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NEW QUESTION 39
A transport company wants to track vehicular movements by capturing geolocation records. The records are 10 B in size and up to 10,000 records are captured each second. Data transmission delays of a few minutes are acceptable, considering unreliable network conditions. The transport company decided to use Amazon Kinesis Data Streams to ingest the dat a. The company is looking for a reliable mechanism to send data to Kinesis Data Streams while maximizing the throughput efficiency of the Kinesis shards.
Which solution will meet the company's requirements?
- A. Kinesis Data Firehose
- B. Kinesis Agent
- C. Kinesis SDK
- D. Kinesis Producer Library (KPL)
Answer: D
NEW QUESTION 40
A marketing company has data in Salesforce, MySQL, and Amazon S3. The company wants to use data from these three locations and create mobile dashboards for its users. The company is unsure how it should create the dashboards and needs a solution with the least possible customization and coding.
Which solution meets these requirements?
- A. Use Amazon Athena federated queries to join the data sources. Use Amazon QuickSight to generate the mobile dashboards.
- B. Use AWS Lake Formation to migrate the data sources into Amazon S3. Use Amazon QuickSight to generate the mobile dashboards.
- C. Use Amazon QuickSight to connect to the data sources and generate the mobile dashboards.
- D. Use Amazon Redshift federated queries to join the data sources. Use Amazon QuickSight to generate the mobile dashboards.
Answer: D
NEW QUESTION 41
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. Create an Amazon EMR cluster and use Amazon S3 as the data source. Create an Apache Spark job to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.
- B. Create an AWS Lambda function to convert the logs into .csv format. Then add the function to the Kinesis Data Firehose transformation configuration. Use Amazon Redshift to perform ad-hoc analyses of the logs using SQL queries and use Amazon QuickSight to develop data visualizations.
- C. Create a second Kinesis Data Firehose delivery stream to deliver the log files to Amazon Elasticsearch Service (Amazon ES). Use Amazon ES to perform text-based searches of the logs for ad-hoc analyses and use Kibana for data visualizations.
- D. Use an AWS Glue crawler to create and update a table in the Glue data catalog from the logs. Use Athena to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.
Answer: D
Explanation:
https://aws.amazon.com/blogs/big-data/analyzing-aws-waf-logs-with-amazon-es-amazon-athena-and-amazon-quicksight/
NEW QUESTION 42
A technology company is creating a dashboard that will visualize and analyze time-sensitive data. The data will come in through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support near-real-time data.
Which visualization solution will meet these requirements?
- A. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Read data into an Amazon SageMaker Jupyter notebook and carry out the desired analyses and visualizations.
- B. Select Amazon Elasticsearch Service (Amazon ES) as the endpoint for Kinesis Data Firehose. Set up a Kibana dashboard using the data in Amazon ES with the desired analyses and visualizations.
- C. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Use AWS Glue to catalog the data and Amazon Athena to query it. Connect Amazon QuickSight with SPICE to Athena to create the desired analyses and visualizations.
- D. Select Amazon Redshift as the endpoint for Kinesis Data Firehose. Connect Amazon QuickSight with SPICE to Amazon Redshift to create the desired analyses and visualizations.
Answer: B
NEW QUESTION 43
A company is streaming its high-volume billing data (100 MBps) to Amazon Kinesis Data Streams. A data analyst partitioned the data on account_id to ensure that all records belonging to an account go to the same Kinesis shard and order is maintained. While building a custom consumer using the Kinesis Java SDK, the data analyst notices that, sometimes, the messages arrive out of order for account_id. Upon further investigation, the data analyst discovers the messages that are out of order seem to be arriving from different shards for the same account_id and are seen when a stream resize runs.
What is an explanation for this behavior and what is the solution?
- A. The records are not being received by Kinesis Data Streams in order. The producer should use the PutRecords API call instead of the PutRecord API call with the SequenceNumberForOrdering parameter.
- B. The hash key generation process for the records is not working correctly. The data analyst should generate an explicit hash key on the producer side so the records are directed to the appropriate shard accurately.
- C. The consumer is not processing the parent shard completely before processing the child shards after a stream resize. The data analyst should process the parent shard completely first before processing the child shards.
- D. There are multiple shards in a stream and order needs to be maintained in the shard. The data analyst needs to make sure there is only a single shard in the stream and no stream resize runs.
Answer: C
Explanation:
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-after-resharding.html the parent shards that remain after the reshard could still contain data that you haven't read yet that was added to the stream before the reshard. If you read data from the child shards before having read all data from the parent shards, you could read data for a particular hash key out of the order given by the data records' sequence numbers. Therefore, assuming that the order of the data is important, you should, after a reshard, always continue to read data from the parent shards until it is exhausted. Only then should you begin reading data from the child shards.
