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Question 1:

A data engineer chooses Amazon DynamoDB as a data store for a regulated application. This application must be submitted to regulators for review. The data engineer needs to provide a control framework that lists the security controls from

the process to follow to add new users down to the physical controls of the data center, including items like security guards and cameras.

How should this control mapping be achieved using AWS?

A. Request AWS third-party audit reports and/or the AWS quality addendum and map the AWS responsibilities to the controls that must be provided.

B. Request data center Temporary Auditor access to an AWS data center to verify the control mapping.

C. Request relevant SLAs and security guidelines for Amazon DynamoDB and define these guidelines within the application\’s architecture to map to the control framework.

D. Request Amazon DynamoDB system architecture designs to determine how to map the AWS responsibilities to the control that must be provided.

Correct Answer: A


Question 2:

An administrator needs to design a distribution strategy for a star schema in a Redshift cluster. The administrator needs to determine the optimal distribution style for the tables in the Redshift schema. In which three circumstances would choosing Key-based distribution be most appropriate? (Select three.)

A. When the administrator needs to optimize a large, slowly changing dimension table.

B. When the administrator needs to reduce cross-node traffic.

C. When the administrator needs to optimize the fact table for parity with the number of slices.

D. When the administrator needs to balance data distribution and collocation data.

E. When the administrator needs to take advantage of data locality on a local node for joins and aggregates.

Correct Answer: ACD


Question 3:

A large grocery distributor receives daily depletion reports from the field in the form of gzip archives od CSV files uploaded to Amazon S3. The files range from 500MB to 5GB. These files are processed daily by an EMR job. Recently it has been observed that the file sizes vary, and the EMR jobs take too long. The distributor needs to tune and optimize the data processing workflow with this limited information to improve the performance of the EMR job. Which recommendation should an administrator provide?

A. Reduce the HDFS block size to increase the number of task processors.

B. Use bzip2 or Snappy rather than gzip for the archives.

C. Decompress the gzip archives and store the data as CSV files.

D. Use Avro rather than gzip for the archives.

Correct Answer: B


Question 4:

A web-hosting company is building a web analytics tool to capture clickstream data from all of the websites hosted within its platform and to provide near-real-time business intelligence. This entire system is built on AWS services. The web-hosting company is interested in using Amazon Kinesis to collect this data and perform sliding window analytics.

What is the most reliable and fault-tolerant technique to get each website to send data to Amazon Kinesis with every click?

A. After receiving a request, each web server sends it to Amazon Kinesis using the Amazon Kinesis PutRecord API. Use the sessionID as a partition key and set up a loop to retry until a success response is received.

B. After receiving a request, each web server sends it to Amazon Kinesis using the Amazon Kinesis Producer Library .addRecords method.

C. Each web server buffers the requests until the count reaches 500 and sends them to Amazon Kinesis using the Amazon Kinesis PutRecord API call.

D. After receiving a request, each web server sends it to Amazon Kinesis using the Amazon Kinesis PutRecord API. Use the exponential back-off algorithm for retries until a successful response is received.

Correct Answer: A


Question 5:

A customer has an Amazon S3 bucket. Objects are uploaded simultaneously by a cluster of servers from multiple streams of data. The customer maintains a catalog of objects uploaded in Amazon S3 using an Amazon DynamoDB table. This

catalog has the following fileds: StreamName, TimeStamp, and ServerName, from which ObjectName can be obtained.

The customer needs to define the catalog to support querying for a given stream or server within a defined time range.

Which DynamoDB table scheme is most efficient to support these queries?

A. Define a Primary Key with ServerName as Partition Key and TimeStamp as Sort Key. Do NOT define a Local Secondary Index or Global Secondary Index.

B. Define a Primary Key with StreamName as Partition Key and TimeStamp followed by ServerName as Sort Key. Define a Global Secondary Index with ServerName as partition key and TimeStamp followed by StreamName.

