AWS Certified Data Engineer - Associate DEA-C01 Exam Dumps & Practice Test Questions
Question 1:
A company is developing an analytics platform that uses Amazon S3 for storage and Amazon Redshift for data warehousing. To improve performance and reduce the overhead of importing data into Redshift, they intend to use Amazon Redshift Spectrum to query data directly from S3.
What are the two best practices the company should follow to ensure fast and efficient query execution using Redshift Spectrum?
A. Compress data files using gzip and ensure they are between 1 GB and 5 GB in size.
B. Store the data in a columnar format such as Parquet or ORC.
C. Partition the data in Amazon S3 based on the most frequently queried columns.
D. Split the data into many small files, each less than 10 KB in size.
E. Use file formats that cannot be split during query execution.
Answer: B, C
Explanation:
When using Amazon Redshift Spectrum, queries are pushed down to S3, and the underlying storage layout and file format significantly impact query performance. Redshift Spectrum is designed to efficiently read large datasets directly from S3 without requiring the data to be loaded into Redshift tables. To optimize performance, two key best practices should be followed:
1. Store the data in a columnar format such as Parquet or ORC (Option B):
Columnar storage formats like Parquet and ORC are highly optimized for analytical queries that access a subset of columns from large datasets. These formats enable Redshift Spectrum to read only the necessary columns instead of scanning the entire file. This results in less I/O, faster query performance, and reduced costs because data scanning charges are based on the amount of data read.
Moreover, both Parquet and ORC support compression and schema evolution, which are essential for performance and flexibility in analytics platforms. Using a columnar format is arguably the most impactful optimization for data lake analytics with Spectrum.
2. Partition the data in Amazon S3 based on the most frequently queried columns (Option C):
Partitioning organizes the data in S3 into a directory structure based on the values of specific columns (e.g., year=2025/month=05/). When queries are filtered on these partition columns, Redshift Spectrum can perform partition pruning, reading only the relevant partitions and skipping the rest.
This significantly reduces the amount of data scanned, which not only accelerates queries but also lowers cost. The effectiveness of partitioning depends on choosing the right columns—typically those used frequently in filters or join conditions.
Now let’s examine why the other options are less effective or counterproductive:
A. Compress data files using gzip and ensure they are between 1 GB and 5 GB in size:
While compressing data is generally beneficial, gzip is not a splittable compression format, meaning Redshift Spectrum cannot parallelize reading a single large file compressed with gzip. This negates performance benefits for large-scale analytics. Instead, splittable compression formats like Snappy (used with Parquet) are preferred. Additionally, while 1 GB to 5 GB file sizes are optimal for many big data systems, this recommendation becomes ineffective when paired with gzip's limitations.D. Split the data into many small files, each less than 10 KB in size:
Having too many small files is a well-known anti-pattern in big data systems, including Redshift Spectrum. Each file carries overhead during processing (e.g., open file handles, task initialization), which can severely degrade performance. Instead, data should be consolidated into fewer, larger files, ideally between 128 MB and 1 GB each, especially when using columnar formats.E. Use file formats that cannot be split during query execution:
This is clearly not a best practice. Using non-splittable formats like CSV with gzip compression restricts Redshift Spectrum’s ability to parallelize data reading, which leads to poor performance. The best practice is to use splittable file formats such as Parquet or ORC with suitable compression schemes (like Snappy).
In summary, for optimal performance with Redshift Spectrum, the company should:
Use columnar storage formats like Parquet or ORC.
Implement partitioning in S3 using the most commonly filtered columns.
These strategies ensure efficient data scanning, lower costs, and faster query execution across their analytics platform.
Question 2:
A company uses Amazon RDS to manage its transactional data. The RDS instance is located in a private subnet within a VPC, meaning it is not accessible from the public internet. A developer has created an AWS Lambda function using default settings to perform insert, update, and delete operations on the RDS database.
What are the two best actions the developer should take to ensure that the Lambda function can privately access the RDS instance, while minimizing operational complexity and maintaining strong security practices?
A. Enable public access for the RDS DB instance.
B. Modify the RDS instance’s security group to only allow access from the Lambda function on the database port.
C. Ensure the Lambda function runs within the same VPC subnet as the RDS instance.
D. Attach the same security group to both the Lambda function and the RDS DB instance, and add a self-referencing rule to allow traffic on the database port.
E. Modify the network ACLs of the private subnet to allow traffic for the database port.
Answer: B, D
Explanation:
When using AWS Lambda to connect to an Amazon RDS instance in a private subnet, you must ensure that the Lambda function is correctly configured to operate within the Virtual Private Cloud (VPC) and has network-level access to the RDS instance. Because the RDS instance is not publicly accessible, all communication must occur through private network interfaces within the VPC.
