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NEW QUESTION 60
A company's data analyst needs to ensure that queries executed in Amazon Athena cannot scan more than a prescribed amount of data for cost control purposes. Queries that exceed the prescribed threshold must be canceled immediately.
What should the data analyst do to achieve this?
- A. For each workgroup, set the control limit for each query to the prescribed threshold.
- B. Enforce the prescribed threshold on all Amazon S3 bucket policies
- C. Configure Athena to invoke an AWS Lambda function that terminates queries when the prescribed threshold is crossed.
- D. For each workgroup, set the workgroup-wide data usage control limit to the prescribed threshold.
Answer: A
Explanation:
https://docs.aws.amazon.com/athena/latest/ug/manage-queries-control-costs-with-workgroups.html
NEW QUESTION 61
A university intends to use Amazon Kinesis Data Firehose to collect JSON-formatted batches of water quality readings in Amazon S3. The readings are from 50 sensors scattered across a local lake. Students will query the stored data using Amazon Athena to observe changes in a captured metric over time, such as water temperature or acidity. Interest has grown in the study, prompting the university to reconsider how data will be stored.
Which data format and partitioning choices will MOST significantly reduce costs? (Choose two.)
- A. Store the data in Apache Avro format using Snappy compression.
- B. Partition the data by sensor, year, month, and day.
- C. Store the data in Apache Parquet format using Snappy compression.
- D. Partition the data by year, month, and day.
- E. Store the data in Apache ORC format using no compression.
Answer: C,E
NEW QUESTION 62
An online retailer needs to deploy a product sales reporting solution. The source data is exported from an external online transaction processing (OLTP) system for reporting. Roll-up data is calculated each day for the previous day's activities. The reporting system has the following requirements:
Have the daily roll-up data readily available for 1 year.
After 1 year, archive the daily roll-up data for occasional but immediate access.
The source data exports stored in the reporting system must be retained for 5 years. Query access will be needed only for re-evaluation, which may occur within the first 90 days.
Which combination of actions will meet these requirements while keeping storage costs to a minimum? (Choose two.)
- A. Store the daily roll-up data initially in the Amazon S3 Standard storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier Deep Archive 1 year after data creation.
- B. Store the daily roll-up data initially in the Amazon S3 Standard storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) 1 year after data creation.
- C. Store the source data initially in the Amazon S3 Glacier storage class. Apply a lifecycle configuration that changes the storage class from Amazon S3 Glacier to Amazon S3 Glacier Deep Archive 90 days after creation, and then deletes the data 5 years after creation.
- D. Store the source data initially in the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier Deep Archive 90 days after creation, and then deletes the data 5 years after creation.
- E. Store the daily roll-up data initially in the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier 1 year after data creation.
Answer: B,D
NEW QUESTION 63
A smart home automation company must efficiently ingest and process messages from various connected devices and sensors. The majority of these messages are comprised of a large number of small files. These messages are ingested using Amazon Kinesis Data Streams and sent to Amazon S3 using a Kinesis data stream consumer application. The Amazon S3 message data is then passed through a processing pipeline built on Amazon EMR running scheduled PySpark jobs.
The data platform team manages data processing and is concerned about the efficiency and cost of downstream data processing. They want to continue to use PySpark.
Which solution improves the efficiency of the data processing jobs and is well architected?
- A. Launch an Amazon Redshift cluster. Copy the collected data from Amazon S3 to Amazon Redshift and move the data processing jobs from Amazon EMR to Amazon Redshift.
- B. Set up AWS Glue Python jobs to merge the small data files in Amazon S3 into larger files and transform them to Apache Parquet format. Migrate the downstream PySpark jobs from Amazon EMR to AWS Glue.
- C. Set up an AWS Lambda function with a Python runtime environment. Process individual Kinesis data stream messages from the connected devices and sensors using Lambda.
- D. Send the sensor and devices data directly to a Kinesis Data Firehose delivery stream to send the data to Amazon S3 with Apache Parquet record format conversion enabled. Use Amazon EMR running PySpark to process the data in Amazon S3.
Answer: D
NEW QUESTION 64
A media company has been performing analytics on log data generated by its applications. There has been a recent increase in the number of concurrent analytics jobs running, and the overall performance of existing jobs is decreasing as the number of new jobs is increasing. The partitioned data is stored in Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA) and the analytic processing is performed on Amazon EMR clusters using the EMR File System (EMRFS) with consistent view enabled. A data analyst has determined that it is taking longer for the EMR task nodes to list objects in Amazon S3.
Which action would MOST likely increase the performance of accessing log data in Amazon S3?
