Here you have the best Amazon DAS-C01 practice exam questions
A financial services company needs to aggregate daily stock trade data from the exchanges into a data store. The company requires that data be streamed directly into the data store, but also occasionally allows data to be modified using SQL. The solution should integrate complex, analytic queries running with minimal latency. The solution must provide a business intelligence dashboard that enables viewing of the top contributors to anomalies in stock prices.
Which solution meets the company's requirements?
Using Amazon Kinesis Data Firehose to stream data to Amazon Redshift is the best option for a financial services company that needs to aggregate daily stock trade data and occasionally modify data using SQL. Kinesis Data Firehose reliably loads streaming data into Redshift with minimal latency, and Amazon Redshift supports complex, analytical queries with low latency which is crucial for business intelligence use cases. Additionally, using Amazon QuickSight with Amazon Redshift as the data source enables the creation of business intelligence dashboards to visualize anomalies in stock prices effectively.
A financial company hosts a data lake in Amazon S3 and a data warehouse on an Amazon Redshift cluster. The company uses Amazon QuickSight to build dashboards and wants to secure access from its on-premises Active Directory to Amazon QuickSight.
How should the data be secured?
To secure access from an on-premises Active Directory to Amazon QuickSight, using an Active Directory connector and single sign-on (SSO) in a corporate network environment is the correct approach. This method leverages the existing Active Directory infrastructure for authentication, allowing users to log in with their corporate credentials. Additionally, it enables IT administrators to manage access to Amazon QuickSight through their existing Active Directory, thereby aligning with the company's security policies and ensuring a seamless and secure user experience.
A real estate company has a mission-critical application using Apache HBase in Amazon EMR. Amazon EMR is configured with a single master node. The company has over 5 TB of data stored on an Hadoop Distributed File System (HDFS). The company wants a cost-effective solution to make its HBase data highly available.
Which architectural pattern meets company's requirements?
To make HBase data highly available, it is essential to ensure both data availability and fault tolerance. The most effective approach is to store the data on an EMR File System (EMRFS) instead of HDFS, as EMRFS uses Amazon S3 which is highly durable. Configuring a primary EMR HBase cluster with multiple master nodes ensures that the cluster can withstand master node failures. Additionally, creating a secondary EMR HBase read-replica cluster in a separate Availability Zone provides redundancy and fault tolerance against Availability Zone failures. This setup allows both clusters to access the same HBase root directory in the S3 bucket, ensuring data availability and lowering costs by avoiding redundant data storage. This architecture effectively balances cost and high availability requirements.
A software company hosts an application on AWS, and new features are released weekly. As part of the application testing process, a solution must be developed that analyzes logs from each Amazon EC2 instance to ensure that the application is working as expected after each deployment. The collection and analysis solution should be highly available with the ability to display new information with minimal delays.
Which method should the company use to collect and analyze the logs?
To meet the requirement of collecting and analyzing logs with minimal delays, a streaming solution integrated with a search and visualization tool is necessary. Using the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to Kinesis Data Firehose, the data can then be pushed to Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) and visualized using OpenSearch Dashboards (Kibana). This option ensures that logs are collected in near real-time, processed efficiently, and made available for visualization with minimal delays. Although minor discrepancies exist in terminology, the described method closely aligns with the desired technical outcome and highly available infrastructure.
A data analyst is using AWS Glue to organize, cleanse, validate, and format a 200 GB dataset. The data analyst triggered the job to run with the Standard worker type. After 3 hours, the AWS Glue job status is still RUNNING. Logs from the job run show no error codes. The data analyst wants to improve the job execution time without overprovisioning.
Which actions should the data analyst take?
To improve the execution time of an AWS Glue job without overprovisioning, enable job metrics to estimate the number of data processing units (DPUs) needed. Based on these metrics, increase the value of the maximum capacity job parameter. This approach helps allocate the precise amount of resources required for the job, improving performance while avoiding unnecessary resource usage. Enabling job bookmarks is more about maintaining state information and preventing the reprocessing of old data, which does not directly impact performance. Adjusting parameters like executor cores or memory overhead depends on specific error codes, which are not present in this scenario.