To achieve secure and private communication with minimal operational overhead, two critical steps should be taken:
1. Modify the RDS instance’s security group to only allow access from the Lambda function on the database port (Option B):
Security groups are the first line of defense in controlling access to AWS resources. RDS instances typically listen on a port such as 3306 (MySQL) or 5432 (PostgreSQL). By limiting the RDS security group to accept inbound traffic only from the Lambda function’s security group on the relevant port, you enforce least-privilege access. This ensures that only authorized Lambda functions can connect to the database.
Using security groups for access control is a best practice because they are stateful and easier to manage than network ACLs. This approach avoids having to open unnecessary ports to a wide range of IP addresses or services.
2. Attach the same security group to both the Lambda function and the RDS DB instance, and add a self-referencing rule to allow traffic on the database port (Option D):
When a Lambda function is configured to run inside a VPC, it can be attached to one or more security groups. By assigning the same security group to both the Lambda function and the RDS instance, and then adding an inbound rule in the security group that allows traffic from itself on the database port, you create a simple, secure connection model.
This strategy ensures that only resources in the same security group can talk to one another. It reduces operational complexity by eliminating the need to maintain multiple security group references, and it maintains strong security boundaries.
Why the other options are incorrect:
A. Enable public access for the RDS DB instance:
Enabling public access is against security best practices when dealing with sensitive data and private VPC resources. Making an RDS instance publicly accessible increases the attack surface and goes against the goal of private communication.C. Ensure the Lambda function runs within the same VPC subnet as the RDS instance:
While it’s true that both resources must reside in the same VPC (or connected VPCs via VPC peering), they do not need to be in the same subnet. In fact, AWS recommends deploying Lambda functions in multiple subnets across different Availability Zones for high availability. What matters is that the Lambda function is configured to run within the same VPC and that routing and security groups allow traffic.E. Modify the network ACLs of the private subnet to allow traffic for the database port:
Although network ACLs can control subnet-level traffic, they are generally not the preferred method for resource-to-resource traffic control within a VPC. Network ACLs are stateless and harder to manage compared to security groups. In most cases, security groups provide sufficient and more manageable access control, especially when dealing with Lambda-to-RDS communication.
To securely and efficiently allow a Lambda function to access an RDS instance in a private subnet, the developer should:
Configure the RDS security group to permit inbound access from the Lambda function (Option B).
Use the same security group for both Lambda and RDS and apply self-referencing rules for internal communication (Option D).
These steps achieve private connectivity, reduce operational complexity, and align with AWS security best practices.
Question 3:
A company has a frontend application built using ReactJS that communicates with backend REST APIs through Amazon API Gateway. A data engineer needs to deploy a Python script that will occasionally be triggered via API Gateway and must return its output after execution. The solution should be implemented with minimal operational complexity and maintenance overhead.
What is the most efficient and low-maintenance approach?
A. Deploy the Python script on an Amazon Elastic Container Service (ECS) cluster.
B. Create an AWS Lambda function written in Python and configure provisioned concurrency.
C. Deploy the Python script on Amazon Elastic Kubernetes Service (EKS) integrated with API Gateway.
D. Create an AWS Lambda function and periodically invoke it every 5 minutes using Amazon EventBridge with mock events to keep the function warm.
Answer: B
Explanation:
The core requirement here is to execute a Python script occasionally via an API Gateway invocation, with the response returned synchronously. The solution must be efficient and low-maintenance, which immediately suggests using serverless technologies that minimize infrastructure management. Let’s analyze each option in detail.
Why Option B is correct:
Creating an AWS Lambda function in Python and connecting it to API Gateway is the most straightforward, serverless solution to this problem. Lambda is designed to execute code in response to events and is natively integrated with API Gateway. When invoked through an HTTP request, the Lambda function can process the payload and return the response directly to the API Gateway, which in turn sends it back to the client (e.g., the ReactJS frontend).
By enabling provisioned concurrency, the engineer ensures that the Lambda function is always pre-initialized and ready to respond quickly, avoiding cold starts that might otherwise delay execution when the function is invoked infrequently. Provisioned concurrency maintains a pre-warmed pool of Lambda instances, which is ideal for use cases where latency matters but traffic is sporadic.
This approach provides:
Minimal operational overhead: No infrastructure provisioning, patching, or scaling management.
High availability and fault tolerance: Managed by AWS.
Seamless integration: Direct support with API Gateway.
Security and scalability: Built-in features without needing custom solutions.
