A marketing company has an application that stores event data in an Amazon RDS database. The company is replicating this data to Amazon Redshift for reporting and business intelligence (BI) purposes. New event data is continuously generated and ingested into the RDS database throughout the day and captured by a change data capture (CDC) replication task in AWS Database Migration Service (AWS DMS). The company requires that the new data be replicated to Amazon Redshift in near-real time.
Which solution meets these requirements?
A. Use Amazon Kinesis Data Streams as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Streams and perform an upsert into the Redshift cluster.
B. Use Amazon S3 as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.
C. Use Amazon DynamoDB as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.
D. Use Amazon Kinesis Data Firehose as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Firehose and perform an upsert into the Redshift cluster.
A company uses Amazon Redshift for its data warehouse. The company is running an ETL process that receives data in data parts from five third-party providers. The data parts contain independent records that are related to one specific job.
The company receives the data parts at various times throughout each day.
A data analytics specialist must implement a solution that loads the data into Amazon Redshift only after the company receives all five data parts.
Which solution will meet these requirements?
A. Create an Amazon S3 bucket to receive the data. Use S3 multipart upload to collect the data from the different sources and to form a single object before loading the data into Amazon Redshift.
B. Use an AWS Lambda function that is scheduled by cron to load the data into a temporary table in Amazon Redshift. Use Amazon Redshift database triggers to consolidate the final data when all five data parts are ready.
C. Create an Amazon S3 bucket to receive the data. Create an AWS Lambda function that is invoked by S3 upload events. Configure the function to validate that all five data parts are gathered before the function loads the data into Amazon Redshift.
D. Create an Amazon Kinesis Data Firehose delivery stream. Program a Python condition that will invoke a buffer flush when all five data parts are received.
An analytics team uses Amazon OpenSearch Service for an analytics API to be used by data analysts. The OpenSearch Service cluster is configured with three master nodes. The analytics team uses Amazon Managed Streaming for Apache Kafka (Amazon MSK) and a customized data pipeline to ingest and store 2 months of data in an OpenSearch Service cluster. The cluster stopped responding, which is regularly causing timeout requests. The analytics team discovers the cluster is handling too many bulk indexing requests.
Which actions would improve the performance of the OpenSearch Service cluster? (Choose two.)
A. Reduce the number of API bulk requests on the OpenSearch Service cluster and reduce the size of each bulk request.
B. Scale out the OpenSearch Service cluster by increasing the number of nodes.
C. Reduce the number of API bulk requests on the OpenSearch Service cluster, but increase the size of each bulk request
D. Increase the number of master nodes for the OpenSearch Service cluster
E. Scale down the pipeline component that is used to ingest the data into the OpenSearch Service cluster.
A company wants find ways to expand its website business by analyzing customer orders and purchasing trends. To perform data analysis, a pipeline must support daily data ingestion from the production databases into a data lake that is built on Amazon S3. The website uses Amazon DynamoDB to store product details and Amazon Aurora PostgreSQL to store order details in production.
Which solution can be used to accomplish these goals with LEAST operational overhead?
A. Leverage AWS Database Migration Service (AWS DMS) to run two continuous data replication jobs from both Aurora PostgreSQL and DynamoDB into Amazon S3. Leverage AWS Glue for data cataloging.
B. Set up an AWS Lake Formation workflow with blueprints for Aurora PostgreSQL and an AWS Glue ETLjob for DynamoDB to ingest data into Amazon S3. Leverage AWS Glue for data cataloging.
C. Create a custom Python script to ingest data from both Aurora PostgreSQL and Amazon DynamoDB into Amazon S3 using the AWS SDK for Python (Boto3) library. Deploy the script on an Amazon EC2 instance and schedule the job to run daily using a cron job. Leverage AWS Glue for data cataloging.
D. Use Amazon EMR to ingest data from both Aurora PostgreSQL and DynamoDB into Amazon S3. Leverage Apache Hive on the same EMR cluster for data cataloging.
A company uses Amazon EC2 instances to receive files from external vendors throughout each day. At the end of each day, the EC2 instances combine the files into a single file, perform gzip compression, and upload the single file to an
Amazon S3 bucket. The total size of all the files is approximately 100 GB each day.
When the files are uploaded to Amazon S3, an AWS Batch job runs a COPY command to load the files into an Amazon Redshift cluster.
Which solution will MOST accelerate the COPY process?
A. Upload the individual files to Amazon S3. Run the COPY command as soon as the files become available.
B. Split the files so that the number of files is equal to a multiple of the number of slices in the Redshift cluster. Compress and upload the files to Amazon S3. Run the COPY command on the files.
C. Split the files so that each file uses 50% of the free storage on each compute node in the Redshift cluster. Compress and upload the files to Amazon S3. Run the COPY command on the files.
D. Apply sharding by breaking up the files so that the DISTKEY columns with the same values go to the same file. Compress and upload the sharded files to Amazon S3. Run the COPY command on the files.
An IoT company is collecting data from multiple sensors and is streaming the data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Each sensor type has its own topic, and each topic has the same number of partitions.
