[Nov-2021] Amazon DAS-C01 Dumps – Reduce Your Chance of Failure in DAS-C01 Exam
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NEW QUESTION 72
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 Apache Parquet partitioned by source IP and sorted by date
- C. In compressed nested JSON partitioned by source IP and sorted by date
- D. In compressed .csv partitioned by date and sorted by source IP
Answer: A
NEW QUESTION 73
A regional energy company collects voltage data from sensors attached to buildings. To address any known dangerous conditions, the company wants to be alerted when a sequence of two voltage drops is detected within 10 minutes of a voltage spike at the same building. It is important to ensure that all messages are delivered as quickly as possible. The system must be fully managed and highly available. The company also needs a solution that will automatically scale up as it covers additional cites with this monitoring feature. The alerting system is subscribed to an Amazon SNS topic for remediation.
Which solution meets these requirements?
- A. Create an Amazon Kinesis Data Firehose delivery stream to capture the incoming sensor data. Use an AWS Lambda transformation function to detect the known event sequence and send the SNS message.
- B. Create an Amazon Kinesis data stream to capture the incoming sensor data and create another stream for alert messages. Set up AWS Application Auto Scaling on both. Create a Kinesis Data Analytics for Java application to detect the known event sequence, and add a message to the message stream. Configure an AWS Lambda function to poll the message stream and publish to the SNS topic.
- C. Create a REST-based web service using Amazon API Gateway in front of an AWS Lambda function. Create an Amazon RDS for PostgreSQL database with sufficient Provisioned IOPS (PIOPS). In the Lambda function, store incoming events in the RDS database and query the latest data to detect the known event sequence and send the SNS message.
- D. Create an Amazon Managed Streaming for Kafka cluster to ingest the data, and use an Apache Spark Streaming with Apache Kafka consumer API in an automatically scaled Amazon EMR cluster to process the incoming data. Use the Spark Streaming application to detect the known event sequence and send the SNS message.
Answer: B
NEW QUESTION 74
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 Athen a. Users are seeing degradation in query performance as time progresses.
Which action can help improve query performance?
- A. Write the files to multiple S3 buckets.
- B. Add more memory and CPU capacity to the streaming application.
- C. Increase the number of shards in Kinesis Data Streams.
- D. Merge the files in Amazon S3 to form larger files.
Answer: D
Explanation:
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/
NEW QUESTION 75
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 CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance groups based on the CloudWatch CapacityRemainingGB metric.
- B. 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.
- 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 CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB metric.
- D. 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.
Answer: D
Explanation:
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-instances-guidelines.html
NEW QUESTION 76
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 Amazon Redshift Spectrum to query files from Amazon S3.
- B. Use the UNLOAD command to upload data into Amazon Redshift.
- C. Use the COPY command with the manifest file to load data into Amazon Redshift.
- D. Use S3DistCp to load files into Amazon Redshift.
- E. Use temporary staging tables during the loading process.
Answer: C,E
NEW QUESTION 77
A media company is using Amazon QuickSight dashboards to visualize its national sales dat a. The dashboard is using a dataset with these fields: ID, date, time_zone, city, state, country, longitude, latitude, sales_volume, and number_of_items.
To modify ongoing campaigns, the company wants an interactive and intuitive visualization of which states across the country recorded a significantly lower sales volume compared to the national average.
Which addition to the company's QuickSight dashboard will meet this requirement?
- A. A drill-down layer for state-level sales volume data.
- B. A pivot table of sales volume data summed up at the state level.
- C. A drill through to other dashboards containing state-level sales volume data.
- D. A geospatial color-coded chart of sales volume data across the country.
Answer: B
NEW QUESTION 78
A manufacturing company has been collecting IoT sensor data from devices on its factory floor for a year and is storing the data in Amazon Redshift for daily analysis. A data analyst has determined that, at an expected ingestion rate of about 2 TB per day, the cluster will be undersized in less than 4 months. A long-term solution is needed. The data analyst has indicated that most queries only reference the most recent 13 months of data, yet there are also quarterly reports that need to query all the data generated from the past 7 years. The chief technology officer (CTO) is concerned about the costs, administrative effort, and performance of a long-term solution.
Which solution should the data analyst use to meet these requirements?
- A. Create a daily job in AWS Glue to UNLOAD records older than 13 months to Amazon S3 and delete those records from Amazon Redshift. Create an external table in Amazon Redshift to point to the S3 location. Use Amazon Redshift Spectrum to join to data that is older than 13 months.
- B. Execute a CREATE TABLE AS SELECT (CTAS) statement to move records that are older than 13 months to quarterly partitioned data in Amazon Redshift Spectrum backed by Amazon S3.
- C. Take a snapshot of the Amazon Redshift cluster. Restore the cluster to a new cluster using dense storage nodes with additional storage capacity.
- D. Unload all the tables in Amazon Redshift to an Amazon S3 bucket using S3 Intelligent-Tiering. Use AWS Glue to crawl the S3 bucket location to create external tables in an AWS Glue Data Catalog.
Create an Amazon EMR cluster using Auto Scaling for any daily analytics needs, and use Amazon Athena for the quarterly reports, with both using the same AWS Glue Data Catalog.
