NEW QUESTION 1
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 Apache ORC partitioned by date and sorted by source IP
• B. In compressed .csv partitioned by date and sorted by source IP
• C. In Apache Parquet partitioned by source IP and sorted by date
• D. In compressed nested JSON partitioned by source IP and sorted by date

Answer: A

NEW QUESTION 2
A transportation company uses IoT sensors attached to trucks to collect vehicle data for its global delivery fleet. The company currently sends the sensor data in small .csv files to Amazon S3. The files are then loaded into a 10-node Amazon Redshift cluster with two slices per node and queried using both Amazon Athena and Amazon Redshift. The company wants to optimize the files to reduce the cost of querying and also improve the speed of data loading into the Amazon Redshift cluster.
Which solution meets these requirements?

• A. Use AWS Glue to convert all the files from .csv to a single large Apache Parquet fil
• B. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• C. Use Amazon EMR to convert each .csv file to Apache Avr
• D. COPY the files into Amazon Redshift and query the file with Athena from Amazon S3.
• E. Use AWS Glue to convert the files from .csv to a single large Apache ORC fil
• F. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• G. Use AWS Glue to convert the files from .csv to Apache Parquet to create 20 Parquet file
• H. COPY the files into Amazon Redshift and query the files with Athena from Amazon S3.

Answer: D

NEW QUESTION 3
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?

• A. Use Amazon Kinesis Data Firehose to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.
• B. Use Amazon Kinesis Data Streams to stream data to Amazon Redshif
• C. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.
• D. Use Amazon Kinesis Data Firehose to stream data to Amazon Redshif
• E. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.
• F. Use Amazon Kinesis Data Streams to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.

Answer: C

NEW QUESTION 4
A company is migrating from an on-premises Apache Hadoop cluster to an Amazon EMR cluster. The cluster runs only during business hours. Due to a company requirement to avoid intraday cluster failures, the EMR cluster must be highly available. When the cluster is terminated at the end of each business day, the data must persist.
Which configurations would enable the EMR cluster to meet these requirements? (Choose three.)

• A. EMR File System (EMRFS) for storage
• B. Hadoop Distributed File System (HDFS) for storage
• C. AWS Glue Data Catalog as the metastore for Apache Hive
• D. MySQL database on the master node as the metastore for Apache Hive
• E. Multiple master nodes in a single Availability Zone
• F. Multiple master nodes in multiple Availability Zones

Answer: ACE

Explanation:
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-ha.html "Note : The cluster can reside only in one Availability Zone or subnet."

NEW QUESTION 5
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. Use an AWS Glue crawler to create and update a table in the Glue data catalog from the log
• B. Use Athena to perform ad-hoc analyses 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. Create an AWS Lambda function to convert the logs into .csv forma
• E. Then add the function to the Kinesis Data Firehose transformation configuratio
• F. Use Amazon Redshift to perform ad-hoc analyses of the logs using SQL queries and use Amazon QuickSight to develop data visualizations.
• G. Create an Amazon EMR cluster and use Amazon S3 as the data sourc
• H. Create an Apache Spark job to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.

Answer: A

Explanation:
https://aws.amazon.com/blogs/big-data/analyzing-aws-waf-logs-with-amazon-es-amazon-athena-and-amazon-qu

NEW QUESTION 6
A media analytics company consumes a stream of social media posts. The posts are sent to an Amazon Kinesis data stream partitioned on user_id. An AWS Lambda function retrieves the records and validates the content before loading the posts into an Amazon Elasticsearch cluster. The validation process needs to receive the posts for a given user in the order they were received. A data analyst has noticed that, during peak hours, the social media platform posts take more than an hour to appear in the Elasticsearch cluster.
What should the data analyst do reduce this latency?

• A. Migrate the validation process to Amazon Kinesis Data Firehose.
• B. Migrate the Lambda consumers from standard data stream iterators to an HTTP/2 stream consumer.
• C. Increase the number of shards in the stream.
• D. Configure multiple Lambda functions to process the stream.

Answer: D

NEW QUESTION 7
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. 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 basi
• B. Query the data as required.
• C. Disable encryption on the Amazon Redshift cluster, configure audit logging, and encrypt the Amazon Redshift cluste
• D. Use Amazon Redshift Spectrum to query the data as required.
• E. Enable default encryption on the Amazon S3 bucket where the logs are stored by using AES-256 encryptio
• F. Copy the data into the Amazon Redshift cluster from Amazon S3 on a daily basi
• G. Query the data as required.
• H. Enable default encryption on the Amazon S3 bucket where the logs are stored by using AES-256 encryptio
• I. Use Amazon Redshift Spectrum to query the data as required.

Answer: A

NEW QUESTION 8
A banking company is currently using an Amazon Redshift cluster with dense storage (DS) nodes to store sensitive data. An audit found that the cluster is unencrypted. Compliance requirements state that a database with sensitive data must be encrypted through a hardware security module (HSM) with automated key rotation.
Which combination of steps is required to achieve compliance? (Choose two.)