- A. Redeploy the EMR clusters that are running slowly to a different Availability Zone.
- B. Use a hash function to create a random string and add that to the beginning of the object prefixes when storing the log data in Amazon S3.
- C. Use a lifecycle policy to change the S3 storage class to S3 Standard for the log data.
- D. Increase the read capacity units (RCUs) for the shared Amazon DynamoDB table.
Answer: A
NEW QUESTION 65
A company launched a service that produces millions of messages every day and uses Amazon Kinesis Data Streams as the streaming service.
The company uses the Kinesis SDK to write data to Kinesis Data Streams. A few months after launch, a data analyst found that write performance is significantly reduced. The data analyst investigated the metrics and determined that Kinesis is throttling the write requests. The data analyst wants to address this issue without significant changes to the architecture.
Which actions should the data analyst take to resolve this issue? (Choose two.)
- A. Replace the Kinesis API-based data ingestion mechanism with Kinesis Agent.
- B. Choose partition keys in a way that results in a uniform record distribution across shards.
- C. Customize the application code to include retry logic to improve performance.
- D. Increase the number of shards in the stream using the UpdateShardCount API.
- E. Increase the Kinesis Data Streams retention period to reduce throttling.
Answer: B,D
Explanation:
https://aws.amazon.com/blogs/big-data/under-the-hood-scaling-your-kinesis-data-streams/
NEW QUESTION 66
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. Configure multiple Lambda functions to process the stream.
- C. Migrate the Lambda consumers from standard data stream iterators to an HTTP/2 stream consumer.
- D. Increase the number of shards in the stream.
Answer: D
NEW QUESTION 67
An online retail company with millions of users around the globe wants to improve its ecommerce analytics capabilities. Currently, clickstream data is uploaded directly to Amazon S3 as compressed files. Several times each day, an application running on Amazon EC2 processes the data and makes search options and reports available for visualization by editors and marketers. The company wants to make website clicks and aggregated data available to editors and marketers in minutes to enable them to connect with users more effectively.
Which options will help meet these requirements in the MOST efficient way? (Choose two.)
- A. Upload clickstream records to Amazon S3 as compressed files. Then use AWS Lambda to send data to Amazon Elasticsearch Service from Amazon S3.
- B. Use Amazon Elasticsearch Service deployed on Amazon EC2 to aggregate, filter, and process the data.
Refresh content performance dashboards in near-real time. - C. Use Kibana to aggregate, filter, and visualize the data stored in Amazon Elasticsearch Service. Refresh content performance dashboards in near-real time.
- D. Use Amazon Kinesis Data Firehose to upload compressed and batched clickstream records to Amazon Elasticsearch Service.
- E. Upload clickstream records from Amazon S3 to Amazon Kinesis Data Streams and use a Kinesis Data Streams consumer to send records to Amazon Elasticsearch Service.
Answer: B,E
NEW QUESTION 68
A large financial company is running its ETL process. Part of this process is to move data from Amazon S3 into an Amazon Redshift cluster. The company wants to use the most cost-efficient method to load the dataset into Amazon Redshift.
Which combination of steps would meet these requirements? (Choose two.)
- A. Use the UNLOAD command to upload data into Amazon Redshift.
- B. Use Amazon Redshift Spectrum to query files from Amazon S3.
- C. Use S3DistCp to load files into Amazon Redshift.
- D. Use temporary staging tables during the loading process.
- E. Use the COPY command with the manifest file to load data into Amazon Redshift.
Answer: B,D
NEW QUESTION 69
An Amazon Redshift database contains sensitive user data. Logging is necessary to meet compliance requirements. The logs must contain database authentication attempts, connections, and disconnections. The logs must also contain each query run against the database and record which database user ran each query.
Which steps will create the required logs?
- A. Enable audit logging for Amazon Redshift using the AWS Management Console or the AWS CLI.
- B. Allow access to the Amazon Redshift database using AWS IAM only. Log access using AWS CloudTrail.
- C. Enable and download audit reports from AWS Artifact.
- D. Enable Amazon Redshift Enhanced VPC Routing. Enable VPC Flow Logs to monitor traffic.
Answer: A
NEW QUESTION 70
A company has a business unit uploading .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to do discovery, and create tables and schemas. An AWS Glue job writes processed data from the created tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creating the Amazon Redshift table appropriately. When the AWS Glue job is rerun for any reason in a day, duplicate records are introduced into the Amazon Redshift table.
Which solution will update the Redshift table without duplicates when jobs are rerun?
- A. Use Apache Spark's DataFrame dropDuplicates() API to eliminate duplicates and then write the data to Amazon Redshift.
