A company manages an Amazon Redshift data warehouse. The data warehouse is in a public subnet inside a custom VPC. A security group allows only traffic from within itself. An ACL is open to all traffic.
The company wants to generate several visualizations in Amazon QuickSight for an upcoming sales event. The company will run QuickSight Enterprise edition in a second AWS account inside a public subnet within a second custom VPC. The new public subnet has a security group that allows outbound traffic to the existing Redshift cluster.
A data engineer needs to establish connections between Amazon Redshift and QuickSight. QuickSight must refresh dashboards by querying the Redshift cluster.
Which solution will meet these requirements?
A. Configure the Redshift security group to allow inbound traffic on the Redshift port from the QuickSight security group.
B. Assign Elastic IP addresses to the QuickSight visualizations. Configure the QuickSight security group to allow inbound traffic on the Redshift port from the Elastic IP addresses.
C. Confirm that the CIDR ranges of the Redshift VPC and the QuickSight VPC are the same. If CIDR ranges are different, reconfigure one CIDR range to match the other. Establish network peering between the VPCs.
D. Create a QuickSight gateway endpoint in the Redshift VPC. Attach an endpoint policy to the gateway endpoint to ensure only specific QuickSight accounts can use the endpoint.
Show Answer
Correct Answer: A
Explanation: QuickSight needs network connectivity to the Redshift cluster and the Redshift security group must allow inbound access on the Redshift port from the QuickSight side. Security groups are stateful, so allowing inbound from the QuickSight security group enables the connection. Option B is incorrect because QuickSight visualizations do not use Elastic IPs this way. Option C is incorrect because VPC peering requires non-overlapping CIDRs, not matching CIDRs, and the question does not require peering to satisfy the described access. Option D is incorrect because gateway endpoints do not exist for QuickSight access to Redshift.
Question 82
A data engineer is optimizing query performance in Amazon Athena notebooks that use Apache Spark to analyze large datasets that are stored in Amazon S3. The data is partitioned.
An AWS Glue crawler updates the partitions.
The data engineer wants to minimize the amount of data that is scanned to improve efficiency of Athena queries.
Which solution will meet these requirements?
A. Apply partition filters in the queries.
B. Increase the frequency of AWS Glue crawler invocations to update the data catalog more often.
C. Organize the data that is in Amazon S3 by using a nested directory structure.
D. Configure Spark to use in-memory caching for frequently accessed data.
Show Answer
Correct Answer: A
Explanation: Applying filters on partition keys enables partition pruning, so Athena reads only the relevant partitions instead of scanning the entire dataset. This minimizes data scanned, improving query performance and reducing cost. Increasing crawler frequency updates metadata but does not reduce scan volume. A nested directory structure alone does not help unless queries use partition keys. Spark in-memory caching can speed repeated access but does not minimize S3 data scanned by Athena partition pruning.
Question 83
An ecommerce company processes millions of orders each day. The company uses AWS Glue ETL to collect data from multiple sources, clean the data, and store the data in an Amazon S3 bucket in CSV format by using the S3 Standard storage class. The company uses the stored data to conduct daily analysis.
The company wants to optimize costs for data storage and retrieval.
Which solution will meet this requirement?
A. Transition the data to Amazon S3 Glacier Flexible Retrieval.
B. Transition the data from Amazon S3 to an Amazon Aurora cluster.
C. Configure AWS Glue ETL to transform the incoming data to Apache Parquet format.
D. Configure AWS Glue ETL to use Amazon EMR to process incoming data in parallel.
Show Answer
Correct Answer: C
Explanation: Apache Parquet is a compressed, columnar storage format optimized for analytics. Converting CSV data to Parquet reduces storage costs and lowers query and retrieval costs because analytics engines read only the required columns. Since the data is analyzed daily, archival storage like S3 Glacier is inappropriate due to retrieval delays and costs. Moving the data to Aurora does not optimize this analytics storage pattern, and using EMR for parallel processing addresses compute, not storage and retrieval efficiency.
