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AWS Certified Data Engineer - 80 Mock
Questions
To watch the walkthrough the questions and get more information about the answers, please check it out the following YouTube video for question from 1 to 40: AWS Certified Data Engineer Associate Exam Practice Questions - ANALYSIS P1 (DEA-C01) For question from 40 to 80, please follow this link: AWS Certified Data Engineer Associate Exam Practice Questions - ANALYSIS P2 (DEA-C01)
1. A data engineer is configuring an AWS Glue job to read data from an Amazon S3 bucket. The data engineer has set up the necessary AWS Glue connection details and an associated IAM role. However, when the data engineer attempts to run the AWS Glue job, the data engineer receives an error message that indicates that there are problems with the Amazon S3 VPC gateway endpoint. The data engineer must resolve the error and connect the AWS Glue job to the S3 bucket. Which solution will meet this requirement? A. Update the AWS Glue security group to allow inbound traffic from the Amazon S3 VPC gateway endpoint. B. Configure an S3 bucket policy to explicitly grant the AWS Glue job permissions to access the S bucket. C. Review the AWS Glue job code to ensure that the AWS Glue connection details include a fully qualified domain name. D. Verify that the VPC's route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint. 2. A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts. Which solution will meet these requirements with the LEAST operational effort? A. Create a separate table for each country’s customer data. Provide access to each analyst based on the country that the analyst serves. B. Register the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row-level security features to enforce the company’s access policies. C. Move the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves. D. Load the data into Amazon Redshift. Create a view for each country. Create separate IAM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.
3. A media company wants to improve a system that recommends media content to customers based on user behaviour and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company’s existing analytics platform. The company wants to minimise the effort and time required to incorporate third-party datasets. Which solution will meet these requirements with the LEAST operational overhead? A. Use API calls to access and integrate third-party datasets from AWS Data Exchange. B. Use API calls to access and integrate third-party datasets from AWS DataSync. C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). 4. A financial company wants to implement a data mesh. The data mesh must support centralised data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations. Which combination of AWS services will implement a data mesh? (Choose two.) A. Use Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis. B. Use Amazon S3 for data storage. Use Amazon Athena for data analysis. C. Use AWS Glue DataBrew for centralised data governance and access control. D. Use Amazon RDS for data storage. Use Amazon EMR for data analysis. E. Use AWS Lake Formation for centralised data governance and access control. 5. A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions. The data engineer requires a less manual way to update the Lambda functions. Which solution will meet this requirement? A. Store a pointer to the custom Python scripts in the execution context object in a shared Amazon S3 bucket. B. Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions. C. Store a pointer to the custom Python scripts in environment variables in a shared Amazon S bucket. D. Assign the same alias to each Lambda function. Call each Lambda function by specifying the function's alias.
A. Choose the FLEX execution class in the Glue job properties. B. Use the Spot Instance type in Glue job properties. C. Choose the STANDARD execution class in the Glue job properties. D. Choose the latest version in the GlueVersion field in the Glue job properties.
10. A data engineer needs to create an AWS Lambda function that converts the format of data from .csv to Apache Parquet. The Lambda function must run only if a user uploads a .csv file to an Amazon S3 bucket. Which solution will meet these requirements with the LEAST operational overhead? A. Create an S3 event notification that has an event type of s3:ObjectCreated:. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification. B. Create an S3 event notification that has an event type of s3:ObjectTagging: for objects that have a tag set to .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification. C. Create an S3 event notification that has an event type of s3:. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification. D. Create an S3 event notification that has an event type of s3:ObjectCreated:. Use a filter rule to generate notifications only when the suffix includes .csv. Set an Amazon Simple Notification Service (Amazon SNS) topic as the destination for the event notification. Subscribe the Lambda function to the SNS topic. 11. A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column. Which solution will MOST speed up the Athena query performance? A. Change the data format from .csv to JSON format. Apply Snappy compression. B. Compress the .csv files by using Snappy compression. C. Change the data format from .csv to Apache Parquet. Apply Snappy compression. D. Compress the .csv files by using gzip compression. 12. A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket. The company needs to display a real-time view of operational efficiency on a large
screen in the manufacturing facility. Which solution will meet these requirements with the LOWEST latency? A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard. C. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard. D. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
13. A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data. The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog. Which solution will meet these requirements? A. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Configure the output destination to a new path in the existing S bucket. B. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Specify a database name for the output. C. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Specify a database name for the output. D. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Configure the output destination to a new path in the existing S3 bucket. 14. A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded. A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB. How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?
A. Configure AWS Glue triggers to run the ETL jobs every hour. B. Use AWS Glue DataBrew to clean and prepare the data for analytics. C. Use AWS Lambda functions to schedule and run the ETL jobs every hour. D. Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift. E. Use the Redshift Data API to load transformed data into Amazon Redshift.
