AWS Certified Data Analytics – Specialty (DAS-C01) Exam Simulator #1

Which of the following is the best way to evaluate that the data loss is within tolerance limits in the event of failures in a collection system?

What is the best way to prevent data loss in the event of failures in a collection system?

Which of the following is the best way to evaluate the costs associated with data acquisition, transfer, and provisioning from various sources into the collection system (networking, bandwidth)?

Which of the following is the most cost-effective way to transfer data from a remote data center to an AWS collection system?

Which of the following is the most important factor to consider when evaluating costs associated with data acquisition, transfer, and provisioning from various sources into the collection system (ETL/data migration costs)?

Which of the following is the most cost-effective way to transfer data from an on-premises data center to an AWS collection system?

A company is using an AWS service to collect and process data from various sources, including a legacy system that is known for frequent downtimes. The company needs to assess the failure scenarios that the collection system may undergo and take remediation actions based on the impact. Which of the following remediation actions should the company take to minimize the impact of a failure scenario?

A company is using an AWS service to collect and process data from various sources, including sensors and social media platforms. The company needs to ensure that the collection system can withstand a failure scenario without compromising data quality. Which of the following actions should the company take to remediate the failure scenario and minimize the impact?

Which of the following is an example of a point of data capture where data persistence needs to be determined?

Which of the following is an example of a collection system with high latency?

A company is collecting data from multiple sources, including social media, website clicks, and IoT devices. The data coming from social media and website clicks is mostly batch data, while the data coming from IoT devices is streaming data. What collection system would be the most suitable for handling this data?

A company is collecting data from various sources, including a web application, a mobile application, and a third-party vendor. The data from the web and mobile applications is transactional data, while the data from the third-party vendor is batch data. What collection system would be the most suitable for handling this data?

A data analytics company needs to collect streaming data from a high-frequency trading platform. Which AWS service is best suited for this use case?

A retail company needs to collect data from transactional databases on a daily basis and process it using a batch processing system. Which AWS service is best suited for this use case?

A company needs to collect and process high-volume, high-velocity data from various sources. They require a service that can scale dynamically, provide fault tolerance, and support real-time analytics. Which AWS service would best fit their requirements?

A company needs to collect and process data from various sources with high fault tolerance and low latency. Which AWS service would be best suited for this use case?

Which data collection system can handle high throughput and large volumes of data with the ability to recover from faults?

Which data collection system is suitable for handling infrequent data updates, low volumes of data, and integration with AWS services?

Which collection solution is suitable for handling real-time data with a requirement for secure, reliable connectivity?

Which collection solution is suitable for handling high-volume, batch-oriented data with the requirement for network encryption?

What AWS service can be used for tagging and classifying data at the object level in an S3 bucket?

Which AWS service can be used for cataloging data and managing metadata in a data lake environment?

Which of the following mechanisms can be used to search and retrieve data catalogs and metadata in Amazon S3?

Which AWS service can be used for searching and retrieving data catalogs and metadata?

Which AWS service can be used to create a searchable catalog of data and metadata across multiple AWS accounts and regions?

What mechanism can be used to create and maintain a data catalog in AWS Glue?

Which of the following mechanisms would be the best option to discover new and updated data sources in an AWS environment?

A company has a high volume of structured data that needs to be accessed frequently for analysis. Which AWS database service would be the best option for storing and managing this data?

A company has a large amount of historical data that is rarely accessed but needs to be kept for compliance reasons. Which AWS storage solution would be the best option to store this data?

What is the best strategy to manage data that is frequently accessed but rarely changed?

What should be the first step in determining the strategy to address data lifecycle requirements?

A company is analyzing a large dataset of customer reviews for a product. The dataset is stored in Amazon S3 and contains information such as the review text, rating, and reviewer ID. The company wants to optimize query performance by using a data format that can compress the data and minimize the amount of storage space used. Which data format should they use?

A company has a large dataset that is stored in multiple files on Amazon S3. The dataset is frequently accessed by a data analytics team that runs complex queries on it. The company needs to decide on the best data layout to use for this dataset to optimize cost and performance. Which data layout should they choose?

A company has a large dataset that it needs to process regularly for analytics purposes. The dataset is comprised of multiple files that range in size from 1GB to 10GB. The company needs to decide on the best file format to use for this dataset. Which file format should they choose to optimize cost and performance?

Which of the following is the best storage layout for a data warehouse that requires fast querying and reporting capabilities?

Which of the following data layout and distribution strategies is best suited for a large dataset that needs to be queried frequently with high performance requirements?

Which of the following encoding strategies is suitable for a data lake workload where data compression and query performance are important, and data encoding should be self-describing?

Which of the following compression strategies is suitable for a data warehouse workload where a high degree of compression is required, and the focus is on query performance?

A data analytics team needs to store large amounts of data that will be used primarily for data warehousing and business intelligence. Which storage format is suitable for this use case?

A data analytics team is working with large amounts of data that requires frequent updates and querying. Which storage format is suitable for this use case?

What is a suitable way to handle schema evolution requirements in a relational database environment?