C. Define a Primary Key with ServerName as Partition Key. Define a Local Secondary Index with StreamName as Partition Key. Define a Global Secondary Index with TimeStamp as Partition Key.

D. Define a Primary Key with ServerName as Partition Key. Define a Local Secondary Index with TimeStamp as Partition Key. Define a Global Secondary Index with StreamName as Partition Key and TimeStamp as Sort Key.

Correct Answer: A


Question 6:

A company has several teams of analysts. Each team of analysts has their own cluster. The teams need to run SQL queries using Hive, Spark-SQL, and Presto with Amazon EMR. The company needs to enable a centralized metadata layer to expose the Amazon S3 objects as tables to the analysts.

Which approach meets the requirement for a centralized metadata layer?

A. EMRFS consistent view with a common Amazon DynamoDB table

B. Bootstrap action to change the Hive Metastore to an Amazon RDS database

C. s3distcp with the outputManifest option to generate RDS DDL

D. Naming scheme support with automatic partition discovery from Amazon S3

Correct Answer: A


Question 7:

A company receives data sets coming from external providers on Amazon S3. Data sets from different providers are dependent on one another. Data sets will arrive at different times and in no particular order. A data architect needs to design a solution that enables the company to do the following:

1.

Rapidly perform cross data set analysis as soon as the data becomes available

2.

Manage dependencies between data sets that arrive at different times

Which architecture strategy offers a scalable and cost-effective solution that meets these requirements?

A. Maintain data dependency information in Amazon RDS for MySQL. Use an AWS Data Pipeline job to load an Amazon EMR Hive table based on task dependencies and event notification triggers in Amazon S3.

B. Maintain data dependency information in an Amazon DynamoDB table. Use Amazon SNS and event notifications to publish data to fleet of Amazon EC2 workers. Once the task dependencies have been resolved, process the data with Amazon EMR.

C. Maintain data dependency information in an Amazon ElastiCache Redis cluster. Use Amazon S3 event notifications to trigger an AWS Lambda function that maps the S3 object to Redis. Once the task dependencies have been resolved, process the data with Amazon EMR.

D. Maintain data dependency information in an Amazon DynamoDB table. Use Amazon S3 event notifications to trigger an AWS Lambda function that maps the S3 object to the task associated with it in DynamoDB. Once all task dependencies have been resolved, process the data with Amazon EMR.

Correct Answer: C


Question 8:

A media advertising company handles a large number of real-time messages sourced from over 200 websites in real time. Processing latency must be kept low. Based on calculations, a 60-shard Amazon Kinesis stream is more than sufficient to handle the maximum data throughput, even with traffic spikes. The company also uses an Amazon Kinesis Client Library (KCL) application running on Amazon Elastic Compute Cloud (EC2) managed by an Auto Scaling group. Amazon CloudWatch indicates an average of 25% CPU and a modest level of network traffic across all running servers.

The company reports a 150% to 200% increase in latency of processing messages from Amazon Kinesis during peak times. There are NO reports of delay from the sites publishing to Amazon Kinesis.

What is the appropriate solution to address the latency?

A. Increase the number of shards in the Amazon Kinesis stream to 80 for greater concurrency.

B. Increase the size of the Amazon EC2 instances to increase network throughput.

C. Increase the minimum number of instances in the Auto Scaling group.

D. Increase Amazon DynamoDB throughput on the checkpoint table.

Correct Answer: D


Question 9:

A Redshift data warehouse has different user teams that need to query the same table with very different query types. These user teams are experiencing poor performance. Which action improves performance for the user teams in this situation?

A. Create custom table views.

B. Add interleaved sort keys per team.

C. Maintain team-specific copies of the table.

D. Add support for workload management queue hopping.

Correct Answer: D

Reference: https://docs.aws.amazon.com/redshift/latest/dg/cm-c-implementing-workload-management.html


Question 10:

A data engineer wants to use an Amazon Elastic Map Reduce for an application. The data engineer needs to make sure it complies with regulatory requirements. The auditor must be able to confirm at any point which servers are running and

which network access controls are deployed.

