A mortgage company has a microservice for accepting payments. This microservice uses the Amazon DynamoDB encryption client with AWS KMS managed keys to encrypt the sensitive data before writing the data to DynamoDB. The nance team should be able to load this data into Amazon Redshift and aggregate the values within the sensitive elds. The Amazon Redshift cluster is shared with other data analysts from different business units.Which steps should a data analyst take to accomplish this task e ciently and securely?
Answer(s): B
Using Lambda to process DynamoDB streams and output to a restricted S3 bucket, then loading into a restricted Redshift table with an IAM role that can access the KMS key ensures end-to-end encryption handling and secure access control for the finance data, matching DAS-C01 concepts.A) Incorrect: decrypting data in Lambda breaks KMS-by-design since encryption is managed by the client; introduces risk and potential policy misconfig. Also assumes a finance-only Redshift table but omits proper IAM/KMS-controlled loading.C) Incorrect: EMR/Hive approach adds unnecessary complexity and does not directly leverage the Redshift COPY with restricted access; cross-service encryption and IAM controls are less straightforward.D) Incorrect: writes back to S3 and uses EMR/Hive path; unnecessary for Redshift ingestion and increases surface area without added security or simplicity.
A company is building a data lake and needs to ingest data from a relational database that has time-series data. The company wants to use managed services to accomplish this. The process needs to be scheduled daily and bring incremental data only from the source into Amazon S3.What is the MOST cost-effective approach to meet these requirements?
Answer(s): A
Incremental ingestion with job bookmarks minimizes data transfer and cost by tracking processed records within Glue without extra state storage.A) Correct: AWS Glue with JDBC and job bookmarks captures incremental changes automatically, ensuring only new or updated rows are processed daily, leveraging managed ETL and minimal data movement.B) DynamoDB store of last updated key adds unnecessary state management; overkill and can complicate failed runs and reconciliation compared to Glue job bookmarks.C) Ingesting the full dataset and then delta requires expensive reprocessing and complex delta detection, increasing cost and processing time.D) DataSync is not designed for incremental ETL within Glue jobs; it would typically transfer full or delta data but lacks integrated incremental ETL control and scheduling in this context.
An Amazon Redshift database contains sensitive user data. Logging is necessary to meet compliance requirements. The logs must contain database authentication attempts, connections, and disconnections. The logs must also contain each query run against the database and record which database user ran each query.Which steps will create the required logs?
Answer(s): C
Enabling audit logging for Amazon Redshift records authentication attempts, connections, disconnections, and each query with the invoking user, satisfying compliance logging requirements.A) Incorrect: VPC Flow Logs capture network traffic, not database-level authentication/query activity or user-level query authorship.B) Incorrect: IAM-only access plus CloudTrail does not guarantee Redshift query-level logs or per-user query provenance inside the database.C) Correct: Redshift audit logging records database authentication attempts, connections, disconnections, and each query with the user, via console or CLI configuration.D) Incorrect: AWS Artifact is for compliance reports and not active audit logging of Redshift database activity.
https://docs.aws.amazon.com/redshift/latest/mgmt/db-auditing.html
A company that monitors weather conditions from remote construction sites is setting up a solution to collect temperature data from the following two weather stations.Station A, which has 10 sensorsStation B, which has ve sensorsThese weather stations were placed by onsite subject-matter experts.Each sensor has a unique ID. The data collected from each sensor will be collected using Amazon Kinesis Data Streams. Based on the total incoming and outgoing data throughput, a single Amazon Kinesis data stream with two shards is created. Two partition keys are created based on the station names. During testing, there is a bottleneck on data coming from Station A, but not from Station B. Upon review, it is con rmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput. How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?
Station A bottleneck is caused by data skew across partition keys; using sensor ID as the partition key distributes records more evenly across shards, improving parallelism without increasing cost. A) Increasing shards raises cost and complexity for marginal gain given throughput is already sufficient. B) A separate stream duplicates infrastructure, adds maintenance, and increases cost. C) Correct: partition key granularity changes data routing to shards, reducing hotspotting while preserving a single data stream. D) Reducing sensors degrades data collection requirements and is unacceptable; it does not address skew within Station A.