Why the other options are less suitable:
A. Deploy the Python script on an Amazon Elastic Container Service (ECS) cluster:
While ECS is a container-based solution that supports running Python scripts, it requires managing container tasks, clusters, and networking. Even with Fargate (serverless compute for containers), this adds operational complexity compared to using Lambda. ECS is more appropriate for persistent or complex containerized services rather than occasionally invoked functions.C. Deploy the Python script on Amazon Elastic Kubernetes Service (EKS):
EKS is a highly complex, fully managed Kubernetes service, which demands substantial effort for setup, configuration, scaling, and security. For a simple Python script executed occasionally, deploying on EKS is overkill and significantly increases operational burden. EKS is best suited for teams already deeply invested in Kubernetes.D. Create an AWS Lambda function and periodically invoke it every 5 minutes using Amazon EventBridge with mock events to keep the function warm:
This workaround aims to avoid cold starts by keeping the Lambda function "warm" through periodic invocations. However, it is inefficient and unnecessary when provisioned concurrency (Option B) is available. Using EventBridge for warming adds recurring cost, complexity, and noise without the reliability of guaranteed low latency that provisioned concurrency provides.
To execute a Python script occasionally via API Gateway and return the result with minimal operational effort, the ideal solution is to use AWS Lambda with provisioned concurrency. This approach leverages fully managed, serverless architecture, supports direct integration with API Gateway, and avoids the complexity of container orchestration or warming hacks — making Option B the best choice.
Question 4:
A company has its main applications in a production AWS account and uses a separate security account to store and analyze security logs. These logs are generated and stored in Amazon CloudWatch Logs within the production account. The company wants to stream these security logs from the production account to the security account using Amazon Kinesis Data Streams, ensuring secure cross-account access and best practices.
Which solution meets these requirements securely and effectively?
A. Create a Kinesis Data Stream in the production account and an IAM role in the security account that allows cross-account permissions for Kinesis Data Streams in the production account.
B. Create a Kinesis Data Stream in the security account, then configure a trust policy for CloudWatch Logs in the security account to send data to the stream.
C. Create a Kinesis Data Stream in the production account and an IAM role in the production account with cross-account permissions to Kinesis Data Streams in the security account.
D. Create a Kinesis Data Stream in the security account, then configure a trust policy for CloudWatch Logs in the production account to send data to the stream.
Answer: D
Explanation:
The scenario here involves streaming security logs from CloudWatch Logs in the production account to a Kinesis Data Stream in a security account. To achieve this securely and follow best practices, the solution must account for both cross-account permissions and ensuring that CloudWatch Logs can stream data to Kinesis in the target security account. Let’s examine the options:
Why Option D is correct:
Kinesis Data Stream in the security account: The security account will own the Kinesis Data Stream because it is responsible for storing and analyzing the logs. This aligns with the principle of separation of duties, where the security account is independent and centralized.
Trust policy for CloudWatch Logs in the production account: The key here is that CloudWatch Logs in the production account needs permission to send logs to the Kinesis stream in the security account. By setting up a trust policy for CloudWatch Logs in the production account, you allow this cross-account interaction. This ensures that CloudWatch Logs in the production account have the appropriate permissions to put logs into the Kinesis stream in the security account.
This solution is effective because:
It ensures secure cross-account access with appropriate IAM roles and policies.
It follows the best practice of centralizing security logs in a separate security account.
The trust policy ensures only CloudWatch Logs in the production account can send data to the Kinesis stream in the security account, maintaining security controls.
Why the other options are less suitable:
A. Create a Kinesis Data Stream in the production account and an IAM role in the security account that allows cross-account permissions for Kinesis Data Streams in the production account:
This option suggests creating the Kinesis Data Stream in the production account and managing permissions in the security account. However, the security account should be the destination of the logs, not the production account. This solution does not align with the requirement to store and analyze logs centrally in the security account.B. Create a Kinesis Data Stream in the security account, then configure a trust policy for CloudWatch Logs in the security account to send data to the stream:
The security account can have the Kinesis Data Stream, but CloudWatch Logs in the production account cannot directly send logs to a stream in another account without setting up appropriate permissions. The trust policy here is incorrectly placed — the correct place for the trust policy is in the production account, allowing CloudWatch Logs in the production account to send data to the Kinesis stream in the security account.C. Create a Kinesis Data Stream in the production account and an IAM role in the production account with cross-account permissions to Kinesis Data Streams in the security account:
While this solution uses a Kinesis Data Stream in the production account, it incorrectly places the Kinesis Data Stream where the logs should be analyzed and stored. The logs should be sent to the security account for centralized management and analysis, so the Kinesis stream should reside in the security account.
The most effective and secure solution is to create a Kinesis Data Stream in the security account (where logs are stored and analyzed) and then set up a trust policy for CloudWatch Logs in the production account to allow it to send logs to the Kinesis stream in the security account. This ensures secure cross-account access and aligns with the best practice of centralizing security logs in a dedicated security account. Thus, Option D is the correct choice.
