Question 1

A software company wants to use instrumentation data to detect and resolve errors to improve application recovery time. The company requires API usage anomalies, like error rate and response time spikes, to be detected in near-real time (NRT) The company also requires that data analysts have access to dashboards for log analysis in NRT

Which solution meets these requirements'?

AUse Amazon Kinesis Data Firehose as the data transport layer for logging data Use Amazon Kinesis Data Analytics to uncover the NRT API usage anomalies Use Kinesis Data Firehose to deliver log data to Amazon OpenSearch Service (Amazon Elasticsearch Service) for search, log analytics, and application monitoring Use OpenSearch Dashboards (Kibana) in Amazon OpenSearch Service (Amazon Elasticsearch Service) for the dashboards.

BUse Amazon Kinesis Data Analytics as the data transport layer for logging data. Use Amazon Kinesis Data Streams to uncover NRT monitoring metrics. Use Amazon Kinesis Data Firehose to deliver log data to Amazon OpenSearch Service (Amazon Elasticsearch Service) for search, log analytics, and application monitoring Use Amazon QuickSight for the dashboards

CUse Amazon Kinesis Data Analytics as the data transport layer for logging data and to uncover NRT monitoring metrics Use Amazon Kinesis Data Firehose to deliver log data to Amazon OpenSearch Service (Amazon Elasticsearch Service) for search, log analytics, and application monitoring Use OpenSearch Dashboards (Kibana) in Amazon OpenSearch Service (Amazon Elasticsearch Service) for the dashboards

DUse Amazon Kinesis Data Firehose as the data transport layer for logging data Use Amazon Kinesis Data Analytics to uncover NRT monitoring metrics Use Amazon Kinesis Data Streams to deliver log data to Amazon OpenSearch Service (Amazon Elasticsearch Service) for search, log analytics, and application monitoring Use Amazon QuickSight for the dashboards.

Answer : C

Question 2

A company uses Amazon kinesis Data Streams to ingest and process customer behavior information from application users each day. A data analytics specialist notices that its data stream is throttling. The specialist has turned on enhanced monitoring for the Kinesis data stream and has verified that the data stream did not exceed the data limits. The specialist discovers that there are hot shards

Which solution will resolve this issue?

AUse a random partition key to ingest the records.

BIncrease the number of shards Split the size of the log records.

CLimit the number of records that are sent each second by the producer to match the capacity of the stream.

DDecrease the size of the records that are sent from the producer to match the capacity of the stream.

Answer : A

Question 3

A data analyst runs a large number of data manipulation language (DML) queries by using Amazon Athena with the JDBC driver. Recently, a query failed after It ran for 30 minutes. The query returned the following message

Java.sql.SGLException: Query timeout

The data analyst does not immediately need the query results However, the data analyst needs a long-term solution for this problem

Which solution will meet these requirements?

ASplit the query into smaller queries to search smaller subsets of data.

BIn the settings for Athena, adjust the DML query timeout limit

CIn the Service Quotas console, request an increase for the DML query timeout

DSave the tables as compressed .csv files

Answer : A

Question 4

A gaming company is collecting cllckstream data into multiple Amazon Kinesis data streams. The company uses Amazon Kinesis Data Firehose delivery streams to store the data in JSON format in Amazon S3 Data scientists use Amazon Athena to query the most recent data and derive business insights. The company wants to reduce its Athena costs without having to recreate the data pipeline. The company prefers a solution that will require less management effort

Which set of actions can the data scientists take immediately to reduce costs?

AChange the Kinesis Data Firehose output format to Apache Parquet Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size For the existing data, run an AWS Glue ETL job to combine and convert small JSON files to large Parquet files and add the YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table.

BCreate an Apache Spark Job that combines and converts JSON files to Apache Parquet files Launch an Amazon EMR ephemeral cluster daily to run the Spark job to create new Parquet files in a different S3 location Use ALTER TABLE SET LOCATION to reflect the new S3 location on the existing Athena table.

CCreate a Kinesis data stream as a delivery target for Kinesis Data Firehose Run Apache Flink on Amazon Kinesis Data Analytics on the stream to read the streaming data, aggregate ikand save it to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table

DIntegrate an AWS Lambda function with Kinesis Data Firehose to convert source records to Apache Parquet and write them to Amazon S3 In parallel, run an AWS Glue ETL job to combine and convert existing JSON files to large Parquet files Create a custom S3 object YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table.

Answer : D

Question 5

A company is building an analytical solution that includes Amazon S3 as data lake storage and Amazon Redshift for data warehousing. The company wants to use Amazon Redshift Spectrum to query the data that is stored in Amazon S3.

Which steps should the company take to improve performance when the company uses Amazon Redshift Spectrum to query the S3 data files? (Select THREE )

Use gzip compression with individual file sizes of 1-5 GB

AUse a columnar storage file format

BPartition the data based on the most common query predicates

CSplit the data into KB-sized files.

DKeep all files about the same size.

EUse file formats that are not splittable

Answer : B, C, D

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