Once a month, a company receives a 100 MB .csv le compressed with gzip. The le contains 50,000 property listing records and is stored in Amazon S3 Glacier.The company needs its data analyst to query a subset of the data for a speci c vendor.What is the most cost-effective solution?
A) Loading into S3 and using S3 Select is not cost-effective for ad-hoc analytics across a subset; S3 Select is limited and not ideal for complex queries or large scans.B) Glacier Select is not available for standard S3 Glacier storage in this workflow; querying directly from Glacier incurs retrieval delays and is not the typical cost-effective analytics path.C) Loading to S3 and using Athena is serverless, pay-per-query, supports SQL on S3 data, and cost-efficient for infrequent queries on 100 MB compressed CSV; partitions and predicate pushdown reduce cost.D) Redshift Spectrum requires managing a data warehouse and can be more expensive for this small, infrequent query workload compared to on-demand Athena.
https://aws.amazon.com/athena/faqs/
A retail company is building its data warehouse solution using Amazon Redshift. As a part of that effort, the company is loading hundreds of les into the fact table created in its Amazon Redshift cluster. The company wants the solution to achieve the highest throughput and optimally use cluster resources when loading data into the company's fact table.How should the company meet these requirements?
Answer(s): D
A single COPY command to load the data is optimal because Redshift automatically parallelizes data loading across nodes, maximizing throughput and efficiently utilizing cluster resources without manual orchestration. It supports data from multiple S3 files in parallel, respects distribution and sort keys, and reduces client-side overhead.A) Multiple COPY commands can serialize work and introduce unnecessary overhead; parallelism is already achieved within a single COPY.B) S3DistCp with HDFS is not applicable to Redshift and adds complexity; Redshift loads directly from S3.C) Loading per node is not how Redshift parallelism is designed; the COPY command handles distribution across nodes automatically.D) Correct: single COPY command leverages Redshift’s internal parallelism for maximum throughput.
A data analyst is designing a solution to interactively query datasets with SQL using a JDBC connection. Users will join data stored in Amazon S3 in Apache ORC format with data stored in Amazon OpenSearch Service (Amazon Elasticsearch Service) and Amazon Aurora MySQL.Which solution will provide the MOST up-to-date results?
A concise explanation: B is correct because it enables near real-time querying by streaming changes from OLTP sources (Amazon ES and Aurora MySQL) into a centralized analytical store (Amazon Redshift) via AWS DMS, ensuring up-to-date results for joined datasets. A is incorrect because ETL into S3 introduces latency and data freshness lag, not suitable for most up-to-date queries. C is incorrect since Glue Spark on a dev endpoint is ad-hoc and not a managed, continuously up-to-date unified query layer. D is incorrect because Presto on EMR can query in place but does not inherently provide real-time streaming ingest or synchronized updates across disparate data stores.
A company developed a new elections reporting website that uses Amazon Kinesis Data Firehose to deliver full logs from AWS WAF to an Amazon S3 bucket.The company is now seeking a low-cost option to perform this infrequent data analysis with visualizations of logs in a way that requires minimal development effort.Which solution meets these requirements?
A) The combination of AWS Glue catalog with Athena for ad-hoc querying and QuickSight for visualizations provides low-cost, serverless analysis with minimal development effort. Glue crawlers automatically catalog logs stored in S3, Athena enables SQL-based analyses without provisioning, and QuickSight offers ready-to-use dashboards. B) OpenSearch/Elasticsearch adds operational overhead and is more suited for full-text search, not low-cost, infrequent analytics with minimal setup. C) Lambda-based transformation plus Redshift introduces provisioning and maintenance cost; Redshift is less cost-efficient for infrequent queries. D) EMR/Spark requires cluster management and higher compute cost, which contradicts “low-cost” and minimal development effort.
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