Which action should the data engineer take to meet this requirement?

A. Provide the auditor IAM accounts with the SecurityAudit policy attached to their group.

B. Provide the auditor with SSH keys for access to the Amazon EMR cluster.

C. Provide the auditor with CloudFormation templates.

D. Provide the auditor with access to AWS DirectConnect to use their existing tools.

Correct Answer: C


Question 11:

A social media customer has data from different data sources including RDS running MySQL, Redshift, and Hive on EMR. To support better analysis, the customer needs to be able to analyze data from different data sources and to combine the results.

What is the most cost-effective solution to meet these requirements?

A. Load all data from a different database/warehouse to S3. Use Redshift COPY command to copy data to Redshift for analysis.

B. Install Presto on the EMR cluster where Hive sits. Configure MySQL and PostgreSQL connector to select from different data sources in a single query.

C. Spin up an Elasticsearch cluster. Load data from all three data sources and use Kibana to analyze.

D. Write a program running on a separate EC2 instance to run queries to three different systems. Aggregate the results after getting the responses from all three systems.

Correct Answer: B


Question 12:

A game company needs to properly scale its game application, which is backed by DynamoDB. Amazon Redshift has the past two years of historical data. Game traffic varies throughout the year based on various factors such as season, movie release, and holiday season. An administrator needs to calculate how much read and write throughput should be provisioned for DynamoDB table for each week in advance.

How should the administrator accomplish this task?

A. Feed the data into Amazon Machine Learning and build a regression model.

B. Feed the data into Spark Mlib and build a random forest modest.

C. Feed the data into Apache Mahout and build a multi-classification model.

D. Feed the data into Amazon Machine Learning and build a binary classification model.

Correct Answer: B


Question 13:

A large oil and gas company needs to provide near real-time alerts when peak thresholds are exceeded in its pipeline system. The company has developed a system to capture pipeline metrics such as flow rate, pressure, and temperature using millions of sensors. The sensors deliver to AWS IoT.

What is a cost-effective way to provide near real-time alerts on the pipeline metrics?

A. Create an AWS IoT rule to generate an Amazon SNS notification.

B. Store the data points in an Amazon DynamoDB table and poll if for peak metrics data from an Amazon EC2 application.

C. Create an Amazon Machine Learning model and invoke it with AWS Lambda.

D. Use Amazon Kinesis Streams and a KCL-based application deployed on AWS Elastic Beanstalk.

Correct Answer: C


Question 14:

A data engineer is running a DWH on a 25-node Redshift cluster of a SaaS service. The data engineer needs to build a dashboard that will be used by customers. Five big customers represent 80% of usage, and there is a long tail of dozens of smaller customers. The data engineer has selected the dashboarding tool.

How should the data engineer make sure that the larger customer workloads do NOT interfere with the smaller customer workloads?

A. Apply query filters based on customer-id that can NOT be changed by the user and apply distribution keys on customer-id.

B. Place the largest customers into a single user group with a dedicated query queue and place the rest of the customers into a different query queue.

C. Push aggregations into an RDS for Aurora instance. Connect the dashboard application to Aurora rather than Redshift for faster queries.

D. Route the largest customers to a dedicated Redshift cluster. Raise the concurrency of the multi-tenant Redshift cluster to accommodate the remaining customers.

Correct Answer: D


Question 15:

An Amazon Kinesis stream needs to be encrypted. Which approach should be used to accomplish this task?

A. Perform a client-side encryption of the data before it enters the Amazon Kinesis stream on the producer.

B. Use a partition key to segment the data by MD5 hash functions, which makes it undecipherable while in transit.

C. Perform a client-side encryption of the data before it enters the Amazon Kinesis stream on the consumer.

D. Use a shard to segment the data, which has built-in functionality to make it indecipherable while in transit.

Correct Answer: B

Reference: https://docs.aws.amazon.com/firehose/latest/dev/encryption.html