Question 84
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.
Show Answer
Correct Answer: B
Explanation: Amazon OpenSearch Service is designed for near real-time log ingestion, indexing, querying, and visualization. CloudWatch Logs subscription filters can stream logs directly to OpenSearch with low latency, where dashboards and searches can be created using OpenSearch Dashboards. The other options either introduce batch delays (Athena on exported S3 logs), misuse CloudWatch anomaly detection (not a general visualization/query solution), or incorrectly use QuickSight/SPICE for log ingestion.
Question 85
A company wants to combine data from multiple software as a service (SaaS) applications for analysis.
A data engineering team needs to use Amazon QuickSight to perform the analysis and build dashboards. A data engineer needs to extract the data from the SaaS applications and make the data available for QuickSight queries.
Which solution will meet these requirements in the MOST operationally efficient way?
A. Create AWS Lambda functions that call the required APIs to extract the data from the applications. Store the data in an Amazon S3 bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight.
B. Use AWS Lambda functions as Amazon Athena data source connectors to run federated queries against the SaaS applications. Create an Athena data source and a dataset in QuickSight.
C. Use Amazon AppFlow to create a flow for each SaaS application. Set an Amazon S3 bucket as the destination. Schedule the flows to extract the data to the bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight.
D. Export data the from the SaaS applications as Microsoft Excel files. Create a data source and a dataset in QuickSight by uploading the Excel files.
Show Answer
Correct Answer: C
Explanation: Amazon AppFlow is the most operationally efficient service for extracting data from supported SaaS applications into Amazon S3 on a schedule without custom code. After landing the data in S3, AWS Glue can catalog it, and Amazon QuickSight can use the cataloged data through a dataset for analysis and dashboards. The other options require custom Lambda code, federated querying that is not the simplest ingestion pattern for analytics, or manual file exports.
Question 86
A sales company uses AWS Glue ETL to collect, process, and ingest data into an Amazon S3 bucket. The AWS Glue pipeline creates a new file in the S3 bucket every hour. File sizes vary from 200 KB to 300 KB. The company wants to build a sales prediction model by using data from the previous 5 years. The historic data includes 44,000 files.
The company builds a second AWS Glue ETL pipeline by using the smallest worker type. The second pipeline retrieves the historic files from the S3 bucket and processes the files for downstream analysis. The company notices significant performance issues with the second ETL pipeline.
The company needs to improve the performance of the second pipeline.
Which solution will meet this requirement MOST cost-effectively?
A. Use a larger worker type.
B. Increase the number of workers in the AWS Glue ETL jobs.
C. Use the AWS Glue DynamicFrame grouping option.
D. Enable AWS Glue auto scaling.
Show Answer
Correct Answer: C
Explanation: The performance issue is caused by processing a very large number of small files (44,000 files of 200–300 KB each), which creates significant file listing and task scheduling overhead in Spark. AWS Glue DynamicFrame grouping combines many small files into larger in-memory partitions for processing, reducing overhead and improving throughput without requiring additional compute resources. Increasing worker size, adding more workers, or enabling auto scaling increases cost and does not address the root cause as cost-effectively.
Question 87
A company uses an Amazon Redshift cluster as a data warehouse that is shared across two departments. To comply with a security policy, each department must have unique access permissions.
Department A must have access to tables and views for Department A. Department B must have access to tables and views for Department B.
The company often runs SQL queries that use objects from both departments in one query.
Which solution will meet these requirements with the LEAST operational overhead?
A. Group tables and views for each department into dedicated schemas. Manage permissions at the schema level.
B. Group tables and views for each department into dedicated databases. Manage permissions at the database level.
C. Update the names of the tables and views to follow a naming convention that contains the department names. Manage permissions based on the new naming convention.
D. Create an IAM user group for each department. Use identity-based IAM policies to grant table and view permissions based on the IAM user group.
