Quiz IT certification exam

Question 1

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?

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Accepted Answer

Map state

Answer

Parallel state

Answer

Choice state

Answer

Wait state

Question 2

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?

Accepted Answer

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.

Answer

Write a custom extract, transform, and load (ETL) job in Python. Use the DataFramedrop duplicatesf) function by importing the Pandas library to perform data deduplication.

Answer

Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

Answer

Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

Question 3

A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3.Which actions will provide the FASTEST queries? (Choose two.)

Accepted Answer

Use a columnar storage file format.

Accepted Answer

Partition the data based on the most common query predicates.

Answer

Use gzip compression to compress individual files to sizes that are between 1 GB and 5 GB.

Answer

Split the data into files that are less than 10 KB.

Answer

Use file formats that are not

Question 4

A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.Which combination of steps will meet this requirement with the LEAST operational overhead? (Choose two.)

Accepted Answer

Configure the Lambda function to run in the same subnet that the DB instance uses.

Accepted Answer

Attach the same security group to the Lambda function and the DB instance. Include a self-referencing rule that allows access through the database port.

Answer

Turn on the public access setting for the DB instance.

Answer

Update the security group of the DB instance to allow only Lambda function invocations on the database port.

Answer

Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.

Question 5

A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway.Which solution will meet these requirements with the LEAST operational overhead?

Accepted Answer

Create an AWS Lambda Python function with provisioned concurrency.

Answer

Deploy a custom Python script on an Amazon Elastic Container Service (Amazon ECS) cluster.

Answer

Deploy a custom Python script that can integrate with API Gateway on Amazon Elastic Kubernetes Service (Amazon EKS).

Answer

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.

Question 6

A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze 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?

Accepted Answer

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.

Answer

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.

Answer

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.

Answer

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.

Question 7

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?

Accepted Answer

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.

Answer

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.

Answer

Ingest the data into Amazon RDS for MySQL. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.

Answer

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.

Question 8

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.)

Accepted Answer

Create an AWS Glue partition index. Enable partition filtering.

Accepted Answer

Use Athena partition projection based on the S3 bucket prefix.

Answer

Bucket the data based on a column that the data have in common in a WHERE clause of the user query

Answer

Transform the data that is in the S3 bucket to Apache Parquet format.

Answer

Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.

Question 9

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?

Accepted Answer

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.

Answer

Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.

Answer

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.

Answer

Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes.

Question 10

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?

Accepted Answer

Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.

Answer

Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.

Answer

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.

Answer

Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.

Question 11

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?

Accepted Answer

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

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.

Answer

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.

Answer

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.

Question 12

A data engineering team is using an Amazon Redshift data warehouse for operational reporting. The team wants to prevent performance issues that might result from long- running queries. A data engineer must choose a system table in Amazon Redshift to record anomalies when a query optimizer identifies conditions that might indicate performance issues.Which table views should the data engineer use to meet this requirement?

Accepted Answer

STL ALERT EVENT LOG

Answer

STL USAGE CONTROL

Answer

STL QUERY METRICS

Answer

STL PLAN INFO

Question 13

A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 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?

Accepted Answer

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.

Answer

Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.

Answer

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.

Answer

Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.

Question 14

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.)

Accepted Answer

Register the S3 path as an AWS Lake Formation location.

Accepted Answer

Enable fine-grained access control in AWS Lake Formation. Add a data filter for each Region.

Answer

Use data filters for each Region to register the S3 paths as data locations.

Answer

Modify the IAM roles of the HR departments to add a data filter for each department's Region.

Answer

Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region.

Question 15

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.)

Accepted Answer

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.

Accepted Answer

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.

Answer

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.

Answer

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).

Answer

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.

Question 16

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?

Accepted Answer

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.

Answer

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.

Answer

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 EC2 instances.

Answer

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.

Question 17

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?

Accepted Answer

Athena data source

Answer

Athena query settings

Answer

Athena workgroup

Answer

Athena query editor

Question 18

A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format: s3://bucket/prefix/year=2023/month=01/day=01.A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket.Which solution will meet these requirements with the LEAST latency?

Accepted Answer

Schedule an AWS Glue crawler to run every morning.

Answer

Manually run the AWS Glue CreatePartition API twice each day.

Answer

Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create partition API call.

Answer

Run the MSCK REPAIR TABLE command from the AWS Glue console.

Question 19

A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.Which AWS service or feature will meet these requirements with the LEAST operational overhead?

