A company's data analyst needs to ensure that queries run in Amazon Athena cannot scan more than a prescribed amount of data for cost control purposes.Queries that exceed the prescribed threshold must be canceled immediately.What should the data analyst do to achieve this?
Answer(s): B
Athena supports per-workgroup query data usage controls; setting the control limit for each query in a workgroup enforces the threshold and cancels queries that exceed it.A) Not correct: Lambda-triggered cancellations are not the standard, scalable mechanism for per-query data usage throttling in Athena.C) Not correct: S3 bucket policies cannot enforce per-query data scanned limits or cancel running queries.D) Not correct: Workgroup-wide data usage limit exists, but the established mechanism is per-query control; wording implies a different configuration path than the supported per-query limit.B) Correct: Configuring the per-query control limit within the workgroup directly enforces the prescribed data-scan threshold.
https://docs.aws.amazon.com/athena/latest/ug/workgroups-setting-control-limits-cloudwatch.html
A marketing company is using Amazon EMR clusters for its workloads. The company manually installs third-party libraries on the clusters by logging in to the master nodes. A data analyst needs to create an automated solution to replace the manual process.Which options can ful ll these requirements? (Choose two.)
Answer(s): A,E
Automating bootstrap actions and custom AMIs provides reproducible EMR environments without manual logins.A) Correct. Placing installation scripts in S3 and running them as EMR bootstrap actions automates per-cluster setup during provisioning. B) Incorrect. Running scripts via Apache Spark is not a cluster-provisioning step and would not automate per-cluster installation before jobs run. C) Incorrect. Modifying the master node and creating a custom AMI risks drift and is less scalable; EMR supports bootstrap/AMI-based approaches but this option is not as clean as A or E. D) Incorrect. DynamoDB + Lambda introduces event-driven installation but is not a standard, reliable per-cluster bootstrap mechanism for EMR software installation. E) Correct. Creating an AMI with the required libraries and re-creating EMR clusters from that AMI ensures consistent, automated environments.
A data engineering team within a shared workspace company wants to build a centralized logging system for all weblogs generated by the space reservation system. The company has a eet of Amazon EC2 instances that process requests for shared space reservations on its website. The data engineering team wants to ingest all weblogs into a service that will provide a near-real-time search engine. The team does not want to manage the maintenance and operation of the logging system.Which solution allows the data engineering team to e ciently set up the web logging system within AWS?
The correct answer enables managed, near-real-time log ingestion with scalable search by using a fully managed delivery stream to a search service.A) Incorrect: Subscribing a Kinesis data stream to CloudWatch is not the standard pattern; CloudWatch Logs should feed a Firehose or a direct destination, and OpenSearch should be the endpoint via Firehose, not via a Kinesis data stream subscription.B) Correct: CloudWatch Logs ingest weblogs, Kinesis Data Firehose delivers to OpenSearch Service, enabling near-real-time search with a managed, serverless pipeline and reduced operational overhead.C) Incorrect: Splunk is not a native AWS managed service integration here; using a Kinesis data stream to CloudWatch and then Splunk as a destination requires additional integration and management, increasing operational load.D) Incorrect: DynamoDB is not a suitable end destination for log search workloads; it’s a NoSQL store, not a search-optimized service like OpenSearch, making it inappropriate for near-real-time log search.
A company wants to research user turnover by analyzing the past 3 months of user activities. With millions of users, 1.5 TB of uncompressed data is generated each day. A 30-node Amazon Redshift cluster with 2.56 TB of solid state drive (SSD) storage for each node is required to meet the query performance goals.The company wants to run an additional analysis on a year's worth of historical data to examine trends indicating which features are most popular. This analysis will be done once a week.What is the MOST cost-effective solution?
Moving older data to S3 with Parquet and using Redshift Spectrum is most cost-effective for infrequent weekly analysis on vast historical data, since Spectrum allows querying external data without resizing or over-provisioning the cluster. A) Increasing to 120 nodes is cost-inefficient and unnecessary just for one weekly analysis. B) Correct: keeps hot data in Redshift, archives cold data to S3 Parquet, uses Spectrum for scalable external querying. C) Persistent EMR/Presto adds maintenance and cost for a weekly job, less seamless than Spectrum. D) DS2 dense storage adds capital cost per node without addressing the weekly analysis cost efficiency.
