A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints.
The company stores the data in multiple tables and loads the data by using the EVEN distribution style.
Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size.
Which solution will meet these requirements?
A. Keep using the EVEN distribution style for all tables. Specify primary and foreign keys for all tables. B. Use the ALL distribution style for large tables. Specify primary and foreign keys for all tables. C. Use the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables. D. Specify a combination of distribution, sort, and partition keys for all tables.
C. Use the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables.
Question 82:
A company is developing a log streaming pipeline that uses Amazon Data Firehose. The pipeline streams Amazon CloudWatch Logs data to an Amazon S3 bucket. The company's analytics team needs to use the data in audits. The pipeline must deliver only the relevant logs to the S3 bucket in a compatible format for the team's analysis.
Which solution will meet these requirements and maintain reliable performance?
A. Set the S3 bucket rules to allow logs from only specific timestamp ranges. Create an AWS Lambda function that converts the log files to the desired format. Use an S3 trigger to invoke the Lambda function. B. Create a subscription filter in the CloudWatch Logs log group that uses the Firehose delivery stream as the destination. Create an AWS Lambda function that converts the log files to the desired format. Configure Firehose to invoke the Lambda function. C. Create a subscription filter in the CloudWatch Logs log group. Configure the filter to monitor the Firehose stream. Create an AWS Lambda function to convert the log files to the desired format. Configure Firehose to invoke the Lambda function. D. Tag the CloudWatch Logs log groups that the analytics team needs. Configure Firehose to ingest only the tagged log groups. Configure Firehose to write the output in the desired format.
B. Create a subscription filter in the CloudWatch Logs log group that uses the Firehose delivery stream as the destination. Create an AWS Lambda function that converts the log files to the desired format. Configure Firehose to invoke the Lambda function.
Question 83:
A manufacturing company wants real-time operational efficiency views from sensor data in Kinesis Data Streams (written to S3 via Firehose).
Which solution has the lowest latency?
A. Use Amazon Managed Service for Apache Flink to process data, write to Timestream, and build Grafana dashboard B. Trigger Lambda via S3 notification, write to Aurora, and build QuickSight dashboard C. Use Flink to process data, send to Timestream via Firehose, and build QuickSight dashboard D. Use AWS Glue bookmarks to read S3 data in real-time, write to Timestream, and build Grafana dashboard
A. Use Amazon Managed Service for Apache Flink to process data, write to Timestream, and build Grafana dashboard
Explanation
Question 84:
A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data
elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3.
Which solution will meet these requirements in the MOST operationally efficient way?
A. 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. B. 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. C. Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day. D. Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.
C. Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
Question 85:
A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery.
The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format.
A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.
Which solution will meet these requirements with the LEAST operational effort?
A. Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table. B. Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table. C. Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table. D. Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.
B. Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table.
Explanation
Amazon Athena provides federated query connectors that allow querying multiple data sources, such as Amazon Redshift, Teradata, and Google BigQuery, without needing to extract the data from the original source. This solution is optimal because it offers the least operational effort by avoiding complex data movement and transformation processes.
Amazon Athena Federated Queries:
Athena's federated queries allow direct querying of data stored across multiple sources, including Amazon Redshift, Teradata, and BigQuery. With Athena's support for Apache Iceberg, the company can easily run a Merge operation on the Iceberg table.
The solution reduces complexity by centralizing the query execution and transformation process in Athena using SQL queries.
Question 86:
A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day.
Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM. A business intelligence team queries the tables between 10 AM and 8 PM every day.
Which solution will meet these requirements in the MOST operationally efficient way?
A. Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3. B. Use an AWS Glue Java Database Connectivity (JDBC) connection. Configure a job bookmark for a column that contains monotonically increasing values. Write custom logic to append the daily incremental data to a full-load copy that is in Amazon S3. C. Use an AWS Database Migration Service (AWS DMS) full load migration to load the data warehouse tables into Amazon S3 every day Overwrite the previous day's full-load copy every day. D. Use AWS Glue to load a full copy of the data warehouse tables into Amazon S3 every day. Overwrite the previous day's full-load copy every day.
A. Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3.
Explanation
The company needs to load data warehouse tables into Amazon S3 and perform incremental synchronization with daily updates. The most efficient solution is to use AWS Database Migration Service (AWS DMS) with a combination of full load and change data capture (CDC) to handle the initial load and daily incremental updates.
Option A: Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon
S3.DMS is designed to migrate databases to AWS, and the combination of full load plus CDC is ideal for handling incremental data changes efficiently. AWS Glue can then be used to append the incremental data to the full data set in S3. This solution is highly operationally efficient because it automates both the full load and incremental updates.