• A. Set up a trusted connection with HSM using a client and server certificate with automatic key rotation.
• B. Modify the cluster with an HSM encryption option and automatic key rotation.
• C. Create a new HSM-encrypted Amazon Redshift cluster and migrate the data to the new cluster.
• D. Enable HSM with key rotation through the AWS CLI.
• E. Enable Elliptic Curve Diffie-Hellman Ephemeral (ECDHE) encryption in the HSM.

Answer: BD

NEW QUESTION 9
A streaming application is reading data from Amazon Kinesis Data Streams and immediately writing the data to an Amazon S3 bucket every 10 seconds. The application is reading data from hundreds of shards. The batch interval cannot be changed due to a separate requirement. The data is being accessed by Amazon Athena. Users are seeing degradation in query performance as time progresses.
Which action can help improve query performance?

• A. Merge the files in Amazon S3 to form larger files.
• B. Increase the number of shards in Kinesis Data Streams.
• C. Add more memory and CPU capacity to the streaming application.
• D. Write the files to multiple S3 buckets.

Answer: A

Explanation:
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/

NEW QUESTION 10
A global pharmaceutical company receives test results for new drugs from various testing facilities worldwide. The results are sent in millions of 1 KB-sized JSON objects to an Amazon S3 bucket owned by the company. The data engineering team needs to process those files, convert them into Apache Parquet format, and load them into Amazon Redshift for data analysts to perform dashboard reporting. The engineering team uses AWS Glue to process the objects, AWS Step Functions for process orchestration, and Amazon CloudWatch for job scheduling.
More testing facilities were recently added, and the time to process files is increasing. What will MOST efficiently decrease the data processing time?

• A. Use AWS Lambda to group the small files into larger file
• B. Write the files back to Amazon S3. Process the files using AWS Glue and load them into Amazon Redshift tables.
• C. Use the AWS Glue dynamic frame file grouping option while ingesting the raw input file
• D. Process the files and load them into Amazon Redshift tables.
• E. Use the Amazon Redshift COPY command to move the files from Amazon S3 into Amazon Redshift tables directl
• F. Process the files in Amazon Redshift.
• G. Use Amazon EMR instead of AWS Glue to group the small input file
• H. Process the files in Amazon EMR and load them into Amazon Redshift tables.

Answer: A

NEW QUESTION 11
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. 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.
• B. Create and schedule an AWS Glue Spark job to run every 5 minute
• C. The job inserts reference data into Amazon Redshift.
• D. Send reference data to Amazon Kinesis Data Stream
• E. Configure the Kinesis data stream to directly load the reference data into Amazon Redshift in real time.
• F. Send the reference data to an Amazon Kinesis Data Firehose delivery strea
• G. Configure Kinesis with a buffer interval of 60 seconds and to directly load the data into Amazon Redshift.

Answer: A

NEW QUESTION 12
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. There are multiple shards in a stream and order needs to be maintained in the shar
• B. The data analyst needs to make sure there is only a single shard in the stream and no stream resize runs.
• C. The hash key generation process for the records is not working correctl
• D. The data analyst should generate an explicit hash key on the producer side so the records are directed to the appropriate shard accurately.
• E. The records are not being received by Kinesis Data Streams in orde
• F. The producer should use the PutRecords API call instead of the PutRecord API call with the SequenceNumberForOrdering parameter.
• G. The consumer is not processing the parent shard completely before processing the child shards after a stream resiz
• H. The data analyst should process the parent shard completely first before processing the child shards.

Answer: D

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 13
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?

• A. Use the exclude patterns feature of AWS Glue to identify the S3 Glacier files for the crawler to exclude.
• B. 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.
• C. Use the excludeStorageClasses property in the AWS Glue Data Catalog table to exclude files on S3 Glacier storage
• D. Use the include patterns feature of AWS Glue to identify the S3 Standard files for the crawler to include.

Answer: A

NEW QUESTION 14
A retail company has 15 stores across 6 cities in the United States. Once a month, the sales team requests a visualization in Amazon QuickSight that provides the ability to easily identify revenue trends across cities and stores. The visualization also helps identify outliers that need to be examined with further analysis.
Which visual type in QuickSight meets the sales team's requirements?

• A. Geospatial chart
• B. Line chart
• C. Heat map
• D. Tree map

Answer: A

NEW QUESTION 15
A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run.
Which approach would allow the developers to solve the issue with minimal coding effort?

• A. Have the ETL jobs read the data from Amazon S3 using a DataFrame.
• B. Enable job bookmarks on the AWS Glue jobs.
• C. Create custom logic on the ETL jobs to track the processed S3 objects.
• D. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.

Answer: B

NEW QUESTION 16
A company is hosting an enterprise reporting solution with Amazon Redshift. The application provides reporting capabilities to three main groups: an executive group to access financial reports, a data analyst group to run long-running ad-hoc queries, and a data engineering group to run stored procedures and ETL processes. The executive team requires queries to run with optimal performance. The data engineering team expects queries to take minutes.
Which Amazon Redshift feature meets the requirements for this task?

• A. Concurrency scaling
• B. Short query acceleration (SQA)
• C. Workload management (WLM)
• D. Materialized views

Answer: D

Explanation:

Materialized views:

NEW QUESTION 17
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