NEW QUESTION 44
A company is migrating its existing on-premises ETL jobs to Amazon EMR. The code consists of a series of jobs written in Java. The company needs to reduce overhead for the system administrators without changing the underlying code. Due to the sensitivity of the data, compliance requires that the company use root device volume encryption on all nodes in the cluster. Corporate standards require that environments be provisioned though AWS CloudFormation when possible.
Which solution satisfies these requirements?
- A. Install open-source Hadoop on Amazon EC2 instances with encrypted root device volumes. Configure the cluster in the CloudFormation template.
- B. Use a CloudFormation template to launch an EMR cluster. In the configuration section of the cluster, define a bootstrap action to encrypt the root device volume of every node.
- C. Use a CloudFormation template to launch an EMR cluster. In the configuration section of the cluster, define a bootstrap action to enable TLS.
- D. Create a custom AMI with encrypted root device volumes. Configure Amazon EMR to use the custom AMI using the CustomAmild property in the CloudFormation template.
Answer: D
NEW QUESTION 45
A large company has a central data lake to run analytics across different departments. Each department uses a separate AWS account and stores its data in an Amazon S3 bucket in that account. Each AWS account uses the AWS Glue Data Catalog as its data catalog. There are different data lake access requirements based on roles. Associate analysts should only have read access to their departmental data. Senior data analysts can have access in multiple departments including theirs, but for a subset of columns only.
Which solution achieves these required access patterns to minimize costs and administrative tasks?
- A. Set up an individual AWS account for the central data lake and configure a central S3 bucket. Use an AWS Lake Formation blueprint to move the data from the various buckets into the central S3 bucket.
On each individual bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls for both associate and senior analysts to view specific tables and columns. - B. Consolidate all AWS accounts into one account. Create different S3 buckets for each department and move all the data from every account to the central data lake account. Migrate the individual data catalogs into a central data catalog and apply fine-grained permissions to give to each user the required access to tables and databases in AWS Glue and Amazon S3.
- C. Set up an individual AWS account for the central data lake. Use AWS Lake Formation to catalog the cross- account locations. On each individual S3 bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls to allow senior analysts to view specific tables and columns.
- D. Keep the account structure and the individual AWS Glue catalogs on each account. Add a central data lake account and use AWS Glue to catalog data from various accounts. Configure cross-account access for AWS Glue crawlers to scan the data in each departmental S3 bucket to identify the schema and populate the catalog. Add the senior data analysts into the central account and apply highly detailed access controls in the Data Catalog and Amazon S3.
Answer: D
NEW QUESTION 46
An insurance company has raw data in JSON format that is sent without a predefined schedule through an Amazon Kinesis Data Firehose delivery stream to an Amazon S3 bucket. An AWS Glue crawler is scheduled to run every 8 hours to update the schema in the data catalog of the tables stored in the S3 bucket. Data analysts analyze the data using Apache Spark SQL on Amazon EMR set up with AWS Glue Data Catalog as the metastore. Data analysts say that, occasionally, the data they receive is stale. A data engineer needs to provide access to the most up-to-date data.
Which solution meets these requirements?
- A. Create an external schema based on the AWS Glue Data Catalog on the existing Amazon Redshift cluster to query new data in Amazon S3 with Amazon Redshift Spectrum.
- B. Using the AWS CLI, modify the execution schedule of the AWS Glue crawler from 8 hours to 1 minute.
- C. Use Amazon CloudWatch Events with the rate (1 hour) expression to execute the AWS Glue crawler every hour.
- D. Run the AWS Glue crawler from an AWS Lambda function triggered by an S3:ObjectCreated:* event notification on the S3 bucket.
Answer: D
Explanation:
https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html "you can use a wildcard (for example, s3:ObjectCreated:*) to request notification when an object is created regardless of the API used" "AWS Lambda can run custom code in response to Amazon S3 bucket events. You upload your custom code to AWS Lambda and create what is called a Lambda function. When Amazon S3 detects an event of a specific type (for example, an object created event), it can publish the event to AWS Lambda and invoke your function in Lambda. In response, AWS Lambda runs your function."
NEW QUESTION 47
A company that produces network devices has millions of users. Data is collected from the devices on an hourly basis and stored in an Amazon S3 data lake.
The company runs analyses on the last 24 hours of data flow logs for abnormality detection and to troubleshoot and resolve user issues. The company also analyzes historical logs dating back 2 years to discover patterns and look for improvement opportunities.