Show Answer
Correct Answer: A
Explanation: Using separate schemas within the same Amazon Redshift database provides logical isolation and schema-level GRANT/REVOKE permissions while still allowing straightforward cross-schema queries using fully qualified object names. Separate databases would complicate queries, naming conventions do not enforce security, and IAM policies do not manage table/view-level permissions in Redshift.
Question 88
A company uses Amazon Redshift as a data warehouse solution. One of the datasets that the company stores in Amazon Redshift contains data for a vendor.
Recently, the vendor asked the company to transfer the vendor’s data into the vendor’s Amazon S3 bucket once each week.
Which solution will meet this requirement?
A. Create an AWS Lambda function to connect to the Redshift data warehouse. Configure the Lambda function to use the Redshift COPY command to copy the required data to the vendor’s S3 bucket on a schedule.
B. Create an AWS Glue job to connect to the Redshift data warehouse. Configure the AWS Glue job to use the Redshift UNLOAD command to load the required data to the vendor’s S3 bucket on a schedule.
C. Use the Amazon Redshift data sharing feature. Set the vendor’s S3 bucket as the destination. Configure the source to be as a custom SQL query that selects the required data.
D. Configure Amazon Redshift Spectrum to use the vendor’s S3 bucket a destination, Enable data querying in both directions.
Show Answer
Correct Answer: B
Explanation: Amazon Redshift exports data to Amazon S3 by using the UNLOAD command, not COPY. AWS Glue can connect to Amazon Redshift, execute the UNLOAD operation on a schedule, and write the results to an Amazon S3 bucket, including a cross-account bucket with appropriate permissions. Redshift data sharing is for sharing Redshift data between Redshift consumers, not S3, and Redshift Spectrum queries data in S3 rather than exporting Redshift tables to S3.
Question 89
A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.
Which solution will meet these requirements MOST cost-effectively?
A. Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue Data Quality to enforce suggested quality rules. Load the data and the quality check results into an Amazon RDS for MySQL instance.
B. Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data into an Amazon RDS for MySQL instance. Load the quality check results into a new S3 bucket.
C. Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue DataBrew to perform quality checks. Load the processed data and the quality check results into a new S3 bucket.
D. Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data and quality check results into an Amazon RDS for MySQL instance.
Show Answer
Correct Answer: A
Explanation: AWS Glue ETL is the appropriate serverless service to extract and transform data from Amazon S3. AWS Glue Data Quality integrates with Glue ETL to perform automated data quality validation efficiently. Because the requirement is to store processed data in a relational database for future queries, loading the transformed data (and, if desired, quality results) into Amazon RDS for MySQL satisfies the storage requirement. The alternatives either rely on DataBrew for production ETL/quality workflows or store the processed data only in Amazon S3 instead of a relational database.
Question 90
A data engineer develops an AWS Glue Apache Spark ETL job to perform transformations on a dataset. When the data engineer runs the job, the job returns an error that reads, “No space left on device.”
The data engineer needs to identify the source of the error and provide a solution.
Which combinations of steps will meet this requirement MOST cost-effectively? (Choose two.)
A. Scale out the workers vertically to address data skewness.
B. Use the Spark UI and AWS Glue metrics to monitor data skew in the Spark executors.
C. Scale out the number of workers horizontally to address data skewness.
D. Enable the --write-shuffie-files-to-s3 job parameter. Use the salting technique.
E. Use error logs in Amazon CloudWatch to monitor data skew.
Show Answer
Correct Answer: B, D
Explanation: The error 'No space left on device' in AWS Glue Spark jobs is commonly caused by local disk exhaustion during shuffle operations, often exacerbated by data skew. Use the Spark UI and AWS Glue metrics to identify skewed partitions and executors (B). Then enable the --write-shuffle-files-to-s3 job parameter to offload shuffle spill files to Amazon S3, and use salting to mitigate skew by distributing hot keys more evenly (D). Scaling workers (A or C) increases cost and may mask rather than resolve the underlying issue, while CloudWatch error logs (E) report failures but are not the best tool to diagnose skew.
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