A bank operates in a regulated environment. The compliance requirements for the country in which the bank operates say that customer data for each state should only be accessible by the bank's employees located in the same state. Bank employees in one state should NOT be able to access data for customers who have provided a home address in a different state. The bank's marketing team has hired a data analyst to gather insights from customer data for a new campaign being launched in certain states. Currently, data linking each customer account to its home state is stored in a tabular .csv le within a single Amazon S3 folder in a private S3 bucket. The total size of the S3 folder is 2 GB uncompressed. Due to the country's compliance requirements, the marketing team is not able to access this folder.The data analyst is responsible for ensuring that the marketing team gets one-time access to customer data for their campaign analytics project, while being subject to all the compliance requirements and controls.Which solution should the data analyst implement to meet the desired requirements with the LEAST amount of setup effort?
Answer(s): D
Access is granted at the visualization layer with least setup: QuickSight Enterprise supports row-level security on data sources imported from S3, aligning with the requirement to restrict by home state for marketing users without complex infrastructure. A) involves reorganizing data and brittle policies with post-project cleanup; policy maintenance is error-prone. B) requires custom Hadoop security on EMR and long setup, increasing operational burden. C) uses Redshift RLS but adds data movement and table lifecycle; more overhead and longer to implement. D) provides built-in RLS with minimal setup and easy deletion post-project.A) Re-arrange data in S3 and policy-based accessB) Custom Hadoop-based RLS on EMRC) Redshift with COPY and RLSD) QuickSight Enterprise with built-in RLS
An online gaming company is using an Amazon Kinesis Data Analytics SQL application with a Kinesis data stream as its source. The source sends three non-null elds to the application: player_id, score, and us_5_digit_zip_code. A data analyst has a .csv mapping le that maps a small number of us_5_digit_zip_code values to a territory code. The data analyst needs to include the territory code, if one exists, as an additional output of the Kinesis Data Analytics application.How should the data analyst meet this requirement while minimizing costs?
Answer(s): C
Storing the mapping as a reference data source and joining a reference table in the Kinesis Data Analytics SQL application minimizes cost by leveraging built-in reference data support and avoids per-record Lambda processing.A) Incorrect: Using Lambda to fetch mapping adds per-record processing and extra egress/invocation costs; defeats built-in reference data optimization.B) Incorrect: Referencing an S3 data file via ARN is not the standard reference data source mechanism for Kinesis Data Analytics; lacks automatic refreshing and efficient joins.C) Correct: Kinesis Data Analytics supports reference data sources from S3; this enables a join to a reference table with low latency and minimal cost, adding territory_code in SELECT.D) Incorrect: Involves Lambda in the data path and separate forwarding, introducing extra compute and IO costs; not the optimized reference data approach.
A company has collected more than 100 TB of log les in the last 24 months. The les are stored as raw text in a dedicated Amazon S3 bucket. Each object has a key of the form year-month-day_log_HHmmss.txt where HHmmss represents the time the log le was initially created. A table was created in Amazon Athena that points to the S3 bucket. One-time queries are run against a subset of columns in the table several times an hour.A data analyst must make changes to reduce the cost of running these queries. Management wants a solution with minimal maintenance overhead.Which combination of steps should the data analyst take to meet these requirements? (Choose three.)
Answer(s): B,C,F
The correct answer optimizes storage format and partitioning with minimal upkeep. Parquet (C) provides columnar storage and improved compression for frequent subset queries, reducing I/O and cost. Date-partitioning using a year-month-day path prefix (D/B) enables pruning; B uses date=year-month-day/ as a partition key, aligning with Athena/Glue partition discovery and query planning, and F repairs table partitions after adding them, ensuring metadata consistency. Incorrect: A converts to Avro which is not as space- or query-optimized for this workload. E and F: E would require rebuilding with PARTITIONED BY and manual partition addition; D alone lacks partition pruning compatibility without proper metadata repair. F is required to recognize new partitions after addition. A) B) C) F
https://docs.aws.amazon.com/athena/latest/ug/msck-repair-table.html
A company has an application that ingests streaming data. The company needs to analyze this stream over a 5-minute timeframe to evaluate the stream for anomalies with Random Cut Forest (RCF) and summarize the current count of status codes. The source and summarized data should be persisted for future use.Which approach would enable the desired outcome while keeping data persistence costs low?
RCF in Kinesis Data Analytics with a 5-minute window and persisting both source and results to S3 is cost-effective and fits stream processing plus anomaly detection in real time while enabling batch export for long-term storage.A) Uses Lambda for per-record processing and DynamoDB; inefficient for 5-minute window and higher latency, and lacks scalable windowed analytics with RCF within Kinesis Analytics.C) Firehose batch delivery and Lambda later is unnecessary complexity; DynamoDB persistence adds cost and latency; redundant with S3 storage.D) Uses 1-minute window in Analytics and 5-minute ingest to S3; mismatch between windowing and delivery, plus indirect processing path and potential latency.B) Correct: native Kinesis Data Analytics windowed processing with RCF, and S3 as durable storage for both source and results.
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