Accepted Answer

Amazon AppFlow

Answer

Amazon Managed Streaming for Apache Kafka (Amazon MSK)

Answer

AWS Glue Data Catalog

Answer

Amazon Kinesis

Question 20

A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.The data engineer's original query is as follows:SELECT product_name, sum(sales_amount)FROM sales_dataWHERE year = 2023GROUP BY product_nameHow should the data engineer modify the Athena query to meet these requirements?

Accepted Answer

Change WHERE year = 2023 to WHERE extractlyear FROM sales data) = 2023.

Answer

Replace sum(sales amount) with count(*J for the aggregation.

Answer

Add HAVING sumfsales amount) > 0 after the GROUP BY clause.

Answer

Remove the GROUP BY clause

Question 21

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?

Accepted Answer

Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.

Answer

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.

Answer

Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.

Answer

Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.

Question 22

A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.Which solution will meet this requirement with the LEAST effort?

Accepted Answer

Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.

Answer

Use Apache Airflow to refresh the materialized views.

Answer

Use the query editor v2 in Amazon Redshift to refresh the materialized views.

Answer

Use an AWS Glue workflow to refresh the materialized views.

Question 23

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?

Accepted Answer

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.

Answer

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.

Answer

Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.

Answer

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.

Question 24

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?

Accepted Answer

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.

Answer

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.

Answer

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.

Answer

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.

Question 25

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?

Accepted Answer

Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times. Schedule lower capacity during off-peak times.

Answer

Increase the provisioned capacity to the maximum capacity that is currently present during peak load times.

Answer

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.

Answer

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.

Question 26

A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution.The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.Which solution will meet these requirements MOST cost-effectively?

Accepted Answer

Use AWS Database Migration Service (AWS DMS) to migrate the Hive metastore into Amazon S3. Configure AWS Glue Data Catalog to scan Amazon S3 to produce the data catalog.

Answer

Configure a Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use AWS Glue Data Catalog to store the company's data catalog as an external data catalog.

Answer

Configure an external Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use Amazon Aurora MySQL to store the company's data catalog.

Answer

Configure a new Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use the new metastore as the company's data catalog.

Question 27

A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution.A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations.The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes.Which solution will meet these requirements?

Accepted Answer

Change the distribution key to the table column that has the largest dimension.

Answer

Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.

Answer

Upgrade the reserved node from ra3.4xlarqe to ra3.16xlarqe.

Answer

Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.

Question 28

A security company stores IoT data that is in JSON format in an Amazon S3 bucket. The data structure can change when the company upgrades the IoT devices. The company wants to create a data catalog that includes the IoT data. The company's analytics department will use the data catalog to index the data.Which solution will meet these requirements MOST cost-effectively?

Accepted Answer

Create an Amazon Athena workgroup. Explore the data that is in Amazon S3 by using Apache Spark through Athena. Provide the Athena workgroup schema and tables to the analytics department.

Answer

Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.

Answer

Create an Amazon Redshift provisioned cluster. Create an Amazon Redshift Spectrum database for the analytics department to explore the data that is in Amazon S3. Create Redshift stored procedures to load the data into Amazon Redshift.

Answer

Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data API. Create an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.

Question 29

A company stores details about transactions in an Amazon S3 bucket. The company wants to log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.Which solution will meet this requirement with the LEAST operational effort?

Accepted Answer

Create a trail of data events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.

Answer

Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the event to Amazon Kinesis Data Firehose. Configure Kinesis Data Firehose to write the event to the logs S3 bucket.

Answer

Create a trail of management events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.

Answer

Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the events to the logs S3 bucket.

Question 30

A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.Which solution will meet these requirements with the LEAST development effort?

Accepted Answer

Use the AWS Glue Data Catalog.

Answer

Use Amazon EMR and Apache Ranger.

Answer

Use a Hive metastore on an EMR cluster.

Answer

Use a metastore on an Amazon RDS for MySQL DB instance.

Question 31

A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.Which solution will meet these requirements with the LEAST operational overhead?

Accepted Answer

Use Amazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.

Answer

Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access through Amazon S3.

Answer

Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access by rows and columns. Provide data access by using Apache Pig.

Answer

Use Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access by rows and columns. Provide data access by using Apache Spark and Amazon Athena federated queries.

Question 32

An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.As the amount of data increases, the company wants to optimize the storage solution to improve query performance.Which combination of solutions will meet these requirements? (Choose two.)

Accepted Answer

Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.

Accepted Answer

Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.