Which of the following is a suitable mechanism to address schema evolution requirements in an Amazon S3 data lake environment?

A gaming company needs to store and query data from a high-performance analytics platform that requires low latency and high throughput. The data is structured and requires frequent updates. What storage solution is most appropriate to address query latency requirements?

A company needs to query a large dataset with sub-second response times. The data is mostly unstructured and the queries require ad-hoc analysis. What storage solution is most appropriate to address query latency requirements?

A data analytics team needs to store and process semi-structured data generated by social media feeds. The data needs to be analyzed using Apache Spark and must be able to handle unpredictable data growth. What storage solution is most appropriate for semi-structured data in this scenario?

A company is looking to store large amounts of structured data generated by their ERP system. The data needs to be accessed frequently by various departments within the company. What storage solution is most appropriate for structured data in this scenario?

A data analyst needs to store and process a large amount of data that is frequently accessed by multiple users. The data is used to train machine learning models and is expected to be updated frequently. What storage solution is most appropriate for transient storage in this scenario?

A data analytics team has been tasked with analyzing a large amount of sensor data generated from a fleet of trucks. The data will be analyzed using Apache Spark and will need to be stored for at least 5 years for compliance purposes. What storage solution is most appropriate for long-term storage in this scenario?

Which storage solution is appropriate for a workload that has frequent updates to data?

Which storage solution is appropriate for a workload that has append-only changes to data?

A data analytics team is using Amazon EMR to run Apache Spark jobs to process large datasets. They want to deploy a monitoring solution to track and troubleshoot issues in real-time. Which AWS service would be the best fit for this scenario?

A data analytics team is using AWS Glue to run ETL jobs on a daily basis. They want to deploy a logging and monitoring solution to audit and trace the data processing activities. Which AWS service would be the best fit for this scenario?

You are designing a data processing solution for a healthcare company that requires high availability and fault tolerance. You need to ensure that the system can recover from processing failures without data loss. Which AWS service should you use for this purpose?

You are tasked with designing a data processing solution on AWS for a financial institution. You need to ensure that the system can recover from processing failures in a timely manner. Which AWS service should you use for this purpose?

Which AWS service can be used to automate the creation of machine learning workflows for data processing solutions?

Which AWS service can be used to automate the deployment and scaling of containerized applications for data processing solutions?

Which of the following tools can be used to aggregate and enrich data for downstream consumption in a batch processing data analytics solution?

Which of the following services can be used to aggregate and enrich data in real-time for downstream consumption in a data analytics solution?

You are designing an AWS data analytics solution that requires a large number of data transformations. The solution needs to be highly scalable and fault-tolerant, and you want to minimize the amount of custom code that you need to write. Which AWS service can you use to create and manage the data transformation workflow?

You need to design an AWS data analytics solution that performs a series of data transformations before loading data into Amazon Redshift for analysis. The solution needs to be highly scalable and resilient. Which AWS service can you use to create and orchestrate the data transformation workflow?

What AWS service can be used to optimize the cost of processing large amounts of data by reducing the amount of data that needs to be processed?

What AWS service can be used to optimize the cost of processing data stored in Amazon S3 by automatically moving infrequently accessed data to a lower-cost storage class?

Which mechanism can be used to replicate Amazon S3 objects between regions for disaster recovery purposes?

Which mechanism can be used to automatically scale an Amazon EMR cluster based on workload demands?

Which ETL technique is appropriate for processing real-time streaming data and integrating with other AWS services?

A company needs to implement a data visualization solution that can handle complex data relationships and provide advanced analytics. Which data visualization solution would be most appropriate for this scenario?

Which data visualization solution would be most appropriate for a scenario where a company needs to visualize and analyze large volumes of data from multiple sources, including on-premises and cloud-based databases?

A company is looking to analyze and visualize large amounts of data stored in multiple databases located in different regions. Which of the following data access mechanisms would be the most appropriate for this scenario?

A data analytics team needs to analyze and visualize data from a large, constantly updated database to identify trends and anomalies in real-time. Which of the following data access mechanisms would be the most appropriate for this scenario?

A company wants to analyze sales data to identify trends and outliers. They also want to be able to pivot the data by product category and geographic region. Which AWS service should be used for this task?

A company wants to analyze website traffic data to identify which pages are most popular among visitors. They also want to be able to drill down to see traffic by time of day and day of the week. Which AWS service should be used for this task?

A company needs to create a dashboard that displays monthly financial data from its Amazon S3 bucket. What data refresh schedule should be used for the dashboard?

A company has a dashboard that displays real-time sales data for its e-commerce website. The sales data is stored in an Amazon Redshift data warehouse. What data refresh schedule should be used for the dashboard to ensure the data is up-to-date?

Which of the following data visualization solutions is best suited for a scenario where an analyst needs to create interactive visualizations and share them with stakeholders?

Which AWS service is the best option for delivering real-time insights on web traffic to marketing teams via email, with customized dashboards and visualizations?

Which AWS service is the best option for delivering daily reports to stakeholders in a CSV format via email, with minimal configuration required?