The data flow logs contain many metrics, such as date, timestamp, source IP, and target IP. There are about 10 billion events every day.
How should this data be stored for optimal performance?
- A. In compressed .csv partitioned by date and sorted by source IP
- B. In compressed nested JSON partitioned by source IP and sorted by date
- C. In Apache Parquet partitioned by source IP and sorted by date
- D. In Apache ORC partitioned by date and sorted by source IP
Answer: B
NEW QUESTION 48
A banking company wants to collect large volumes of transactional data using Amazon Kinesis Data Streams for real-time analytics. The company uses PutRecord to send data to Amazon Kinesis, and has observed network outages during certain times of the day. The company wants to obtain exactly once semantics for the entire processing pipeline.
What should the company do to obtain these characteristics?
- A. Rely on the exactly one processing semantics of Apache Flink and Apache Spark Streaming included in Amazon EMR.
- B. Rely on the processing semantics of Amazon Kinesis Data Analytics to avoid duplicate processing of events.
- C. Design the data producer so events are not ingested into Kinesis Data Streams multiple times.
- D. Design the application so it can remove duplicates during processing be embedding a unique ID in each record.
Answer: D
NEW QUESTION 49
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 finance team should be able to load this data into Amazon Redshift and aggregate the values within the sensitive fields. 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 efficiently and securely?
- A. Create an AWS Lambda function to process the DynamoDB stream. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command with the IAM role that has access to the KMS key to load the data from S3 to the finance table.
- B. Create an AWS Lambda function to process the DynamoDB stream. Decrypt the sensitive data using the same KMS key. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command to load the data from Amazon S3 to the finance table.
- C. Create an Amazon EMR cluster. Create Apache Hive tables that reference the data stored in DynamoDB. Insert the output to the restricted Amazon S3 bucket for the finance team. Use the COPY command with the IAM role that has access to the KMS key to load the data from Amazon S3 to the finance table in Amazon Redshift.
- D. Create an Amazon EMR cluster with an EMR_EC2_DefaultRole role that has access to the KMS key. Create Apache Hive tables that reference the data stored in DynamoDB and the finance table in Amazon Redshift. In Hive, select the data from DynamoDB and then insert the output to the finance table in Amazon Redshift.
Answer: A
NEW QUESTION 50
A company has an encrypted Amazon Redshift cluster. The company recently enabled Amazon Redshift audit logs and needs to ensure that the audit logs are also encrypted at rest. The logs are retained for 1 year. The auditor queries the logs once a month.
What is the MOST cost-effective way to meet these requirements?
- A. Enable default encryption on the Amazon S3 bucket where the logs are stored by using AES-256 encryption. Use Amazon Redshift Spectrum to query the data as required.
- B. Disable encryption on the Amazon Redshift cluster, configure audit logging, and encrypt the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query the data as required.
- C. Encrypt the Amazon S3 bucket where the logs are stored by using AWS Key Management Service (AWS KMS). Copy the data into the Amazon Redshift cluster from Amazon S3 on a daily basis. Query the data as required.
- D. Enable default encryption on the Amazon S3 bucket where the logs are stored by using AES-256 encryption. Copy the data into the Amazon Redshift cluster from Amazon S3 on a daily basis. Query the data as required.
Answer: C
NEW QUESTION 51
A company is building a service to monitor fleets of vehicles. The company collects IoT data from a device in each vehicle and loads the data into Amazon Redshift in near-real time. Fleet owners upload .csv files containing vehicle reference data into Amazon S3 at different times throughout the day. A nightly process loads the vehicle reference data from Amazon S3 into Amazon Redshift. The company joins the IoT data from the device and the vehicle reference data to power reporting and dashboards. Fleet owners are frustrated by waiting a day for the dashboards to update.
Which solution would provide the SHORTEST delay between uploading reference data to Amazon S3 and the change showing up in the owners' dashboards?
- A. Send the reference data to an Amazon Kinesis Data Firehose delivery stream. Configure Kinesis with a buffer interval of 60 seconds and to directly load the data into Amazon Redshift.
- B. Send reference data to Amazon Kinesis Data Streams. Configure the Kinesis data stream to directly load the reference data into Amazon Redshift in real time.
- C. Use S3 event notifications to trigger an AWS Lambda function to copy the vehicle reference data into Amazon Redshift immediately when the reference data is uploaded to Amazon S3.
- D. Create and schedule an AWS Glue Spark job to run every 5 minutes. The job inserts reference data into Amazon Redshift.