Answer

Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.

Answer

Use an S3 bucket that is in the same account that uses Athena to query the data.

Answer

Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.

Question 33

A company uses Amazon RDS for MySQL as the database for a critical application. The database workload is mostly writes, with a small number of reads.A data engineer notices that the CPU utilization of the DB instance is very high. The high CPU utilization is slowing down the application. The data engineer must reduce the CPU utilization of the DB Instance.Which actions should the data engineer take to meet this requirement? (Choose two.)

Accepted Answer

Use the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization. Optimize the problematic queries.

Accepted Answer

Implement caching to reduce the database query load.

Answer

Modify the database schema to include additional tables and indexes.

Answer

Reboot the RDS DB instance once each week.

Answer

Upgrade to a larger instance size.

Question 34

A company has used an Amazon Redshift table that is named Orders for 6 months. The company performs weekly updates and deletes on the table. The table has an interleaved sort key on a column that contains AWS Regions.The company wants to reclaim disk space so that the company will not run out of storage space. The company also wants to analyze the sort key column.Which Amazon Redshift command will meet these requirements?

Accepted Answer

VACUUM REINDEX Orders

Answer

VACUUM FULL Orders

Answer

VACUUM DELETE ONLY Orders

Answer

VACUUM SORT ONLY Orders

Question 35

A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time.The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds.Which solution will meet these requirements with the LEAST operational overhead?

Accepted Answer

Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in Amazon DynamoDB for querying.

Answer

Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in Amazon S3 for querying.

Answer

Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for querying.

Answer

Use Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data. Use AWS Glue to store the data in Amazon RDS for querying.

Question 36

A company stores data in a data lake that is in Amazon S3. Some data that the company stores in the data lake contains personally identifiable information (PII). Multiple user groups need to access the raw data. The company must ensure that user groups can access only the PII that they require.Which solution will meet these requirements with the LEAST effort?

Accepted Answer

Use Amazon Athena to query the data. Set up AWS Lake Formation and create data filters to establish levels of access for the company's IAM roles. Assign each user to the IAM role that matches the user's PII access requirements.

Answer

Use Amazon QuickSight to access the data. Use column-level security features in QuickSight to limit the PII that users can retrieve from Amazon S3 by using Amazon Athena. Define QuickSight access levels based on the PII access requirements of the users.

Answer

Build a custom query builder UI that will run Athena queries in the background to access the data. Create user groups in Amazon Cognito. Assign access levels to the user groups based on the PII access requirements of the users.

Answer

Create IAM roles that have different levels of granular access. Assign the IAM roles to IAM user groups. Use an identity-based policy to assign access levels to user groups at the column level.

Question 37

A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.The data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually.Which solution will meet these requirements with the LEAST operational overhead?

Accepted Answer

Use Amazon Redshift Serverless to automatically process the analytics workload.

Answer

Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.

Answer

Use the AWS CLI to automatically process the analytics workload.

Answer

Use AWS CloudFormation templates to automatically process the analytics workload.

Question 38

A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size.A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file.Which solution will meet this requirement with the LEAST operational effort?

Accepted Answer

Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.

Answer

Create and run an Apache Spark job in an AWS Glue notebook. Configure the job to read the S3 file and calculate the number of distinct customers.

Answer

Create an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file. Run SQL queries from Amazon Athena to calculate the number of distinct customers.

Answer

Create and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.

Question 39

A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.Which solution will meet these requirements with the LEAST operational overhead?

Accepted Answer

Use the streaming ingestion feature of Amazon Redshift.

Answer

Load data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift.

Answer

Load the data into Amazon S3. Use the COPY command to load the data into Amazon Redshift.

Answer

Use the Amazon Aurora zero-ETL integration with Amazon Redshift.

Question 40

A data engineer needs to use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket. When the data engineer connects to the QuickSight dashboard, the data engineer receives an error message that indicates insufficient permissions.Which factors could cause to the permissions-related errors? (Choose two.)

Accepted Answer

QuickSiqht does not have access to the S3 bucket.

Accepted Answer

QuickSight does not have access to decrypt S3 data.

Answer

There is no connection between QuickSgqht and Athena.

Answer

The Athena tables are not cataloged.

Answer

There is no 1AM role assigned to QuickSiqht.

Amazon DEA-C01 Practice Test 2

Amazon DEA-C01 Practice Tests

Amazon DEA-C01 Practice Test 2

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Amazon DEA-C01 Practice Tests

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