A healthcare organization wants to develop a mobile application that allows doctors to monitor patient data in real-time. Which of the following data visualization solutions is the most appropriate for this scenario?

A company wants to develop a mobile application that displays real-time stock market data for users. Which of the following data visualization solutions is the most appropriate for this scenario?

Which web delivery method is most appropriate for delivering real-time data updates to a large number of users simultaneously?

Which data visualization solution is most suitable for creating interactive dashboards and reports that allow users to explore data at a granular level?

Correct Answer: A. Amazon QuickSight

Amazon QuickSight is a cloud-powered business intelligence service that provides interactive dashboards and reports for visualizing and exploring data at a granular level. It is an excellent solution for data analysts and business users who want to gain insights into their data quickly and easily. With QuickSight, users can create interactive dashboards and reports that allow them to slice and dice data, drill down into details, and explore data at any level of granularity.

Option A is correct because Amazon QuickSight is a cloud-powered business intelligence service that provides interactive dashboards and reports for visualizing and exploring data at a granular level.
Option B is incorrect because Amazon Kinesis Data Analytics is a streaming data analysis service that is used to process and analyze real-time data streams. It is not a suitable solution for creating interactive dashboards and reports.
Option C is incorrect because Amazon Elasticsearch Service is a search and analytics engine that is used to search, analyze, and visualize large amounts of data. While it can be used to create visualizations, it is not an ideal solution for creating interactive dashboards and reports.
Option D is incorrect because Amazon Redshift is a data warehousing service that is used to store and analyze large amounts of data. While it can be used to create visualizations, it is not an ideal solution for creating interactive dashboards and reports.

A healthcare provider wants to analyze its patient data to identify potential risk factors for certain diseases. Which AWS data analysis solution would be the most appropriate for this scenario?

A retail company wants to analyze its sales data to determine which products are selling the most and which stores are generating the most revenue. Which AWS data analysis solution would be the most appropriate for this scenario?

A company wants to analyze its e-commerce data to identify trends in customer behavior and optimize their sales strategy. Which AWS data analysis solution would be the most appropriate for this scenario?

A company wants to analyze its social media data to determine customer sentiment towards their brand. Which AWS data analysis solution would be the most appropriate for this scenario?

Which of the following is true regarding Amazon RDS encryption?

Which of the following is a feature of Amazon S3 server-side encryption?

Correct Answer: B It encrypts data at rest with AES-256 encryption.

Explanation:Amazon S3 server-side encryption uses AES-256 encryption to encrypt data at rest. It does not use client-side encryption with AWS KMS-managed keys, nor does it encrypt data in transit using SSL/TLS. Although it is possible to use a user-defined encryption key, it is not a feature of Amazon S3 server-side encryption.

Option A is incorrect because Amazon S3 server-side encryption does not use client-side encryption with AWS KMS-managed keys.
Option C is incorrect because Amazon S3 server-side encryption does not encrypt data in transit using SSL/TLS.
Option D is incorrect because while it is possible to use a user-defined encryption key, it is not a feature of Amazon S3 server-side encryption.

A data scientist wants to use AWS CloudHSM to generate random numbers for cryptographic operations. Which of the following CloudHSM features should they use?

A data analyst wants to use AWS CloudHSM to store their encryption keys. Which of the following CloudHSM features should they use?

A data scientist wants to encrypt data on an EC2 instance using AWS KMS. Which of the following AWS KMS features should they use?

A data analyst wants to use AWS KMS to protect their data at rest on Amazon S3. Which of the following AWS KMS features should they use?

A data scientist is working on a project that requires the use of client-side encryption with a high level of security. Which of the following encryption approaches will best suit their needs?

A data analyst is working on a project that requires the use of client-side encryption. Which of the following encryption approaches will best suit their needs?

A data analyst needs to encrypt data stored in an Amazon EBS volume and is considering using server-side encryption with Amazon-managed keys (SSE-S3). Which of the following statements is true regarding SSE-S3?

A data analyst needs to encrypt data stored in an Amazon S3 bucket and is considering using server-side encryption with AWS KMS-managed keys (SSE-KMS). Which of the following statements is true regarding SSE-KMS?

A data analyst is working with a large dataset that contains sensitive customer data, and needs to mask some of the data fields to protect customer privacy. Which AWS service can the analyst use to meet this requirement?

A data analyst is working with sensitive customer data and needs to determine the encryption requirements for storing the data in an Amazon S3 bucket. The data should only be accessible by authorized users. Which AWS service can the analyst use to meet these requirements?

A data analyst needs to implement role-based access control for an Amazon Redshift cluster. Which of the following mechanisms should the analyst use to control access to the cluster?

A data analytics team wants to implement role-based access control for an Amazon S3 bucket. Which of the following mechanisms should the team use to control access to the bucket?

A data analyst needs to control access to an Amazon RDS database instance. Which of the following mechanisms should the analyst use to control traffic to and from the instance?

A data analyst needs to implement access control for a new Amazon EC2 instance. Which of the following mechanisms should the analyst use to control traffic to and from the instance?