Answer: C
NEW QUESTION 52
A large company receives files from external parties in Amazon EC2 throughout the day. At the end of the day, the files are combined into a single file, compressed into a gzip file, and uploaded to Amazon S3. The total size of all the files is close to 100 GB daily. Once the files are uploaded to Amazon S3, an AWS Batch program executes a COPY command to load the files into an Amazon Redshift cluster.
Which program modification will accelerate the COPY process?
- A. Split the number of files so they are equal to a multiple of the number of slices in the Amazon Redshift cluster. Gzip and upload the files to Amazon S3. Run the COPY command on the files.
- B. Upload the individual files to Amazon S3 and run the COPY command as soon as the files become available.
- C. Apply sharding by breaking up the files so the distkey columns with the same values go to the same file. Gzip and upload the sharded files to Amazon S3. Run the COPY command on the files.
- D. Split the number of files so they are equal to a multiple of the number of compute nodes in the Amazon Redshift cluster. Gzip and upload the files to Amazon S3. Run the COPY command on the files.
Answer: A
NEW QUESTION 53
A company needs to store objects containing log data in JSON format. The objects are generated by eight applications running in AWS. Six of the applications generate a total of 500 KiB of data per second, and two of the applications can generate up to 2 MiB of data per second.
A data engineer wants to implement a scalable solution to capture and store usage data in an Amazon S3 bucket. The usage data objects need to be reformatted, converted to .csv format, and then compressed before they are stored in Amazon S3. The company requires the solution to include the least custom code possible and has authorized the data engineer to request a service quota increase if needed.
Which solution meets these requirements?
- A. Configure an Amazon Kinesis Data Firehose delivery stream for each application. Write AWS Lambda functions to read log data objects from the stream for each application. Have the function perform reformatting and .csv conversion. Enable compression on all the delivery streams.
- B. Configure an Amazon Kinesis data stream for each application. Write an AWS Lambda function to read usage data objects from the stream for each application. Have the function perform .csv conversion, reformatting, and compression of the data. Have the function store the output in Amazon S3.
- C. Configure an Amazon Kinesis data stream with one shard per application. Write an AWS Lambda function to read usage data objects from the shards. Have the function perform .csv conversion, reformatting, and compression of the data. Have the function store the output in Amazon S3.
- D. Store usage data objects in an Amazon DynamoDB table. Configure a DynamoDB stream to copy the objects to an S3 bucket. Configure an AWS Lambda function to be triggered when objects are written to the S3 bucket. Have the function convert the objects into .csv format.
Answer: A
NEW QUESTION 54
A financial company uses Apache Hive on Amazon EMR for ad-hoc queries. Users are complaining of sluggish performance.
A data analyst notes the following:
* Approximately 90% of queries are submitted 1 hour after the market opens.
* Hadoop Distributed File System (HDFS) utilization never exceeds 10%.
Which solution would help address the performance issues?
- A. Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric.
Create an automatic scaling policy to scale in the instance groups based on the CloudWatch YARNMemoryAvailablePercentage metric. - B. Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance groups based on the CloudWatch CapacityRemainingGB metric.
- C. Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric.
Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch YARNMemoryAvailablePercentage metric. - D. Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB metric.
Answer: B
NEW QUESTION 55
An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well-functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.
Which solution meets these requirements?
- A. Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.
- B. Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
- C. Export the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.
- D. Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function.
Perform the join with AWS Glue ETL scripts.
Answer: B
NEW QUESTION 56
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?
- A. 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.
- B. 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.
- C. 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.
- D. 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.
Answer: D
NEW QUESTION 57
A retail company leverages Amazon Athena for ad-hoc queries against an AWS Glue Data Catalog. The data analytics team manages the data catalog and data access for the company. The data analytics team wants to separate queries and manage the cost of running those queries by different workloads and teams. Ideally, the data analysts want to group the queries run by different users within a team, store the query results in individual Amazon S3 buckets specific to each team, and enforce cost constraints on the queries run against the Data Catalog.
Which solution meets these requirements?
- A. Create Athena workgroups for each team within the company. Set up IAM workgroup policies that control user access and actions on the workgroup resources.
- B. Create Athena resource groups for each team within the company and assign users to these groups. Add S3 bucket names and other query configurations to the properties list for the resource groups.
- C. Create Athena query groups for each team within the company and assign users to the groups.
- D. Create IAM groups and resource tags for each team within the company. Set up IAM policies that control user access and actions on the Data Catalog resources.
Answer: A
Explanation:
https://aws.amazon.com/about-aws/whats-new/2019/02/athena_workgroups/
NEW QUESTION 58
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?
- A. Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact tables.
- B. Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers and customers tables.
- C. 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.
- D. 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.
Answer: B
NEW QUESTION 59
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