Question 5:
A company uses Amazon S3 to manage its transactional data lake, which contains semi-structured JSON files. Daily snapshots of data are provided by the source system, with some files being small and others as large as tens of terabytes. The data engineer is tasked with implementing a Change Data Capture (CDC) strategy to only ingest new or modified data daily. The solution must be cost-effective, reduce unnecessary data storage, and integrate seamlessly with the existing S3-based data lake.
Which approach is most cost-effective for detecting and ingesting the changed data?
A. Implement an AWS Lambda function to compare the current and previous JSON snapshots and only ingest the differences into the data lake.
B. Load the full snapshot into Amazon RDS for MySQL and use AWS Database Migration Service (DMS) to send changes to the data lake.
C. Use an open-source table format such as Apache Hudi, Delta Lake, or Apache Iceberg to directly merge new JSON files with existing S3 data, identifying and storing only new or updated records.
D. Load the snapshot into an Aurora MySQL DB instance running Aurora Serverless, and use AWS DMS to capture and send changes to the data lake.
Answer: C
Explanation:
In this case, the task is to implement a Change Data Capture (CDC) strategy for efficiently managing new or modified data in a data lake on Amazon S3. The goal is to make the process cost-effective, minimize unnecessary data storage, and integrate seamlessly with the current S3-based environment. Let’s analyze each option in detail.
Why Option C is correct:
The most cost-effective and efficient approach is to use an open-source table format such as Apache Hudi, Delta Lake, or Apache Iceberg. These frameworks are designed to manage large datasets in distributed data lakes and integrate well with Amazon S3. They support Change Data Capture functionality natively and allow the incremental processing of new or modified data.
Merging new data with existing S3 data: These table formats provide powerful capabilities for merging newly ingested data with existing data in the data lake. This avoids the need to reload the entire dataset, which would incur unnecessary storage costs.
Efficiently tracking changes: These formats use write-ahead logs (WAL) and metadata tracking to keep track of changes and incremental modifications. This makes it easy to identify and store only new or updated records, significantly reducing storage needs.
Cost-effective: By focusing on incremental data ingestion, these frameworks eliminate the need for full dataset reloads, which saves on both compute and storage costs.
Seamless integration with S3: These open-source formats are well-suited for use with Amazon S3, and tools like AWS Glue and Amazon Athena can integrate directly with them to query the data efficiently.
This approach directly addresses the need to ingest only new or modified data while minimizing storage costs and complexity.
Why the other options are less suitable:
A. Implement an AWS Lambda function to compare the current and previous JSON snapshots and only ingest the differences into the data lake:
While this solution sounds feasible, it would involve significant complexity in handling large datasets, especially tens of terabytes of data. Lambda functions are also not ideal for processing large datasets due to memory and timeout limits, and the logic to compare snapshots can be both time-consuming and prone to errors. Additionally, this would increase operational overhead because of the need for custom code to track changes and handle all data merging.B. Load the full snapshot into Amazon RDS for MySQL and use AWS Database Migration Service (DMS) to send changes to the data lake:
This option introduces an additional relational database layer (RDS for MySQL), which is not necessary for handling data that resides in S3. Using DMS in this context would involve extra complexity and costs (both for the RDS instance and DMS) that are unnecessary when dealing with large-scale datasets in S3. The CDC requirement is better served by native integration tools with the data lake, like Apache Hudi, Delta Lake, or Apache Iceberg.D. Load the snapshot into an Aurora MySQL DB instance running Aurora Serverless, and use AWS DMS to capture and send changes to the data lake:
Similar to Option B, this solution involves introducing Aurora MySQL (an RDS-compatible database service) and using DMS to push changes to the data lake. This approach is overcomplicated and introduces more cost and complexity for a simple CDC scenario with S3. The data already resides in JSON format in S3, and integrating with a relational database would add unnecessary complexity without significant benefits.
The most efficient, cost-effective, and low-maintenance approach to implementing Change Data Capture for the S3-based data lake is to use open-source table formats like Apache Hudi, Delta Lake, or Apache Iceberg. These frameworks integrate directly with S3, allow for incremental data ingestion, and track changes efficiently, reducing storage costs and operational complexity. Therefore, Option C is the best choice.
Question 6:
A data engineer is running queries on a large dataset stored in Amazon S3 using Amazon Athena. The queries use metadata from the AWS Glue Data Catalog. Recently, the engineer noticed that the query performance is degrading, especially during the query planning phase. Upon investigation, the engineer finds that the root cause of the slow performance is the large number of partitions in the S3 bucket, which results in longer query planning times.
Which TWO solutions can help optimize Athena query performance by reducing partition overhead?
