Free AWS Certified Data Analytics – Specialty Exam DAS-C01 Exam Practice Test

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?

• Enable detailed monitoring on Amazon EC2, use Amazon CloudWatch agent to store logs in Amazon S3, and use Amazon Athena for fast, interactive log analytics.
• Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and visualize using Amazon QuickSight.
• Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to Kinesis Data Firehose to further push the data to Amazon Elasticsearch Service and Kibana.
• Use Amazon CloudWatch subscriptions to get access to a real-time feed of logs and have the logs delivered to Amazon Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and Kibana.

A bank is using Amazon Managed Streaming for Apache Kafka (Amazon MSK) to populate real-time data into a data lake The data lake is built on Amazon S3, and data must be accessible from the data lake within 24 hours Different microservices produce messages to different topics in the cluster The cluster is created with 8 TB of Amazon Elastic Block Store (Amazon EBS) storage and a retention period of 7 daysThe customer transaction volume has tripled recently and disk monitoring has provided an alert that the cluster is almost out of storage capacityWhat should a data analytics specialist do to prevent the cluster from running out of disk space1?

• Use the Amazon MSK console to triple the broker storage and restart the cluster
• Create an Amazon CloudWatch alarm that monitors the KafkaDataLogsDiskUsed metric Automatically flush the oldest messages when the value of this metric exceeds 85%
• Create a custom Amazon MSK configuration Set the log retention hours parameter to 48 Update the cluster with the new configuration file
• Triple the number of consumers to ensure that data is consumed as soon as it is added to a topic.

A bank is building an Amazon S3 data lake. The bank wants a single data repository for customer data needs, such as personalized recommendations. The bank needs to use Amazon Kinesis Data Firehose to ingest customers' personal information, bank accounts, and transactions in near real time from a transactional relational database.All personally identifiable information (Pll) that is stored in the S3 bucket must be masked. The bank has enabled versioning for the S3 bucket.Which solution will meet these requirements?

• Invoke an AWS Lambda function from Kinesis Data Firehose to mask the PII before Kinesis Data Firehose delivers the data to the S3 bucket.
• Use Amazon Macie to scan the S3 bucket. Configure Macie to discover Pll. Invoke an AWS Lambda function from S3 events to mask the Pll.
• Configure server-side encryption (SSE) for the S3 bucket. Invoke an AWS Lambda function from S3 events to mask the PII.
• Create an AWS Lambda function to read the objects, mask the Pll, and store the objects back with
• same key. Invoke the Lambda function from S3 events.

A company wants to build a real-time data processing and delivery solution for streaming data. The data is being streamed through an Amazon Kinesis data stream. The company wants to use an Apache Flink application to process the data before writing the data to another Kinesis data stream. The data must be stored in an Amazon S3 data lake every 60 seconds for further analytics.Which solution will meet these requirements with the LEAST operational overhead?Host the Flink application on an Amazon EMR cluster. Use Amazon Kinesis Data Firehose to write the data to Amazon S3.Host the Flink application on Amazon Kinesis Data Analytics. Use AWS Glue to write the data to Amazon S3.Host the Flink application on an Amazon EMR cluster. Use AWS Glue to write the data to Amazon S3.Host the Flink application on Amazon Kinesis Data Analytics. Use Amazon Kinesis Data Firehose to write the data to Amazon S3.

An event ticketing website has a data lake on Amazon S3 and a data warehouse on Amazon Redshift. Two datasets exist: events data and sales data. Each dataset has millions of records.The entire events dataset is frequently accessed and is stored in Amazon Redshift. However, only the last 6 months of sales data is frequently accessed and is stored in Amazon Redshift. The rest of the sales data is available only in Amazon S3.A data analytics specialist must create a report that shows the total revenue that each event has generated in the last 12 months. The report will be accessed thousands of times each week.Which solution will meet these requirements with the LEAST operational effort?

• Create an AWS Glue job to access sales data that is older than 6 months from Amazon S3 and to access event and sales data from Amazon Redshift. Load the results into a new table in Amazon Redshift.
• Create a stored procedure to copy sales data that is older than 6 months and newer than 12 months from Amazon S3 to Amazon Redshift. Create a materialized view with the autorefresh option
• Create an AWS Lambda function to copy sales data that is older than 6 months and newer than 12 months to an Amazon Kinesis Data Firehose delivery stream. Specify Amazon Redshift as the destination of the delivery stream. Create a materialized view with the autorefresh option.
• Create a materialized view in Amazon Redshift with the autorefresh option. Use Amazon Redshift Spectrum to include sales data that is older than 6 months.

