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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q52-Q57):

NEW QUESTION # 52
A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AW5 Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Dairy.csv in a second 53 bucket.
Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day's CSV file.
A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid.
Which solution will meet these requirements with the LEAST effort?

• A. Invoke an AWS Lambda function to check the file for missing data and to fill in missing values in required fields.
• B. Run a SQL query in Amazon Athena to read the CSV file and drop missing rows. Copy the corrected CSV file to the second S3 bucket.
• C. Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field.
• D. Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required files and empty files.

Answer: D

Explanation:
* Problem Analysis:
* The company runs adaily AWS Glue ETL pipelineto clean and transform files received in an S3 bucket.
* If a file isincomplete or empty, the previous day's file should be retained.
* Need a solution to validate files before overwriting the existing file.
* Key Considerations:
* Automate data validation with minimal human intervention.
* Use built-in AWS Glue capabilities for ease of integration.
* Ensure robust validation for missing or incomplete data.
* Solution Analysis:
* Option A: Lambda Function for Validation
* Lambda can validate files, but it would require custom code.
* Does not leverage AWS Glue's built-in features, adding operational complexity.
* Option B: AWS Glue Data Quality Rules
* AWS Glue Data Quality allows definingData Quality Definition Language (DQDL)rules.
* Rules can validate if required fields are missing or if the file is empty.
* Automatically integrates into the existing ETL pipeline.
* If validation fails, retain the previous day's file.
* Option C: AWS Glue Studio with Filling Missing Values
* Modifying ETL code to fill missing values with most common values risks introducing inaccuracies.
* Does not handle empty files effectively.
* Option D: Athena Query for Validation
* Athena can drop rows with missing values, but this is a post-hoc solution.
* Requires manual intervention to copy the corrected file to S3, increasing complexity.
* Final Recommendation:
* UseAWS Glue Data Qualityto define validation rules in DQDL for identifying missing or incomplete data.
* This solution integrates seamlessly with the ETL pipeline and minimizes manual effort.
Implementation Steps:
* EnableAWS Glue Data Qualityin the existing ETL pipeline.
* DefineDQDL Rules, such as:
* Check if a file is empty.
* Verify required fields are present and non-null.
* Configure the pipeline to proceed with overwriting only if the file passes validation.
* In case of failure, retain the previous day's file.
:
AWS Glue Data Quality Overview
Defining DQDL Rules
AWS Glue Studio Documentation

 

NEW QUESTION # 53
A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?

• A. Confiqure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe- Write a SQL SELECT statement on the dataframe to query the required column.
• B. Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.
• C. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.
• D. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.

Answer: B

Explanation:
Option B is the best solution to meet the requirements with the least operational overhead because S3 Select is a feature that allows you to retrieve only a subset of data from an S3 object by using simple SQL expressions. S3 Select works on objects stored in CSV, JSON, or Parquet format. By using S3 Select, you can avoid the need to download and process the entire S3 object, which reduces the amount of data transferred and the computation time. S3 Select is also easy to use and does not require any additional services or resources.
Option A is not a good solution because it involves writing custom code and configuring an AWS Lambda function to load data from the S3 bucket into a pandas dataframe and query the required column. This option adds complexity and latency to the data retrieval process and requires additional resources and configuration. Moreover, AWS Lambda has limitations on the execution time, memory, and concurrency, which may affect the performance and reliability of the data retrieval process.
Option C is not a good solution because it involves creating and running an AWS Glue DataBrew project to consume the S3 objects and query the required column. AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. However, in this scenario, the data is already in Parquet format, which is a columnar storage format that is optimized for analytics. Therefore, there is no need to use AWS Glue DataBrew to prepare the data. Moreover, AWS Glue DataBrew adds extra time and cost to the data retrieval process and requires additional resources and configuration.
Option D is not a good solution because it involves running an AWS Glue crawler on the S3 objects and using a SQL SELECT statement in Amazon Athena to query the required column. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location. Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. However, in this scenario, the schema and format of the data are already known and fixed, so there is no need to run a crawler to discover them. Moreover, running a crawler and using Amazon Athena adds extra time and cost to the data retrieval process and requires additional services and configuration.
Reference:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
S3 Select and Glacier Select - Amazon Simple Storage Service
AWS Lambda - FAQs
What Is AWS Glue DataBrew? - AWS Glue DataBrew
Populating the AWS Glue Data Catalog - AWS Glue
What is Amazon Athena? - Amazon Athena

 

NEW QUESTION # 54
A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.
The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.
Which solutions will meet these requirements? (Choose two.)

• A. Use Athena partition projection based on the S3 bucket prefix.
• B. Bucketthe data based on a column thatthe data have in common in a WHERE clause of the user query
• C. Create an AWS Glue partition index. Enable partition filtering.
• D. Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.
• E. Transform the data that is in the S3 bucket to Apache Parquet format.