Answer: A
NEW QUESTION 79
A marketing company is using Amazon EMR clusters for its workloads. The company manually installs third- party libraries on the clusters by logging in to the master nodes. A data analyst needs to create an automated solution to replace the manual process.
Which options can fulfill these requirements? (Choose two.)
- A. Launch an Amazon EC2 instance with Amazon Linux and install the required third-party libraries on the instance. Create an AMI and use that AMI to create the EMR cluster.
- B. Install the required third-party libraries in the existing EMR master node. Create an AMI out of that master node and use that custom AMI to re-create the EMR cluster.
- C. Use an Amazon DynamoDB table to store the list of required applications. Trigger an AWS Lambda function with DynamoDB Streams to install the software.
- D. Place the required installation scripts in Amazon S3 and execute them using custom bootstrap actions.
- E. Place the required installation scripts in Amazon S3 and execute them through Apache Spark in Amazon EMR.
Answer: A,D
Explanation:
Explanation
https://aws.amazon.com/about-aws/whats-new/2017/07/amazon-emr-now-supports-launching-clusters-with-custo
https://docs.aws.amazon.com/de_de/emr/latest/ManagementGuide/emr-plan-bootstrap.html
NEW QUESTION 80
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. Create custom logic on the ETL jobs to track the processed S3 objects.
- B. Have the ETL jobs read the data from Amazon S3 using a DataFrame.
- C. Enable job bookmarks on the AWS Glue jobs.
- D. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.
Answer: C
NEW QUESTION 81
A company currently uses Amazon Athena to query its global datasets. The regional data is stored in Amazon S3 in the us-east-1 and us-west-2 Regions. The data is not encrypted. To simplify the query process and manage it centrally, the company wants to use Athena in us-west-2 to query data from Amazon S3 in both Regions. The solution should be as low-cost as possible.
What should the company do to achieve this goal?
- A. Enable cross-Region replication for the S3 buckets in us-east-1 to replicate data in us-west-2. Once the data is replicated in us-west-2, run the AWS Glue crawler there to update the AWS Glue Data Catalog in us-west-2 and run Athena queries.
- B. Use AWS DMS to migrate the AWS Glue Data Catalog from us-east-1 to us-west-2. Run Athena queries in us-west-2.
- C. Update AWS Glue resource policies to provide us-east-1 AWS Glue Data Catalog access to us-west-2.
Once the catalog in us-west-2 has access to the catalog in us-east-1, run Athena queries in us-west-2. - D. Run the AWS Glue crawler in us-west-2 to catalog datasets in all Regions. Once the data is crawled, run Athena queries in us-west-2.
Answer: A
NEW QUESTION 82
Three teams of data analysts use Apache Hive on an Amazon EMR cluster with the EMR File System (EMRFS) to query data stored within each teams Amazon S3 bucket. The EMR cluster has Kerberos enabled and is configured to authenticate users from the corporate Active Directory. The data is highly sensitive, so access must be limited to the members of each team.
Which steps will satisfy the security requirements?
- A. For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3.
Create three additional IAM roles, each granting access to each team's specific bucket. Add the service role for the EMR cluster EC2 instances to the trust policies for the additional IAM roles. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team. - B. For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3.
Create three additional IAM roles, each granting access to each team's specific bucket. Add the service role for the EMR cluster EC2 instances to the trust polices for the base IAM roles. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team. - C. For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3.
Create three additional IAM roles, each granting access to each team's specific bucket. Add the service role for the EMR cluster EC2 instances to the trust polices for the additional IAM roles. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team. - D. For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3.
Create three additional IAM roles, each granting access to each team's specific bucket. Add the additional IAM roles to the cluster's EMR role for the EC2 trust policy. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
Answer: C
NEW QUESTION 83
A retail company's data analytics team recently created multiple product sales analysis dashboards for the average selling price per product using Amazon QuickSight. The dashboards were created from .csv files uploaded to Amazon S3. The team is now planning to share the dashboards with the respective external product owners by creating individual users in Amazon QuickSight. For compliance and governance reasons, restricting access is a key requirement. The product owners should view only their respective product analysis in the dashboard reports.
Which approach should the data analytics team take to allow product owners to view only their products in the dashboard?
- A. Create dataset rules with row-level security.
- B. Create a manifest file with row-level security.
- C. Separate the data by product and use IAM policies for authorization.
- D. Separate the data by product and use S3 bucket policies for authorization.
Answer: C
NEW QUESTION 84
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 Firehose to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.
- C. 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.
- D. Use Amazon Kinesis Data Streams to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.
Answer: C
NEW QUESTION 85
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. 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.
- C. Upload the individual files to Amazon S3 and run the COPY command as soon as the files become available.
- D. 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.
Answer: A
NEW QUESTION 86
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. Partition the data by sensor, year, month, and day.
- B. Store the data in Apache Parquet format using Snappy compression.
- C. Store the data in Apache ORC format using no compression.
- D. Partition the data by year, month, and day.
- E. Store the data in Apache Avro format using Snappy compression.
Answer: B,C
NEW QUESTION 87
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