A company receives data from its vendor in JSON format with a timestamp in the file name. The vendor uploads the data to an Amazon S3 bucket, and the data is registered into the company’s data lake for analysis and reporting. The company has configured an S3 Lifecycle policy to archive all files to S3 Glacier after 5 days.The company wants to ensure that its AWS Glue crawler catalogs data only from S3 Standard storage and ignores the archived files. A data analytics specialist must implement a solution to achieve this goal without changing the current S3 bucket configuration.Which solution meets these requirements?

• Use the exclude patterns feature of AWS Glue to identify the S3 Glacier files for the crawler to exclude.
• Schedule an automation job that uses AWS Lambda to move files from the original S3 bucket to a new S3 bucket for S3 Glacier storage.
• Use the excludeStorageClasses property in the AWS Glue Data Catalog table to exclude files on S3 Glacier storage
• Use the include patterns feature of AWS Glue to identify the S3 Standard files for the crawler to include.

An ecommerce company stores customer purchase data in Amazon RDS. The company wants a solution to store and analyze historical data. The most recent 6 months of data will be queried frequently for analytics workloads. This data is several terabytes large. Once a month, historical data for the last 5 years must be accessible and will be joined with the more recent data. The company wants to optimize performance and cost.Which storage solution will meet these requirements?

• Create a read replica of the RDS database to store the most recent 6 months of data. Copy the historical data into Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3 and Amazon RDS. Run historical queries using Amazon Athena.
• Use an ETL tool to incrementally load the most recent 6 months of data into an Amazon Redshift cluster. Run more frequent queries against this cluster. Create a read replica of the RDS database to run queries on the historical data.
• Incrementally copy data from Amazon RDS to Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3. Use Amazon Athena to query the data.
• Incrementally copy data from Amazon RDS to Amazon S3. Load and store the most recent 6 months of data in Amazon Redshift. Configure an Amazon Redshift Spectrum table to connect to all historical data.

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 Elasticsearch Service (Amazon ES) and Amazon Aurora MySQL.Which solution will provide the MOST up-to-date results?

• Use AWS Glue jobs to ETL data from Amazon ES and Aurora MySQL to Amazon S3. Query the data with Amazon Athena.
• Use Amazon DMS to stream data from Amazon ES and Aurora MySQL to Amazon Redshift. Query the data with Amazon Redshift.
• Query all the datasets in place with Apache Spark SQL running on an AWS Glue developer endpoint.
• Query all the datasets in place with Apache Presto running on Amazon EMR.

A large ride-sharing company has thousands of drivers globally serving millions of unique customers every day. The company has decided to migrate an existing data mart to Amazon Redshift. The existing schema includes the following tables.A trips fact table for information on completed rides. A drivers dimension table for driver profiles. A customers fact table holding customer profile information.The company analyzes trip details by date and destination to examine profitability by region. The drivers data rarely changes. The customers data frequently changes.What table design provides optimal query performance?

• Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers and customers tables.
• Use DISTSTYLE EVEN for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.
• Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.
• Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact tables.

A company operates toll services for highways across the country and collects data that is used to understand usage patterns. Analysts have requested the ability to run traffic reports in near-real time. The company is interested in building an ingestion pipeline that loads all the data into an Amazon Redshift cluster and alerts operations personnel when toll traffic for a particular toll station does not meet a specified threshold. Station data and the corresponding threshold values are stored in Amazon S3.Which approach is the MOST efficient way to meet these requirements?

• Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneously. Create a reference data source in Kinesis Data Analytics to temporarily store the threshold values from Amazon S3 and compare the count of vehicles for a particular toll station against its corresponding threshold value. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.
• Use Amazon Kinesis Data Streams to collect all the data from toll stations. Create a stream in Kinesis Data Streams to temporarily store the threshold values from Amazon S3. Send both streams to Amazon Kinesis Data Analytics to compare the count of vehicles for a particular toll station against its corresponding threshold value. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met. Connect Amazon Kinesis Data Firehose to Kinesis Data Streams to deliver the data to Amazon Redshift.
• Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift. Then, automatically trigger an AWS Lambda function that queries the data in Amazon Redshift, compares the count of vehicles for a particular toll station against its corresponding threshold values read from Amazon S3, and publishes an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.
• Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneously. Use Kinesis Data Analytics to compare the count of vehicles against the threshold value for the station stored in a table as an in-application stream based on information stored in Amazon S3. Configure an AWS Lambda function as an output for the application that will publish an Amazon Simple Queue Service (Amazon SQS) notification to alert
• operations personnel if the threshold is not met.