A company analyzes its data in an Amazon Redshift data warehouse, which currently has a cluster of three dense storage nodes. Due to a recent business acquisition, the company needs to load an additional 4 TB of user data into Amazon Redshift. The engineering team will combine all the user data and apply complex calculations that require I/O intensive resources. The company needs to adjust the cluster's capacity to support the change in analytical and storage requirements.Which solution meets these requirements?
Answer(s): A
Elastic resize with dense compute nodes provides rapid cluster scaling for compute-heavy workloads and allows growth beyond current storage while preserving data. This supports I/O-intensive calculations by increasing compute capacity without lengthy data redistribution.A) Elastic resize with dense compute nodes — correct: fast scaling for compute and storage needs on Redshift Spectrum/Redshift cluster; minimizes downtime.B) Classic resize with dense compute nodes — slower operation due to full data redistribution; not optimal for rapid, I/O-intensive workloads.C) Elastic resize with dense storage nodes — benefits storage, not compute; may bottleneck complex calculations requiring CPU/memory.D) Classic resize with dense storage nodes — slow and storage-focused; not suitable for compute-intensive analytics.
https://aws.amazon.com/redshift/pricing/
A company stores its sales and marketing data that includes personally identi able information (PII) in Amazon S3. The company allows its analysts to launch their own Amazon EMR cluster and run analytics reports with the data. To meet compliance requirements, the company must ensure the data is not publicly accessible throughout this process. A data engineer has secured Amazon S3 but must ensure the individual EMR clusters created by the analysts are not exposed to the public internet.Which solution should the data engineer to meet this compliance requirement with LEAST amount of effort?
Answer(s): C
Enabling the block public access setting for Amazon EMR at the account level prevents any EMR clusters from being reachable over the public internet by default, meeting the compliance requirement with minimal operational effort.A) Incorrect. EMR security configurations exist, but applying account-wide public access blocking is more comprehensive and requires less ongoing management.B) Incorrect. Monitoring security groups is reactive and labor-intensive; it does not guarantee no public exposure across all clusters.D) Incorrect. WAF protects web workloads; EMR cluster access is not primarily HTTP(S) traffic and WAF does not enforce EMR network access controls.
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-security-groups.html
A nancial company uses Amazon S3 as its data lake and has set up a data warehouse using a multi-node Amazon Redshift cluster. The data les in the data lake are organized in folders based on the data source of each data le. All the data les are loaded to one table in the Amazon Redshift cluster using a separateCOPY command for each data le location. With this approach, loading all the data les into Amazon Redshift takes a long time to complete. Users want a faster solution with little or no increase in cost while maintaining the segregation of the data les in the S3 data lake.Which solution meets these requirements?
Answer(s): D
Copying all file locations via a manifest ensures Redshift can parallelize the COPY without enumerating each file individually, preserving S3 data lake organization and avoiding extra data movement or separate processing steps.A) Using EMR to consolidate files adds cost and time; it changes data layout and increases processing outside Redshift.B) Loading to Aurora and ETL to Redshift introduces unnecessary cross-service data transfer and cost, plus latency.C) Glue job to copy into one folder introduces extra data movement and a separate ETL step, increasing cost and time.D) Manifest enables a single COPY command to ingest multiple files in parallel while preserving source organization.
https://docs.aws.amazon.com/redshift/latest/dg/r_COPY.html
A company's marketing team has asked for help in identifying a high performing long-term storage service for their data based on the following requirements:The data size is approximately 32 TB uncompressed.There is a low volume of single-row inserts each day.There is a high volume of aggregation queries each day.Multiple complex joins are performed.The queries typically involve a small subset of the columns in a table.Which storage service will provide the MOST performant solution?
