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?
A. 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. B. 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. C. 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. D. 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.
A. 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.
Explanation
Amazon Athena is a serverless, interactive query service that enables you to analyze data in Amazon S3 using standard SQL. AWS Lake Formation is a service that helps you build, secure, and manage data lakes on AWS. You can use AWS Lake Formation to create data filters that define the level of access for different IAM roles based on the columns, rows, or tags of the data. By using Amazon Athena to query the data and AWS Lake Formation to create data filters, the company can meet the requirements of ensuring that user groups can access only the PII that they require with the least effort. The solution is to use Amazon Athena to query the data in the data lake that is in Amazon S3. Then, set up AWS Lake Formation and create data filters to establish levels of access for the company's IAM roles. For example, a data filter can allow a user group to access only the columns that contain the PII that they need, such as name and email address, and deny access to the columns that contain the PII that they do not need, such as phone number and social security number. Finally, assign each user to the IAM role that matches the user's PII access requirements. This way, the user groups can access the data in the data lake securely and efficiently. The other options are either not feasible or not optimal. Using Amazon QuickSight to access the data (option B) would require the company to pay for the QuickSight service and to configure the column-level security features for each user. Building a custom query builder UI that will run Athena queries in the background to access the data (option C) would require the company to develop and maintain the UI and to integrate it with Amazon Cognito. Creating IAM roles that have different levels of granular access (option
D. would require the company to manage multiple IAM roles and policies and to ensure that they are aligned with the data schema.
Question 42:
A marketing company uses Amazon S3 to store marketing data. The company uses versioning in some buckets. The company runs several jobs to read and load data into the buckets.
To help cost-optimize its storage, the company wants to gather information about incomplete multipart uploads and outdated versions that are present in the S3 buckets.
Which solution will meet these requirements with the LEAST operational effort?
A. Use AWS CLI to gather the information. B. Use Amazon S3 Inventory configurations reports to gather the information. C. Use the Amazon S3 Storage Lens dashboard to gather the information. D. Use AWS usage reports for Amazon S3 to gather the information.
C. Use the Amazon S3 Storage Lens dashboard to gather the information.
Question 43:
A company must ingest a continuous clickstream, run a lightweight record transformation, and deliver compressed data to Amazon S3 with minimal custom infrastructure. The company does not need to build custom stream consumers.
Which solution will meet these requirements?
A. Configure Amazon Kinesis Data Firehose with a Lambda transformation and an Amazon S3 destination. B. Configure Amazon SQS FIFO queues and run an Amazon EC2 fleet to poll and transform messages. C. Configure AWS Database Migration Service to replicate the clickstream into Amazon S3. D. Configure Amazon Redshift federated queries to read the clickstream and unload the results to Amazon S3.
A. Configure Amazon Kinesis Data Firehose with a Lambda transformation and an Amazon S3 destination.
Explanation
Kinesis Data Firehose can ingest streaming records, invoke Lambda for lightweight transformation, and deliver to Amazon S3 as a managed pipeline. Running EC2 pollers increases operational overhead. AWS DMS is for database migration and change replication. Redshift federated queries query external operational databases; they are not a managed clickstream ingestion path.
Question 44:
A manufacturing company uses AWS Glue jobs to process IoT sensor data to generate predictive maintenance models. A data engineer needs to implement automated data quality checks to identify temperature readings that are outside the expected range of -50C to 150C. The data quality checks must also identify records that are missing timestamp values.
The data engineer needs a solution that requires minimal coding and can automatically flag the specified issues.
Which solution will meet these requirements?
A. Create an AWS Glue DataBrew project to profile the sensor data Define completeness rules for timestamps. Set up numeric range validation for temperature values. B. Use AWS Glue's Data Quality rules and machine learning (ML)-based anomaly detection to identify missing timestamps and to detect temperature anomalies. C. Create an AWS Lambda function to scan the sensor data files to validate temperature ranges. Use AWS Glue Data Catalog tables to check timestamp completeness. D. Create an AWS Glue DynamicFrame that uses a custom data quality operator to profile the sensor data. Use Amazon SageMaker Data Wrangler transforms to validate timestamps and temperature ranges.
A. Create an AWS Glue DataBrew project to profile the sensor data Define completeness rules for timestamps. Set up numeric range validation for temperature values.
Question 45:
A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company's data center. The company has 70 tables in the Oracle database. All the tables haveprimary keys. Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?
A. Create an Apache Sqoop job in Amazon EMR to read the data from the Oracle database. Configure the Sqoop job to write the data to Amazon S3 in Parquet format. B. Create an AWS Glue connection to the Oracle database. Create an AWS Glue bookmark job to ingest the data incrementally and to write the data to Amazon S3 in Parquet format. C. Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format. D. Create an Oracle database in Amazon RDS. Use AWS Database Migration Service (AWS DMS) to migrate the on-premises Oracle database to Amazon RDS. Configure triggers on the tables to invoke AWS Lambda functions to write changed records to Amazon S3 in Parquet format.
C. Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.
Explanation
The company needs to ingest tables from an on-premises Oracle database into a data lake on Amazon S3 in Apache Parquet format . The most efficient solution, requiring the least manual effort, would be to use AWS Database Migration Service (DMS) for continuous data replication.
Option C: Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.AWS DMS can continuously replicate data from the Oracle database into Amazon S3, transforming it into Parquet format as it ingests the data. DMS simplifies the process by providing ongoing replication with minimal setup, and it automatically handles the conversion to Parquet format without requiring manual transformations or separate jobs. This option is theleast effort solution since it automates both the ingestion and transformation processes.