A. Implement a partition index in AWS Glue and enable partition filtering in Athena.
B. Use bucketing in Athena based on a commonly queried column.
C. Implement partition projection in Athena, leveraging the S3 bucket’s prefix structure.
D. Convert the data in the S3 bucket to Apache Parquet format.
E. Use Amazon EMR S3DistCP to consolidate smaller S3 files into larger ones.
Answer: C, D
Explanation:
The situation described here involves performance degradation in Amazon Athena due to the large number of partitions in an S3 bucket. When querying a large number of partitions, the query planning phase can take much longer, which slows down query execution. Therefore, the focus should be on optimizing how Athena interacts with the partitions and how the data is stored to minimize unnecessary overhead. Let's evaluate the options.
Why Option C is correct:
Implement partition projection in Athena, leveraging the S3 bucket’s prefix structure:
Partition projection is a feature in Athena that allows you to optimize how partitions are handled when querying data stored in S3. Instead of scanning all the partitions, partition projection allows Athena to dynamically determine partition values based on the structure of the S3 object keys (prefixes). This reduces the time spent in the query planning phase.
For example, if your S3 bucket’s prefix structure is based on a time-based partitioning scheme (e.g., s3://bucket/year=2021/month=01/day=01/), Athena can use this prefix pattern to infer partition values without needing to load all partitions from the Glue Data Catalog. This drastically reduces the query planning time.
This technique is particularly useful when there are many partitions, as it avoids the overhead of managing a large number of partitions in the Glue Data Catalog.
Why Option D is correct:
Convert the data in the S3 bucket to Apache Parquet format:
Apache Parquet is a columnar storage format that is highly optimized for query performance in systems like Athena. Converting data from JSON, CSV, or other row-based formats into Parquet can lead to significant performance improvements because:
Columnar storage allows Athena to scan only the necessary columns for a query, reducing the amount of data read and processed.
Parquet also supports predicate pushdown, meaning that filters (e.g., WHERE clauses) are applied at the data scan level, further reducing the amount of data processed.
Compression in Parquet helps reduce the amount of data read from S3, making queries more efficient.
While this change won’t reduce the number of partitions directly, it optimizes how the data is accessed, which can reduce overall query execution time.
Why the other options are less suitable:
A. Implement a partition index in AWS Glue and enable partition filtering in Athena:
While partition filtering is useful in Athena to restrict the data queried based on partition keys, partition indexing isn’t directly available in AWS Glue for optimizing Athena queries. Partition filtering, however, would work more effectively if combined with other optimizations like partition projection. So, while partition filtering is important, it does not directly address the root cause of partition overhead in the Glue Data Catalog in this context.B. Use bucketing in Athena based on a commonly queried column:
Bucketing is useful in certain scenarios to optimize joins and grouping operations in Athena, but it does not specifically address the issue of partition management in Athena when there is a large number of partitions. Bucketing may be useful if the engineer wants to optimize specific queries involving aggregation or joins, but it does not reduce partition overhead during query planning.E. Use Amazon EMR S3DistCP to consolidate smaller S3 files into larger ones:
While consolidating smaller files into larger ones can reduce overhead in reading many small files (which is a different performance concern), it does not directly address the issue of partition management or query planning overhead due to a large number of partitions in Athena. The focus of the question is on partition overhead, and this solution is more relevant to optimizing file sizes for query efficiency rather than optimizing partition management.
The most effective solutions to optimize Athena query performance by reducing partition overhead are:
Implement partition projection (Option C) to reduce the number of partitions Athena needs to process during the query planning phase.
Convert data to Apache Parquet format (Option D) to optimize the data storage and access patterns, improving overall query performance.
Question 7:
A data engineer is designing a real-time analytics pipeline to process streaming data in AWS. The requirement is to perform time-based aggregations over a 30-minute window and ensure fault tolerance and high availability. The solution should also minimize operational overhead.
Given the need for real-time processing and low operational complexity, which AWS service is most suitable?
A. Use an AWS Lambda function that processes data and performs time-based aggregations on data from Amazon Kinesis Data Streams.
B. Use Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) to perform time-based aggregations and analyze the data.
C. Use an AWS Lambda function to process data and perform tumbling window aggregations based on event timestamps.
D. Use Amazon Managed Service for Apache Flink to perform time-based analytics using multiple types of aggregations on data within a 30-minute window.
Answer: B
Explanation:
The goal of this solution is to process streaming data in real-time, perform time-based aggregations over a 30-minute window, ensure fault tolerance, and minimize operational overhead. Given these requirements, let's evaluate each option.
Why Option B is correct:
Amazon Managed Service for Apache Flink (formerly known as Kinesis Data Analytics) is the most suitable service for this use case for several reasons:
Real-time stream processing: Apache Flink is a powerful, distributed stream processing engine that excels in real-time data processing. It natively supports time-based aggregations, including windowing techniques such as tumbling windows and sliding windows. This is ideal for aggregating data over a 30-minute time window.