Answer(s): B
A high-performing long-term storage solution for analytical workloads with large data, frequent aggregations, complex joins, and narrow-column queries is Amazon Redshift, which is optimized for OLAP workloads and columnar storage enabling efficient scans and aggregations on 32 TB. A) Aurora MySQL is row-oriented OLTP not ideal for complex analytical joins on large datasets. C) Neptune is a graph database suited for graph traversals, not columnar analytics. D) Elasticsearch excels at full-text search and certain aggregations but not large-scale relational joins or wide-column analytics as efficiently as Redshift.
A technology company is creating a dashboard that will visualize and analyze time-sensitive data. The data will come in through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support near-real-time data.Which visualization solution will meet these requirements?
A) OpensSearch/Elasticsearch with Kibana dashboards provides near-real-time visualization directly from Firehose data via continuous indexing and dashboards, aligning with 60-second buffering for near-real-time insights.B) Reading from S3 and analyzing in SageMaker introduces batch processing latency; not suitable for near-real-time dashboards with 60-second Firehose interval.C) Redshift with SPICE involves ETL/loading delays and is optimized for batch OLAP queries, not near-real-time streaming visualization.D) S3 + Glue catalog + Athena is serverless but Athena queries incur latency not ideal for near-real-time dashboards; SPICE with QuickSight on Athena is not optimized for streaming data ingestion.
A nancial company uses Apache Hive on Amazon EMR for ad-hoc queries. Users are complaining of sluggish performance.A data analyst notes the following:Approximately 90% of queries are submitted 1 hour after the market opens.Hadoop Distributed File System (HDFS) utilization never exceeds 10%.Which solution would help address the performance issues?
D) Correct: YARNMemoryAvailablePercentage scaling directly monitors resource pressure in the cluster where Hive queries run, enabling timely scale-out/in as memory pressure changes, addressing typical ad-hoc query latency during peak market hours.A) Uses CapacityRemainingGB, which measures disk capacity not necessarily tied to in-heap memory pressure or YARN containers; may react to storage capacity rather than compute/memory demand, making it less effective for query performance.B) Uses YARNMemoryAvailablePercentage for core/task node groups but misspecifies cluster group configuration nuances; also scaling policies require appropriate thresholds and metrics alignment.C) CapacityRemainingGB-based policies are correct for capacity, but express requirement pairs with memory metric; option D aligns both scaling actions with memory pressure in a Hive/EMR environment.
A media company has been performing analytics on log data generated by its applications. There has been a recent increase in the number of concurrent analytics jobs running, and the overall performance of existing jobs is decreasing as the number of new jobs is increasing. The partitioned data is stored inAmazon S3 One Zone-Infrequent Access (S3 One Zone-IA) and the analytic processing is performed on Amazon EMR clusters using the EMR File System(EMRFS) with consistent view enabled. A data analyst has determined that it is taking longer for the EMR task nodes to list objects in Amazon S3. Which action would MOST likely increase the performance of accessing log data in Amazon S3?
Using RCUs for the DynamoDB table increases metadata/query throughput for EMR’s metastore/lookups, reducing bottlenecks when listing objects via EMRFS consistent view and S3 operations, thus improving overall access performance for log data.A) Adding a random prefix to object keys does not address S3 listing latency or EMRFS metadata operations; it can even hinder predictable partitioning and auth optimization. B) Changing storage class to S3 Standard helps retrieval latency but S3 One Zone-IA is already optimized for infrequent access; the bottleneck is metadata/listing, not storage tier. D) Redeploying to another AZ does not resolve the root cause of S3 listing performance under EMRFS consistent view.
A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run. Which approach would allow the developers to solve the issue with minimal coding effort?
Enabling job bookmarks ensures AWS Glue tracks previously read data and processes only new incremental data on each run, minimizing code changes.A) Reading via DataFrame does not automatically track processed records; it still requires logic to identify deltas. B) Job bookmarks automatically persist state between runs and filter out already-processed S3 objects, matching the incremental-load requirement with minimal coding. C) Custom tracking logic would require additional development and maintenance, increasing complexity. D) Deleting processed data is destructive and not a reliable or scalable delta mechanism; it risks data loss and complicates reprocessing.
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