Answer: A,C

Explanation:
The best solutions to resolve the performance bottleneck and reduce Athena query planning time are to create an AWS Glue partition index and enable partition filtering, and to use Athena partition projection based on the S3 bucket prefix.
AWS Glue partition indexes are a feature that allows you to speed up query processing of highly partitioned tables cataloged in AWS Glue Data Catalog. Partition indexes are available for queries in Amazon EMR, Amazon Redshift Spectrum, and AWS Glue ETL jobs. Partition indexes are sublists of partition keys defined in the table. When you create a partition index, you specify a list of partition keys that already exist on a given table. AWS Glue then creates an index for the specified keys and stores it in the Data Catalog. When you run a query that filters on the partition keys, AWS Glue uses the partition index to quickly identify the relevant partitions without scanning the entiretable metadata. This reduces the query planning time and improves the query performance1.
Athena partition projection is a feature that allows you to speed up query processing of highly partitioned tables and automate partition management. In partition projection, Athena calculates partition values and locations using the table properties that you configure directly on your table in AWS Glue. The table properties allow Athena to 'project', or determine, the necessary partition information instead of having to do a more time-consuming metadata lookup in the AWS Glue Data Catalog. Because in-memory operations are often faster than remote operations, partition projection can reduce the runtime of queries against highly partitioned tables. Partition projection also automates partition management because it removes the need to manually create partitions in Athena, AWS Glue, or your external Hive metastore2.
Option B is not the best solution, as bucketing the data based on a column that the data have in common in a WHERE clause of the user query would not reduce the query planning time. Bucketing is a technique that divides data into buckets based on a hash function applied to a column. Bucketing can improve the performance of join queries by reducing the amount of data that needs to be shuffled between nodes. However, bucketing does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario3.
Option D is not the best solution, as transforming the data that is in the S3 bucket to Apache Parquet format would not reduce the query planning time. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes. However, Parquet does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario4.
Option E is not the best solution, as using the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects would not reduce the query planning time. S3DistCP is a tool that can copy large amounts of data between Amazon S3 buckets or from HDFS to Amazon S3. S3DistCP can also aggregate smaller files into larger files to improve the performance of sequential access. However, S3DistCP does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario5. References:
Improve query performance using AWS Glue partition indexes
Partition projection with Amazon Athena
Bucketing vs Partitioning
Columnar Storage Formats
S3DistCp
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

 

NEW QUESTION # 55
A gaming company uses Amazon Kinesis Data Streams to collect clickstream data. The company uses Amazon Kinesis Data Firehose delivery streams to store the data in JSON format in Amazon S3. Data scientists at the company use Amazon Athena to query the most recent data to obtain business insights.
The company wants to reduce Athena costs but does not want to recreate the data pipeline.
Which solution will meet these requirements with the LEAST management effort?

• A. Integrate an AWS Lambda function with Firehose to convert source records to Apache Parquet and write them to Amazon S3. In parallel, run an AWS Glue extract, transform, and load (ETL) job to combine the JSON files and convert the JSON files to large Parquet files. Create a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
• B. Create an Apache Spark job that combines JSON files and converts the JSON files to Apache Parquet files. Launch an Amazon EMR ephemeral cluster every day to run the Spark job to create new Parquet files in a different S3 location. Use the ALTER TABLE SET LOCATION statement to reflect the new S3 location on the existing Athena table.
• C. Change the Firehose output format to Apache Parquet. Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size. For the existing data, create an AWS Glue extract, transform, and load (ETL) job. Configure the ETL job to combine small JSON files, convert the JSON files to large Parquet files, and add the YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
• D. Create a Kinesis data stream as a delivery destination for Firehose. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to run Apache Flink on the Kinesis data stream. Use Flink to aggregate the data and save the data to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.