Fault tolerance and high availability: Flink is designed with fault tolerance and high availability in mind. It handles state management and checkpointing to recover from failures, making it an ideal choice for real-time data processing where data loss is not acceptable.
Low operational overhead: As a fully managed service, Amazon Managed Service for Apache Flink handles infrastructure management, including scaling, monitoring, and fault tolerance, allowing the data engineer to focus on the application logic instead of operational complexity.
Multiple aggregation types: Apache Flink supports a variety of aggregation techniques and windowing options, which allows for flexibility when performing complex time-based aggregations, meeting the need to aggregate over a 30-minute window and beyond.
This service is a fully managed, scalable, and resilient solution that fits well for real-time streaming analytics with time-based aggregations, making it the best choice.
Why the other options are less suitable:
A. Use an AWS Lambda function that processes data and performs time-based aggregations on data from Amazon Kinesis Data Streams:
While AWS Lambda can be used to process data from Kinesis Data Streams, it is not ideal for complex time-based aggregations on large volumes of streaming data. Lambda functions have limitations such as execution time (maximum 15 minutes) and stateless execution, making them less suitable for handling time-based aggregations that span longer periods (such as 30-minute windows). Moreover, managing fault tolerance and ensuring high availability with Lambda requires additional infrastructure, which increases operational complexity.C. Use an AWS Lambda function to process data and perform tumbling window aggregations based on event timestamps:
Like option A, this option involves using AWS Lambda for performing tumbling window aggregations. However, Lambda functions are still not the best choice for real-time streaming analytics that require continuous, complex aggregation logic over larger time windows (e.g., 30 minutes). The need for persistent state and efficient management of large data volumes makes Lambda less optimal. While it can handle basic aggregations, scaling and fault tolerance would require additional management and make the solution more complex.D. Use Amazon Managed Service for Apache Flink to perform time-based analytics using multiple types of aggregations on data within a 30-minute window:
While this option also uses Amazon Managed Service for Apache Flink, which is a good choice for real-time analytics, it is not as specific as Option B in addressing the most suitable service for low operational complexity. Both options B and D would likely be equally valid for the use case, but Option B provides a more direct match to the original requirements of minimizing complexity and providing effective time-based aggregations. This option is slightly more generic in describing multiple types of aggregations, but it is still a valid solution.
Amazon Managed Service for Apache Flink is the best choice for real-time processing, time-based aggregations, high availability, and fault tolerance, while minimizing operational overhead. It is a fully managed service that scales and handles complex streaming data with ease.
Option B is the most suitable because it directly matches the requirements of processing streaming data, performing time-based aggregations, and reducing operational complexity.
Question 8:
A company is transitioning from Microsoft SQL Server on Amazon EC2 to Amazon RDS for SQL Server. The analytics team needs to export large datasets daily by performing SQL joins across multiple tables. These exports must be in Apache Parquet format and stored in Amazon S3.
What is the most operationally efficient solution to automate this extraction and transformation process?
A. Create a SQL view on the EC2-hosted SQL Server with the necessary data. Use an AWS Glue job to read from the view and export the data to Parquet format in S3, scheduling it to run daily.
B. Use SQL Server Agent on the EC2 instance to run a daily query that exports the data to CSV format, then trigger a Lambda function to convert the CSV to Parquet.
C. Create a SQL view on the EC2-hosted SQL Server. Run an AWS Glue crawler to read the view, then transform and store the data as Parquet in S3 using a Glue job, scheduled to run daily.
D. Write an AWS Lambda function that connects via JDBC to the SQL Server on EC2, fetches and transforms the data to Parquet format, and uploads it to S3. Use Amazon EventBridge to trigger the Lambda function daily.
Answer: A
Explanation:
The goal here is to automate the extraction and transformation process, exporting large datasets from an SQL Server (on EC2 or RDS) to Apache Parquet format and storing the data in Amazon S3. Let’s evaluate the options based on operational efficiency, ease of maintenance, and scalability.
Why Option A is correct:
Using AWS Glue for data extraction and transformation is the most operationally efficient solution for several reasons:
Glue’s Managed ETL Service: AWS Glue is a fully managed service designed specifically for ETL (Extract, Transform, Load) operations. It handles the data extraction, transformation, and loading to S3 with minimal operational overhead.
SQL View for Data Extraction: By creating a SQL view in SQL Server, you can encapsulate the necessary SQL joins and data logic into a single, reusable query. This simplifies the extraction process because the view can be queried directly by AWS Glue.
Direct Export to Parquet: Glue supports native conversion to Apache Parquet, which is a columnar storage format optimized for analytics. It can read data from SQL Server (via JDBC) and directly export the transformed data into Parquet format.
Automation and Scheduling: AWS Glue jobs can be easily scheduled to run daily via the AWS Glue console or AWS CloudWatch events, providing a seamless automation experience with minimal effort. Once the Glue job is configured, it handles the entire extraction and transformation process without needing to manually intervene.