18. A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling. Which solution will meet this requirement? A. Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups. B. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster. C. Turn on concurrency scaling in the settings during the creation of any new Redshift cluster. D. Turn on concurrency scaling for the daily usage quota for the Redshift cluster. 19. A data engineer must orchestrate a series of Amazon Athena queries that will run every day. Each query can run for more than 15 minutes. Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)
A. Use an AWS Lambda function and the Athena Boto3 client start_query_execution
API call to invoke the Athena queries programmatically. B. Create an AWS Step Functions workflow and add two states. Add the first state before the Lambda function. Configure the second state as a Wait state to periodically check whether the
Athena query has finished using the Athena Boto3 get_query_execution API call.
Configure the workflow to invoke the next query when the current query has finished running. C. Use an AWS Glue Python shell job and the Athena Boto3 client
start_query_execution API call to invoke the Athena queries programmatically.
D. Use an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully. Configure the Python shell script to invoke the next query when the current query has finished running. E. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch.
20. A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options. The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS. Which extract, transform, and load (ETL) service will meet these requirements? A. AWS Glue B. Amazon EMR C. AWS Lambda D. Amazon Redshift 21. A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable information (PII). The data engineer must implement a solution to profile the dataset and obfuscate the PII. Which solution will meet this requirement with the LEAST operational effort? A. Use an Amazon Kinesis Data Firehose delivery stream to process the dataset. Create an AWS Lambda transform function to identify the PII. Use an AWS SDK to obfuscate the PII. Set the S data lake as the target for the delivery stream. B. Use the Detect PII transform in AWS Glue Studio to identify the PII. Obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake. C. Use the Detect PII transform in AWS Glue Studio to identify the PII. Create a rule in AWS Glue Data Quality to obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake. D. Ingest the dataset into Amazon DynamoDB. Create an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data. Use the same Lambda function to ingest the data into the S3 data lake. 22. A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3-based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data. The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort. Which solution will meet these requirements with the LEAST operational overhead? A. AWS Glue workflows B. AWS Step Functions tasks C. AWS Lambda functions D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows
A. Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis. B. Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files. C. Use Amazon Athena Federated Query to join the data from all data sources. D. Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.
26. A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimised and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company's current level of performance. Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.) A. Use Hadoop Distributed File System (HDFS) as a persistent data store. B. Use Amazon S3 as a persistent data store. C. Use x86-based instances for core nodes and task nodes. D. Use Graviton instances for core nodes and task nodes. E. Use Spot Instances for all primary nodes. 27. A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools. Which solution will meet these requirements with the LEAST operational overhead? A. Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift to make the data immediately available for real- time analysis. B. Access the data from Kinesis Data Streams by using SQL queries. Create materialised views directly on top of the stream. Refresh the materialised views regularly to query the most recent stream data. C. Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. Create a materialised view to read data from the stream. Set the materialised view to auto refresh. D. Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3. Use the COPY command to load the data from Amazon S3 to a table in Amazon Redshift.
28. A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications. The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day. A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs. Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.) A. Partition the data that is in the S3 bucket. Organise the data by year, month, and day. B. Increase the AWS Glue instance size by scaling up the worker type. C. Convert the AWS Glue schema to the DynamicFrame schema class. D. Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day. E. Modify the IAM role that grants access to AWS Glue to grant access to all S3 features. 29. A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file. Which Step Functions state should the data engineer use to meet these requirements? A. Parallel state B. Choice state C. Map state D. Wait state 30. A company is migrating a legacy application to an Amazon S3-based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information. The data engineer must identify and remove duplicate information from the legacy application data. Which solution will meet these requirements with the LEAST operational overhead? A. Write a custom extract, transform, and load (ETL) job in Python. Use the DataFrame.drop_duplicates() function by importing the Pandas library to perform data deduplication. B. Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication. C. Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication. D. Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library to perform data deduplication.
D. Create an AWS Lambda function. Ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events.
34. A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyse security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs. The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account. Which solution will meet these requirements? A. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account. B. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account. C. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account. D. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account. 35. A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes. A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake. Which solution will capture the changed data MOST cost-effectively? A. Create an AWS Lambda function to identify the changes between the previous data and the current data. Configure the Lambda function to ingest the changes into the data lake. B. Ingest the data into Amazon RDS for MySQL. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake. C. Use an open source data lake format to merge the data source with the S3 data lake to insert the new data and update the existing data. D. Ingest the data into an Amazon Aurora MySQL DB instance that runs Aurora Serverless. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.
36. 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. Create an AWS Glue partition index. Enable partition filtering. B. Bucket the data based on a column that the data have in common in a WHERE clause of the user query. C. Use Athena partition projection based on the S3 bucket prefix. D. Transform the data that is in the S3 bucket to Apache Parquet format. E. Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects. 37. A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant. Which solution will meet these requirements with the LEAST operational overhead? A. Use an AWS Lambda function that includes both the business and the analytics logic to perform time-based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams. B. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyse the data that might occasionally contain duplicates by using multiple types of aggregations. C. Use an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp. D. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyse the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes. 38. A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage. Which solution will meet these requirements with the LEAST operational overhead? A. Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances. B. Create new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.
