable data pipelines.
Question 40
A company wants to enable data analysts to explore data in S3 using SQL without requiring database administration skills. Which service should they use?
A) Amazon Athena
B) Amazon EC2 with PostgreSQL
C) Self-managed Hadoop cluster
D) Amazon RDS with manual loading
Answer: A
Explanation:
Amazon Athena is a serverless query service that requires zero database administration. Analysts can start querying data in S3 immediately using standard SQL without provisioning infrastructure, managing databases, or performing complex setup. Athena automatically handles query execution, scaling, and optimization.
Athena uses the AWS Glue Data Catalog to store table metadata, which can be automatically populated by Glue crawlers. Once tables are defined, analysts can query them using familiar SQL syntax through the Athena console, JDBC/ODBC connections, or BI tools. This simplicity makes data accessible to users without technical database skills.
The serverless nature means analysts pay only for queries run, with no idle infrastructure costs. Athena scales automatically to handle concurrent users and varying query workloads. There are no servers to patch, no capacity planning, and no performance tuning required, allowing analysts to focus on insights rather than infrastructure.
Using Amazon EC2 with PostgreSQL requires setting up and managing database servers, including installation, configuration, security patching, backup management, and capacity planning. This approach demands database administration skills and ongoing operational effort, which contradicts the requirement of avoiding database administration.
A self-managed Hadoop cluster requires extensive expertise in Hadoop ecosystem tools, cluster management, and distributed systems. This is one of the most operationally complex options and requires specialized skills far beyond basic database administration. It does not meet the requirement of enabling easy exploration without technical skills.
Amazon RDS with manual loading requires database administration including schema design, data loading processes, query optimization, and capacity management. While RDS reduces some operational burden compared to self-managed databases, it still requires significant technical knowledge and does not work directly with data in S3.