Answer: C

Explanation:
Step 1: Understanding the Problem
The company collectsclickstream datavia Amazon Kinesis Data Streams and stores it inJSON formatin Amazon S3 using Kinesis Data Firehose. They useAmazon Athenato query the data, but they want toreduce Athena costswhile maintaining the same data pipeline.
Since Athena charges based on the amount of data scanned during queries, reducing the data size (by converting JSON to a more efficient format likeApache Parquet) is a key solution to lowering costs.
Step 2: Why Option A is Correct
* Option Aprovides a straightforward way to reduce costs withminimal management overhead:
* Changing the Firehose output format to Parquet: Parquet is a columnar data format, which is more compact and efficient than JSON for Athena queries. It significantly reduces the amount of data scanned, which in turn reduces Athena query costs.
* Custom S3 Object Prefix (YYYYMMDD): Adding a date-based prefix helps in partitioning the data, which further improves query efficiency in Athena by limiting the data scanned to only relevant partitions.
* AWS Glue ETL Job for Existing Data: To handle existing data stored in JSON format, a one- time AWS Glue ETL job can combine small JSON files, convert them to Parquet, and apply the YYYYMMDD prefix. This ensures consistency in the S3 bucket structure and allows Athena to efficiently query historical data.
* ALTER TABLE ADD PARTITION: This command updates Athena's table metadata to reflect the new partitions, ensuring that future queries target only the required data.
Step 3: Why Other Options Are Not Ideal
* Option B (Apache Spark on EMR)introduces higher management effort by requiring the setup of Apache Spark jobsand anAmazon EMR cluster. While it achieves the goal of converting JSON to Parquet, it involves running and maintaining an EMR cluster, which adds operational complexity.
* Option C (Kinesis and Apache Flink)is a more complex solution involvingApache Flink, which adds a real-time streaming layer to aggregate data. Although Flink is a powerful tool for stream processing, it adds unnecessary overhead in this scenario since the company already uses Kinesis Data Firehose for batch delivery to S3.
* Option D (AWS Lambda with Firehose)suggests usingAWS Lambdato convert records in real time.
While Lambda can work in some cases, it's generally not the best tool for handling large-scale data transformations like JSON-to-Parquet conversion due to potential scaling and invocation limitations.
Additionally, running parallel Glue jobs further complicates the setup.
Step 4: How Option A Minimizes Costs
* By usingApache Parquet, Athena queries become more efficient, as Athena will scan significantly less data, directly reducing query costs.
* Firehosenatively supports Parquet as an output format, so enabling this conversion in Firehose requires minimal effort. Once set, new data will automatically be stored in Parquet format in S3, without requiring any custom coding or ongoing management.
* TheAWS Glue ETL jobfor historical data ensures that existing JSON files are also converted to Parquet format, ensuring consistency across the data stored in S3.
Conclusion:
Option A meets the requirement toreduce Athena costswithout recreating the data pipeline, using Firehose's native support forApache Parquetand a simple one-timeAWS Glue ETL jobfor existing data. This approach involvesminimal management effortcompared to the other solutions.

 

NEW QUESTION # 56
A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3.
Which solution will meet these requirements in the MOST operationally efficient way?

• A. Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.
• B. Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.
• C. Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
• D. Create a view in the EC2 instance-based SQL Server databases that contains the required data elements.
  Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.

Answer: D

Explanation:
Option A is the most operationally efficient way to meet the requirements because it minimizes the number of steps and services involved in the data export process. AWS Glue is a fully managed service that can extract, transform, and load (ETL) data from various sources to various destinations, including Amazon S3. AWS Glue can also convert data to different formats, such as Parquet, which is a columnar storage format that is optimized for analytics. By creating a view in the SQL Server databases that contains the required data elements, the AWS Glue job can select the data directly from the view without having to perform any joins or transformations on the source data. The AWS Glue job can then transfer the data in Parquet format to an S3 bucket and run on a daily schedule.
Option B is not operationally efficient because it involves multiple steps and services to export the data. SQL Server Agent is a tool that can run scheduled tasks on SQL Server databases, such as executing SQL queries.
However, SQL Server Agent cannot directly export data to S3, so the query output must be saved as .csv objects on the EC2 instance. Then, an S3 event must be configured to trigger an AWS Lambda function that can transform the .csv objects to Parquet format and upload them to S3. This option adds complexity and latency to the data export process and requires additional resources and configuration.
Option C is not operationally efficient because it introduces an unnecessary step of running an AWS Glue crawler to read the view. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location. However, in this scenario, the schema and format of the data elements are already known and fixed, so there is no need to run a crawler to discover them. The AWS Glue job can directly select the data from the view without using the Data Catalog. Running a crawler adds extra time and cost to the data export process.
Option D is not operationally efficient because it requires custom code and configuration to query the databases and transform the data. An AWS Lambda function is a service that can run code in response to events or triggers, such as Amazon EventBridge. Amazon EventBridge is a service that can connect applications and services with event sources, such as schedules, and route them to targets, such as Lambda functions. However, in this scenario, using a Lambda function to query the databases and transform the data is not the best option because it requires writing and maintaining code that uses JDBC to connect to the SQL Server databases, retrieve the required data, convert the data to Parquet format, and transfer the data to S3.
This option also has limitations on the execution time, memory, and concurrency of the Lambda function, which may affect the performance and reliability of the data export process.
References:
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
* AWS Glue Documentation
* Working with Views in AWS Glue
* Converting to Columnar Formats

 

NEW QUESTION # 57
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