The company is planning to turn on more sensors. However, the company wants to evaluate which sensor types are producing the most data so that the company can scale accordingly. The company needs to know which sensor types have
the largest values for the following metrics: BytesInPerSec and MessagesInPerSec.
Which level of monitoring for Amazon MSK will meet these requirements?
A. DEFAULT level
B. PER_TOPIC_PER_BROKER level
C. PER_BROKER level
D. PER_TOPIC level
A company's system operators and security engineers need to analyze activities within specific date ranges of AWS CloudTrail logs. All log files are stored in an Amazon S3 bucket, and the size of the logs is more than 5 TB. The solution must be cost-effective and maximize query performance.
Which solution meets these requirements?
A. Copy the logs to a new S3 bucket with a prefix structure of
B. Create a table on Amazon Athena. Manually add metadata partitions by using the ALTER TABLE ADD PARTITION statement, and use multiple columns for the partition key. Use Athena to query the table and partitions.
C. Launch an Amazon EMR cluster and use Amazon S3 as a data store for Apache HBase. Load the logs from the S3 bucket to an HBase table on Amazon EMR. Use Amazon Athena to query the table and partitions.
D. Create an AWS Glue job to copy the logs from the S3 source bucket to a new S3 bucket and create a table using Apache Parquet file format, Snappy as compression codec, and partition by date. Use Amazon Athena to query the table and partitions.
A company is attempting to use Amazon Athena to run analytics queries. The company is using the UNLOAD statement and has noticed that the result of the query is not appearing in the destination Amazon S3 bucket. The query previously ran successfully, and the company has not changed any configuration since then.
Which reason would prevent the query from running successfully?
A. The destination S3 bucket does not have the output folder.
B. The destination S3 bucket contains files from previous queries.
C. The destination S3 bucket has an S3 Lifecycle policy configured.
D. The destination S3 bucket has versioning enabled.
A financial services company hosts its data warehouse on a Hadoop cluster located in an on-premises data center. The data is 300 TB in size and grows by 1 TB each day. The data is generated in real time from the company's trading system. The raw data is transformed at the end of the trading day using a custom tool running on the Hadoop cluster.
The company is migrating its data warehouse to AWS using a managed data warehouse product provided by a third party that can ingest data from Amazon S3. The company has already established a 10 Gbps connection with an AWS Region using AWS Direct Connect. The company is required by its security and regulatory compliance policies to not transfer data over the public internet. The company wants to minimize changes to its custom tool for data transformation. The company also plans to eliminate the on-premises Hadoop cluster after the migration.
Which solution MOST cost-effectively meets these requirements?
A. Create a VPC endpoint for Amazon S3. Run a one-time copy job using the DistCp tool to copy existing files from Hadoop to a target S3 bucket over the VPC endpoint Schedule a nightly DistCp job on the Hadoop cluster to copy the incremental files produced by the custom tool to the target S3 bucket
B. Create a VPC endpoint for Amazon S3. Run a one-time copy job using the DistCp tool to copy existing files from Hadoop to a target S3 bucket over the VPC endpoint. Schedule a nightly job on the trading system servers that produces raw data to copy the incremental raw files to the target S3 bucket. Run the data transformation tool on a transient Amazon EMR cluster to output files to Amazon S3.
C. Create a VPC endpoint for Amazon S3. Run a one-time copy job using the DistCp tool to copy existing files from Hadoop to a target S3 bucket over the VPC endpoint. Set up an Amazon Kinesis data stream to ingest raw data from the trading system in real time. Use Amazon Kinesis Data Analytics to transform the raw data and output files to Amazon S3.
D. Complete a one-time transfer of the data using AWS Snowball Edge devices transferring to a target S3 bucket. Schedule a nightly job on the trading system servers that produces raw data to copy the incremental raw files to the target S3 bucket Run the data transformation tool on a transient Amazon EMR cluster to output files to Amazon S3.
A company uses an Amazon QuickSight business intelligence (BI) dashboard to make decisions. A data warehouse that is hosted on an Amazon Redshift cluster is the primary data source for the dashboard.
The user experience has degraded because some BI queries are complex and include multiple table joins. A data analytics specialist needs to reduce query response times to improve the user experience and meet SLAs.
Which solution will meet these requirements with the LEAST operational overhead?
A. Create automated materialized views. Turn on the query rewrite feature in Amazon Redshift.
B. Create views in Amazon Redshift. Change the QuickSight code to select from the views.
C. Create a new consolidated table by using CREATE TABLE AS (CTAS). Change the QuickSight code to select from the consolidated table.
D. Create an external table. Turn on the query rewrite feature in Amazon Redshift.
Nowadays, the certification exams become more and more important and required by more and more enterprises when applying for a job. But how to prepare for the exam effectively? How to prepare for the exam in a short time with less efforts? How to get a ideal result and how to find the most reliable resources? Here on Vcedump.com, you will find all the answers. Vcedump.com provide not only Amazon exam questions, answers and explanations but also complete assistance on your exam preparation and certification application. If you are confused on your DAS-C01 exam preparations and Amazon certification application, do not hesitate to visit our Vcedump.com to find your solutions here.