- B. Modify the AWS Glue job to copy the rows into a staging table. Add SQL commands to replace the existing rows in the main table as postactions in the DynamicFrameWriter class.
- C. Load the previously inserted data into a MySQL database in the AWS Glue job. Perform an upsert operation in MySQL, and copy the results to the Amazon Redshift table.
- D. Use the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.
Answer: B
Explanation:
https://aws.amazon.com/premiumsupport/knowledge-center/sql-commands-redshift-glue-job/ See the section Merge an Amazon Redshift table in AWS Glue (upsert)
NEW QUESTION 71
A company that monitors weather conditions from remote construction sites is setting up a solution to collect temperature data from the following two weather stations.
Station A, which has 10 sensors
Station B, which has five sensors
These weather stations were placed by onsite subject-matter experts.
Each sensor has a unique ID. The data collected from each sensor will be collected using Amazon Kinesis Data Streams.
Based on the total incoming and outgoing data throughput, a single Amazon Kinesis data stream with two shards is created. Two partition keys are created based on the station names. During testing, there is a bottleneck on data coming from Station A, but not from Station B.
Upon review, it is confirmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput.
How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?
- A. Create a separate Kinesis data stream for Station A with two shards, and stream Station A sensor data to the new stream.
- B. Modify the partition key to use the sensor ID instead of the station name.
- C. Reduce the number of sensors in Station A from 10 to 5 sensors.
- D. Increase the number of shards in Kinesis Data Streams to increase the level of parallelism.
Answer: B
Explanation:
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-resharding.html
"Splitting increases the number of shards in your stream and therefore increases the data capacity of the stream. Because you are charged on a per-shard basis, splitting increases the cost of your stream"
NEW QUESTION 72
Once a month, a company receives a 100 MB .csv file compressed with gzip. The file contains 50,000 property listing records and is stored in Amazon S3 Glacier. The company needs its data analyst to query a subset of the data for a specific vendor.
What is the most cost-effective solution?
- A. Load the data to Amazon S3 and query it with Amazon Redshift Spectrum.
- B. Query the data from Amazon S3 Glacier directly with Amazon Glacier Select.
- C. Load the data into Amazon S3 and query it with Amazon S3 Select.
- D. Load the data to Amazon S3 and query it with Amazon Athena.
Answer: D
NEW QUESTION 73
A healthcare company uses AWS data and analytics tools to collect, ingest, and store electronic health record (EHR) data about its patients. The raw EHR data is stored in Amazon S3 in JSON format partitioned by hour, day, and year and is updated every hour. The company wants to maintain the data catalog and metadata in an AWS Glue Data Catalog to be able to access the data using Amazon Athena or Amazon Redshift Spectrum for analytics.
When defining tables in the Data Catalog, the company has the following requirements:
Choose the catalog table name and do not rely on the catalog table naming algorithm. Keep the table updated with new partitions loaded in the respective S3 bucket prefixes.
Which solution meets these requirements with minimal effort?
- A. Create an Apache Hive catalog in Amazon EMR with the table schema definition in Amazon S3, and update the table partition with a scheduled job. Migrate the Hive catalog to the Data Catalog.
- B. Run an AWS Glue crawler that connects to one or more data stores, determines the data structures, and writes tables in the Data Catalog.
- C. Use the AWS Glue console to manually create a table in the Data Catalog and schedule an AWS Lambda function to update the table partitions hourly.
- D. Use the AWS Glue API CreateTable operation to create a table in the Data Catalog. Create an AWS Glue crawler and specify the table as the source.
Answer: D
Explanation:
Updating Manually Created Data Catalog Tables Using Crawlers: To do this, when you define a crawler, instead of specifying one or more data stores as the source of a crawl, you specify one or more existing Data Catalog tables. The crawler then crawls the data stores specified by the catalog tables. In this case, no new tables are created; instead, your manually created tables are updated.
NEW QUESTION 74
A retail company is building its data warehouse solution using Amazon Redshift. As a part of that effort, the company is loading hundreds of files into the fact table created in its Amazon Redshift cluster. The company wants the solution to achieve the highest throughput and optimally use cluster resources when loading data into the company's fact table.
How should the company meet these requirements?
- A. Use LOAD commands equal to the number of Amazon Redshift cluster nodes and load the data in parallel into each node.
- B. Use multiple COPY commands to load the data into the Amazon Redshift cluster.
- C. Use S3DistCp to load multiple files into the Hadoop Distributed File System (HDFS) and use an HDFS connector to ingest the data into the Amazon Redshift cluster.
- D. Use a single COPY command to load the data into the Amazon Redshift cluster.
Answer: C
NEW QUESTION 75
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