This approach is scalable, fully managed, and minimizes operational overhead, making it the most efficient solution.
Why the other options are less suitable:
B. Use SQL Server Agent on the EC2 instance to run a daily query that exports the data to CSV format, then trigger a Lambda function to convert the CSV to Parquet:
This approach involves multiple steps and services:SQL Server Agent is used to export data to CSV, which introduces unnecessary complexity and adds overhead, especially when dealing with large datasets. CSV files are not as efficient for analytics as Parquet.
The Lambda function would then convert the CSV to Parquet, but this introduces additional complexity and the need for managing CSV files in S3.
This method is less efficient because you would be dealing with intermediate CSV files, which are bulky and slower to process compared to Parquet files. Additionally, the Lambda function would need to handle large amounts of data, which could be cumbersome.
C. Create a SQL view on the EC2-hosted SQL Server. Run an AWS Glue crawler to read the view, then transform and store the data as Parquet in S3 using a Glue job, scheduled to run daily:
While this option uses AWS Glue (which is a good choice for ETL), it introduces an unnecessary Glue crawler. Crawlers are typically used to discover schema information for new or unknown datasets, not for reading data from an existing SQL Server view. A Glue job can directly read from the view without the need for a crawler, making this option slightly more complicated than necessary. The crawler step is redundant and adds operational complexity.D. Write an AWS Lambda function that connects via JDBC to the SQL Server on EC2, fetches and transforms the data to Parquet format, and uploads it to S3. Use Amazon EventBridge to trigger the Lambda function daily:
While Lambda is a powerful tool for event-driven operations, using it in this case would require complex JDBC connections to the SQL Server, handling large datasets within the Lambda function’s resource constraints, and implementing data transformation logic. Lambda functions have limitations on execution time (maximum 15 minutes) and memory (maximum 10 GB), which could become a bottleneck when dealing with large datasets.Moreover, maintaining Lambda functions and managing the JDBC connection to SQL Server adds operational overhead. This makes it a less efficient choice for this use case compared to AWS Glue, which is designed to handle ETL tasks more efficiently.
The most operationally efficient solution for automating the extraction, transformation, and storage of large datasets from SQL Server to Apache Parquet in Amazon S3 is to use AWS Glue. It minimizes complexity by directly reading from SQL views, transforming the data, and exporting it to Parquet in S3, all with minimal maintenance overhead.
Question 9:
A company uses Amazon S3 to store large volumes of transactional data in JSON format. The data engineer wants to implement a real-time streaming solution to detect and process changes (insertions or updates) as they occur, with the goal of reducing the volume of data being ingested and improving processing efficiency.
Which solution is most effective for implementing Change Data Capture (CDC) in this scenario?
A. Use AWS Lambda to process the data from S3 and compare current and previous JSON files to capture changes.
B. Implement AWS Glue to read from S3, identify changes, and load the changed data into the data lake.
C. Use Amazon Kinesis Data Streams to ingest the transactional data in real-time and process it with AWS Lambda.
D. Use Amazon Redshift Spectrum to query data directly from S3 and perform CDC operations.
Answer: C
Explanation:
The scenario calls for a real-time streaming solution to detect and process changes, focusing on inserting or updating data and reducing the volume of ingested data. For implementing Change Data Capture (CDC) in this situation, let’s break down the options and see why Option C is the best solution.
Why Option C is correct:
Amazon Kinesis Data Streams is specifically designed for handling real-time data streaming. It allows you to ingest transactional data in real-time and process it immediately to detect changes as they occur. Here's why it’s the best choice:
Real-time data ingestion: Kinesis Data Streams is built for capturing and streaming data continuously. This is a key requirement since the data engineer wants to detect and process changes as they occur in near real time.
CDC: By streaming data through Kinesis, you can track and capture insertions and updates to your transactional data in real-time. This minimizes the volume of data ingested since only the changes are captured and processed.
Efficient processing: You can use AWS Lambda in combination with Kinesis Data Streams to process incoming data and implement custom logic for detecting changes, aggregating, and filtering out unnecessary data. This is more efficient than periodically scanning large static files, and it minimizes operational overhead.
Scalability: Kinesis Data Streams is highly scalable and can handle large volumes of data, which is essential given the large datasets stored in S3.