Options B, C, and Dare less operationally efficient because they either require writing custom logic to handle bookmarks manually or involve unnecessary daily full loads.
References:
AWS Database Migration Service Documentation
AWS Glue Documentation
Question 87:
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
B. Amazon EMR
Explanation
AWS Glue is a fully managed serverless ETL service that can handle petabytes of data in seconds. AWS Glue can run Apache Spark and Apache Flink jobs without requiring any infrastructure provisioning or management. AWS Glue can also integrate with Apache Pig, Apache Oozie, and Apache Hbase using AWS Glue Data Catalog and AWS Glue workflows. AWS Glue can reduce the overall operational overhead by automating the data discovery, data preparation, and data loading processes. AWS Glue can alsooptimize the cost and performance of ETL jobs by using AWS Glue Job Bookmarking, AWS Glue Crawlers, and AWS Glue Schema Registry.
Question 88:
A company runs multiple applications on AWS. The company configured each application to output logs.
The company wants to query and visualize the application logs in near real time.
Which solution will meet these requirements?
A. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an Amazon S3 bucket. Create an AWS Lambda function that runs on a schedule to export the required log groups to the S3 bucket. Use Amazon Athena to query the log data in the S3 bucket. B. Create an Amazon OpenSearch Service domain. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an OpenSearch Service subscription filter for each log group to stream the data to OpenSearch. Create the required queries and dashboards in OpenSearch Service to analyze and visualize the data. C. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Use CloudWatch log anomaly detection to query and visualize the log data. D. Update the application code to send the log data to Amazon QuickSight by using Super-fast, Parallel, In-memory Calculation Engine (SPICE). Create the required analyses and dashboards in QuickSight.
B. Create an Amazon OpenSearch Service domain. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an OpenSearch Service subscription filter for each log group to stream the data to OpenSearch. Create the required queries and dashboards in OpenSearch Service to analyze and visualize the data.
Explanation
By streaming your CloudWatch Logs log groups directly into an Amazon OpenSearch Service domain via subscription filters, you get near-real-time indexing and can build Kibana dashboards for querying and visualization with virtually no batching delays. This fully managed pipeline minimizes custom glue code and meets your requirements with low operational overhead.
Question 89:
A data engineer at a company is optimizing extract, transform, and load (ETL) workflows. The current architecture uses Amazon EMR and Apache Spark for large-scale transformations and AWS Glue for other ETL tasks. The workflows load processed data into an Amazon S3 based data lake.
The company wants to move to a fully managed serverless solution that can orchestrate multiple ETL jobs and automate execution. The new solution must continue to use Spark to process data. The company needs to orchestrate and automate the ETL workflows with minimal manual intervention.
Which solution will meet these requirements?
A. Migrate all ETL jobs to AWS Glue. Use AWS Glue workflows to orchestrate the pipeline. B. Configure AWS Step Functions and Amazon EventBridge to orchestrate and invoke ETL workflows in AWS Glue and Amazon EMR. C. Configure AWS Lambda functions to process Amazon S3 event notifications for data transformation tasks when new data is uploaded. D. Use Amazon Managed Workflows for Apache Airflow automatic scheduling to orchestrate the Spark-based ETL jobs.
A. Migrate all ETL jobs to AWS Glue. Use AWS Glue workflows to orchestrate the pipeline.
Question 90:
A data engineer creates an AWS Glue Data Catalog table by using an AWS Glue crawler that is named Orders. The data engineer wants to add the following new partitions:
The data engineer must edit the metadata to include the new partitions in the table without scanning all the folders and les in the location of the table.
Which data de nition language (DDL) statement should the data engineer use in Amazon Athena?
A. ALTER TABLE Orders ADD PARTITION(order_date='2023-01-01') LOCATION `s3://transactions/ orders/order_date=2023-01-01'; ALTER TABLE Orders ADD PARTITION(order_date='2023-01-02') LOCATION `s3://transactions/ orders/ order_date=2023-01-02'; B. MSCK REPAIR TABLE Orders; C. REPAIR TABLE Orders; D. ALTER TABLE Orders MODIFY PARTITION(order_date='2023-01-01') LOCATION `s3://transactions/ orders/2023-01-01'; ALTER TABLE Orders MODIFY PARTITION(order_date='2023-01-02') LOCATION `s3://transactions/ orders/2023- 01-02';
A. ALTER TABLE Orders ADD PARTITION(order_date='2023-01-01') LOCATION `s3://transactions/ orders/order_date=2023-01-01'; ALTER TABLE Orders ADD PARTITION(order_date='2023-01-02') LOCATION `s3://transactions/ orders/ order_date=2023-01-02';
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