Other options:
Option A (Apache Sqoop on EMR)involves more manual configuration and management, including setting up EMR clusters and writing Sqoop jobs.
Option B (AWS Glue bookmark job)involves configuring Glue jobs, which adds complexity. While Glue supports data transformations, DMS offers a more seamless solution for database replication.
Option D (RDS and Lambda triggers)introduces unnecessary complexity by involving RDS and Lambda for a task that DMS can handle more efficiently.
References:
AWS Database Migration Service (DMS)
DMS S3 Target Documentation
Question 46:
A company is building a data stream processing application. The application runs in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The application stores processed data in an Amazon DynamoDB table.
The company needs the application containers in the EKS cluster to have secure access to the DynamoDB table. The company does not want to embed AWS credentials in the containers.
Which solution will meet these requirements?
A. Store the AWS credentials in an Amazon S3 bucket. Grant the EKS containers access to the S3 bucket to retrieve the credentials. B. Attach an IAM role to the EKS worker nodes. Grant the IAM role access to DynamoDB. Use the IAM role to set up IAM roles service accounts (IRSA) functionality. C. Create an IAM user that has an access key to access the DynamoDB table. Use environment variables in the EKS containers to store the IAM user access key data. D. Create an IAM user that has an access key to access the DynamoDB table. Use Kubernetes secrets that are mounted in a volume of the EKS cluster nodes to store the user access key data.
B. Attach an IAM role to the EKS worker nodes. Grant the IAM role access to DynamoDB. Use the IAM role to set up IAM roles service accounts (IRSA) functionality.
Explanation
In this scenario, the company is using Amazon Elastic Kubernetes Service (EKS) and wants secure access to DynamoDB without embedding credentials inside the application containers. The best practice is to useIAM roles for service accounts (IRSA) , which allows assigning IAM roles to Kubernetes service accounts. This lets the EKS pods assume specific IAM roles securely, without the need to store credentials in containers.
IAM Roles for Service Accounts (IRSA): With IRSA, each pod in the EKS cluster can assume an IAM role that grants access to DynamoDB without needing to manage long-term credentials. The IAM role can be attached to the service account associated with the pod.
This ensures least privilege access, improving security by preventing credentials from being embedded in the containers.
Question 47:
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 C. Athena data source D. Athena query editor
B. Athena workgroup
Question 48:
A data engineer is processing and analyzing multiple terabytes of raw data that is in Amazon S3. The data engineer needs to clean and prepare the data. Then the data engineer needs to load the data into Amazon Redshift for analytics.
The data engineer needs a solution that will give data analysts the ability to perform complex queries. The solution must eliminate the need to perform complex extract, transform, and load (ETL) processes or to manage infrastructure.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon EMR to prepare the data. Use AWS Step Functions to load the data into Amazon Redshift. Use Amazon QuickSight to run queries. B. Use AWS Glue DataBrew to prepare the data. Use AWS Glue to load the data into Amazon Redshift. Use Amazon Redshift to run queries. C. Use AWS Lambda to prepare the data. Use Amazon Kinesis Data Firehose to load the data into Amazon Redshift. Use Amazon Athena to run queries. D. Use AWS Glue to prepare the data. Use AWS Database Migration Service (AVVS DMS) to load the data into Amazon Redshift. Use Amazon Redshift Spectrum to run queries.
B. Use AWS Glue DataBrew to prepare the data. Use AWS Glue to load the data into Amazon Redshift. Use Amazon Redshift to run queries.
Question 49:
A company uses Amazon Redshift as its data warehouse. Data encoding is applied to the existing tables of the data warehouse. A data engineer discovers that the compression encoding applied to some of the tables is not the best fit for the data.
The data engineer needs to improve the data encoding for the tables that have sub-optimal encoding.
Which solution will meet this requirement?
A. Run the ANALYZE command against the identified tables. Manually update the compression encoding of columns based on the output of the command. B. Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command. C. Run the VACUUM REINDEX command against the identified tables. D. Run the VACUUM RECLUSTER command against the identified tables.
B. Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command.
Explanation
To improve data encoding for Amazon Redshift tables where sub-optimal encoding has been applied, the correct approach is to analyze the table to determine the optimal encoding based on the data distribution and characteristics.
Option B: Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command.The ANALYZE COMPRESSION command in Amazon Redshift analyzes the columnar data and suggests the best compression encoding for each column. The output provides recommendations for changing the current encoding to improve storage efficiency and query performance. After analyzing, you can manually apply the recommended encoding to the columns.
Option A(ANALYZE command) is incorrect because it is primarily used to update statistics on tables, not to analyze or suggest compression encoding. Options C and D(VACUUM commands) deal with reclaiming disk space and reorganizing data, not optimizing compression encoding.
Question 50:
A company needs to aggregate and filter a large amount of streaming data in real-time with low latency.
The company needs to store the data in Amazon S3 for analysis.
Which solution will meet these requirements in the MOST operationally efficient way?
A. Use Amazon Kinesis Data Streams with provisioned capacity and AWS Lambda functions to perform custom transformations and to integrate with Amazon S3. B. Use Amazon Data Firehose with built-in data transformations. Deliver the data directly to Amazon S3. Most Voted C. Use Amazon Kinesis Data Streams and Amazon Managed Service for Apache Flink to perform complex processing and to integrate with Amazon S3. D. Use Amazon Data Firehose and AWS Lambda functions to perform custom transformations and to deliver the data to Amazon S3.
B. Use Amazon Data Firehose with built-in data transformations. Deliver the data directly to Amazon S3. Most Voted
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