Why the other options are less suitable:
A. Use AWS Lambda to process the data from S3 and compare current and previous JSON files to capture changes:
While AWS Lambda is an excellent tool for processing data, using it to compare current and previous JSON files in S3 adds complexity and inefficiency. Each time a new file is uploaded to S3, you would need to compare it against the previous version, which is not an optimal approach for real-time CDC. Also, Lambda functions are not designed for managing large files or conducting complex comparisons efficiently at scale, making this solution less effective for real-time change detection.B. Implement AWS Glue to read from S3, identify changes, and load the changed data into the data lake:
While AWS Glue is a great service for ETL tasks and can process data from S3, it is not designed specifically for real-time streaming. AWS Glue jobs are typically run in batches and are better suited for periodic processing rather than real-time data capture. For CDC, a streaming solution like Kinesis is more appropriate for efficiently processing changes as they happen.D. Use Amazon Redshift Spectrum to query data directly from S3 and perform CDC operations:
Amazon Redshift Spectrum allows you to query data in S3, but it is primarily designed for data analytics and querying large datasets rather than for real-time change detection. Redshift Spectrum is not specifically optimized for handling real-time streaming data or capturing changes on a transactional basis. It also involves more overhead for querying and processing large datasets, making it less suitable for this use case.
The best solution for real-time Change Data Capture (CDC) is to use Amazon Kinesis Data Streams in combination with AWS Lambda. This setup provides the capability to stream data in real-time, process changes as they occur, and efficiently capture insertions and updates with minimal overhead. It is designed to handle high volumes of data while maintaining low-latency processing, which makes it the ideal choice in this scenario.
Question 10:
A company wants to run large-scale machine learning workloads on its data stored in Amazon S3. They need a managed solution that allows them to run ML models in parallel across large datasets, with minimal infrastructure management. The solution should scale automatically based on the size of the data.
Which AWS service would be most suitable for this requirement?
A. Amazon SageMaker with built-in distributed training capabilities.
B. AWS Lambda with an event-driven architecture for ML inference.
C. Amazon EMR with Apache Spark for distributed ML processing.
D. Amazon EC2 with Spot Instances to run ML workloads in parallel.
Answer: A
Explanation:
When considering a solution to run large-scale machine learning (ML) workloads with minimal infrastructure management and automatic scaling, the most suitable option is Amazon SageMaker with its built-in distributed training capabilities. Here's a detailed breakdown of the options:
Why Option A is correct:
Amazon SageMaker is a fully managed service specifically designed for building, training, and deploying machine learning models at scale. It is ideal for the scenario where large-scale ML workloads need to be run in parallel across large datasets stored in Amazon S3. Here's why it’s the best choice:
Managed Solution: SageMaker is a fully managed service that abstracts away the complexity of infrastructure management. The company does not need to worry about provisioning or managing servers, as SageMaker handles the underlying infrastructure, making it easy to scale up or down based on workload requirements.
Built-in Distributed Training: SageMaker supports distributed training, which allows the model to scale automatically across multiple instances for faster processing of large datasets. It can handle parallelism in model training without the need for manually configuring or managing clusters. This fits perfectly with the need to run ML models in parallel over large datasets.
Integration with S3: SageMaker integrates seamlessly with Amazon S3, where the company stores its datasets. Data can be easily accessed for training directly from S3, and there are built-in tools to monitor the progress and performance of models.
Automatic Scaling: SageMaker automatically scales the resources required to train the model, adjusting compute capacity based on the size of the data and the complexity of the model. This auto-scaling feature ensures that the company only pays for the compute resources it needs, optimizing cost efficiency.
Why the other options are less suitable:
B. AWS Lambda with an event-driven architecture for ML inference:
AWS Lambda is best suited for small, stateless operations that respond to events. While Lambda can perform ML inference, it is not designed for large-scale, distributed training of models. ML workloads, especially those involving large datasets, would quickly exceed Lambda's execution time and memory limits. Additionally, Lambda does not provide a managed solution for parallel ML training at scale, making it unsuitable for this use case.C. Amazon EMR with Apache Spark for distributed ML processing:
Amazon EMR (Elastic MapReduce) is a powerful service for distributed data processing, typically using Apache Spark or Hadoop. While EMR can be used for distributed ML tasks, it requires more infrastructure management compared to SageMaker. The company would need to manage the EMR clusters, scaling, and configuration, which increases operational overhead. Moreover, it is not specifically designed for ML model training and does not provide the same high-level managed ML training capabilities as SageMaker.D. Amazon EC2 with Spot Instances to run ML workloads in parallel:
EC2 Spot Instances can be used to run ML workloads in parallel, but this requires manual setup and management of the infrastructure. It lacks the managed, high-level tools and auto-scaling features that SageMaker offers. Additionally, Spot Instances can be terminated by AWS at any time if capacity is needed for On-Demand instances, which may cause interruptions in long-running ML training jobs, making it less reliable for large-scale training jobs compared to SageMaker.
For running large-scale machine learning workloads on data stored in Amazon S3 with minimal infrastructure management, Amazon SageMaker is the most suitable solution. It provides a fully managed environment for training ML models, automatic scaling, and integration with S3, making it ideal for handling large datasets efficiently.