41. A data engineer must ingest a source of structured data that is in .csv format into an Amazon S data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file. Which solution will meet these requirements MOST cost-effectively? A. Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format. B. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to ingest the data into the data lake in JSON format. C. Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format. D. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format. 42. A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage. A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region. Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.) A. Use data filters for each Region to register the S3 paths as data locations. B. Register the S3 path as an AWS Lake Formation location. C. Modify the IAM roles of the HR departments to add a data filter for each department's Region. D. Enable fine-grained access control in AWS Lake Formation. Add a data filter for each Region. E. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region. 43. A company uses AWS Step Functions to orchestrate a data pipeline. The pipeline consists of Amazon EMR jobs that ingest data from data sources and store the data in an Amazon S3 bucket. The pipeline also includes EMR jobs that load the data to Amazon Redshift. The company's cloud infrastructure team manually built a Step Functions state machine. The cloud infrastructure team launched an EMR cluster into a VPC to support the EMR jobs. However, the deployed Step Functions state machine is not able to run the EMR jobs. Which combination of steps should the company take to identify the reason the Step Functions state machine is not able to run the EMR jobs? (Choose two.) A. Use AWS CloudFormation to automate the Step Functions state machine deployment. Create a step to pause the state machine during the EMR jobs that fail. Configure the step to wait for a human user to send approval through an email message. Include details of the EMR task in the email message for further analysis.
B. Verify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. Verify that the Step Functions state machine code also includes IAM permissions to access the Amazon S3 buckets that the EMR jobs use. Use Access Analyzer for S3 to check the S3 access properties. C. Check for entries in Amazon CloudWatch for the newly created EMR cluster. Change the AWS Step Functions state machine code to use Amazon EMR on EKS. Change the IAM access policies and the security group configuration for the Step Functions state machine code to reflect inclusion of Amazon Elastic Kubernetes Service (Amazon EKS). D. Query the flow logs for the VPC. Determine whether the traffic that originates from the EMR cluster can successfully reach the data providers. Determine whether any security group that might be attached to the Amazon EMR cluster allows connections to the data source servers on the informed ports. E. Check the retry scenarios that the company configured for the EMR jobs. Increase the number of seconds in the interval between each EMR task. Validate that each fallback state has the appropriate catch for each decision state. Configure an Amazon Simple Notification Service (Amazon SNS) topic to store the error messages.
44. A company is developing an application that runs on Amazon EC2 instances. Currently, the data that the application generates is temporary. However, the company needs to persist the data, even if the EC2 instances are terminated. A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI) and configure the instances to preserve the data. Which solution will meet this requirement? A. Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data. Apply the default settings to the EC2 instances. B. Launch new EC2 instances by using an AMI that is backed by a root Amazon Elastic Block Store (Amazon EBS) volume that contains the application data. Apply the default settings to the EC instances. C. Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume. Attach an Amazon Elastic Block Store (Amazon EBS) volume to contain the application data. Apply the default settings to the EC2 instances. D. Launch new EC2 instances by using an AMI that is backed by an Amazon Elastic Block Store (Amazon EBS) volume. Attach an additional EC2 instance store volume to contain the application data. Apply the default settings to the EC2 instances. 45. A company uses Amazon Athena to run SQL queries for extract, transform, and load (ETL) tasks by using Create Table As Select (CTAS). The company must use Apache Spark instead of SQL to generate analytics. Which solution will give the company the ability to use Spark to access Athena? A. Athena query settings B. Athena workgroup
B. Change WHERE year = 2023 to WHERE extract(year FROM sales_data) = 2023. C. Add HAVING sum(sales_amount) > 0 after the GROUP BY clause. D. Remove the GROUP BY clause.
49. 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. Configure 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 S 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. 50. A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialised views. Which solution will meet this requirement with the LEAST effort? A. Use Apache Airflow to refresh the materialised views. B. Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialised views. C. Use the query editor v2 in Amazon Redshift to refresh the materialised views. D. Use an AWS Glue workflow to refresh the materialised views. 51. A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services. Which solution will meet these requirements with the LEAST management overhead? A. Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job. B. Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job. C. Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.
D. Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.
52. A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3. The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata. Which solution will meet these requirements with the LEAST operational overhead? A. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically. B. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog. C. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically. D. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog. 53. A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule. Every Monday, there is an immediate increase in activity early in the morning. The application has very low usage during weekends. The company must ensure that the application performs consistently during peak usage times. Which solution will meet these requirements in the MOST cost-effective way? A. Increase the provisioned capacity to the maximum capacity that is currently present during peak load times. B. Divide the table into two tables. Provision each table with half of the provisioned capacity of the original table. Spread queries evenly across both tables. C. Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times. Schedule lower capacity during off-peak times. D. Change the capacity mode from provisioned to on-demand. Configure the table to scale up and scale down based on the load on the table.
