Top Tips Of Regenerate DAS-C01 Actual Exam

NEW QUESTION 1
A data analytics specialist is building an automated ETL ingestion pipeline using AWS Glue to ingest compressed files that have been uploaded to an Amazon S3 bucket. The ingestion pipeline should support incremental data processing.
Which AWS Glue feature should the data analytics specialist use to meet this requirement?

• A. Workflows
• B. Triggers
• C. Job bookmarks
• D. Classifiers

Answer: C

NEW QUESTION 2
A human resources company maintains a 10-node Amazon Redshift cluster to run analytics queries on the company’s data. The Amazon Redshift cluster contains a product table and a transactions table, and both tables have a product_sku column. The tables are over 100 GB in size. The majority of queries run on both tables.
Which distribution style should the company use for the two tables to achieve optimal query performance?

• A. An EVEN distribution style for both tables
• B. A KEY distribution style for both tables
• C. An ALL distribution style for the product table and an EVEN distribution style for the transactions table
• D. An EVEN distribution style for the product table and an KEY distribution style for the transactions table

Answer: B

NEW QUESTION 3
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 4
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. Upload the individual files to Amazon S3 and run the COPY command as soon as the files become available.
• B. Split the number of files so they are equal to a multiple of the number of slices in the Amazon Redshift cluste
• C. Gzip and upload the files to Amazon S3. Run the COPY command on the files.
• D. Split the number of files so they are equal to a multiple of the number of compute nodes in the Amazon Redshift cluste
• E. Gzip and upload the files to Amazon S3. Run the COPY command on the files.
• F. 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: B

NEW QUESTION 5
An online gaming company is using an Amazon Kinesis Data Analytics SQL application with a Kinesis data stream as its source. The source sends three non-null fields to the application: player_id, score, and us_5_digit_zip_code.
A data analyst has a .csv mapping file that maps a small number of us_5_digit_zip_code values to a territory code. The data analyst needs to include the territory code, if one exists, as an additional output of the Kinesis Data Analytics application.
How should the data analyst meet this requirement while minimizing costs?

• A. Store the contents of the mapping file in an Amazon DynamoDB tabl
• B. Preprocess the records as they arrive in the Kinesis Data Analytics application with an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exist
• C. Change the SQL query in the application to include the new field in the SELECT statement.
• D. Store the mapping file in an Amazon S3 bucket and configure the reference data column headers for the.csv file in the Kinesis Data Analytics applicatio
• E. Change the SQL query in the application to include a join to the file’s S3 Amazon Resource Name (ARN), and add the territory code field to the SELECT columns.
• F. Store the mapping file in an Amazon S3 bucket and configure it as a reference data source for the Kinesis Data Analytics applicatio
• G. Change the SQL query in the application to include a join to the reference table and add the territory code field to the SELECT columns.
• H. Store the contents of the mapping file in an Amazon DynamoDB tabl
• I. Change the Kinesis DataAnalytics application to send its output to an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exist
• J. Forward the record from the Lambda function to the original application destination.

Answer: C

NEW QUESTION 6
A media content company has a streaming playback application. The company wants to collect and analyze the data to provide near-real-time feedback on playback issues. The company needs to consume this data and return results within 30 seconds according to the service-level agreement (SLA). The company needs the consumer to identify playback issues, such as quality during a specified timeframe. The data will be emitted as JSON and may change schemas over time.
Which solution will allow the company to collect data for processing while meeting these requirements?

• A. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure an S3 event trigger an AWS Lambda function to process the dat
• B. The Lambda function will consume the data and process it to identify potential playback issue
• C. Persist the raw data to Amazon S3.
• D. Send the data to Amazon Managed Streaming for Kafka and configure an Amazon Kinesis Analytics for Java application as the consume
• E. The application will consume the data and process it to identify potential playback issue
• F. Persist the raw data to Amazon DynamoDB.
• G. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon S3 to trigger an event for AWS Lambda to proces
• H. The Lambda function will consume the data and process it to identify potential playback issue
• I. Persist the raw data to Amazon DynamoDB.
• J. Send the data to Amazon Kinesis Data Streams and configure an Amazon Kinesis Analytics for Java application as the consume
• K. The application will consume the data and process it to identify potential playback issue
• L. Persist the raw data to Amazon S3.

Answer: D

Explanation:
https://aws.amazon.com/blogs/aws/new-amazon-kinesis-data-analytics-for-java/

NEW QUESTION 7
A company wants to collect and process events data from different departments in near-real time. Before storing the data in Amazon S3, the company needs to clean the data by standardizing the format of the address and timestamp columns. The data varies in size based on the overall load at each particular point in time. A single data record can be 100 KB-10 MB.
How should a data analytics specialist design the solution for data ingestion?

• A. Use Amazon Kinesis Data Stream
• B. Configure a stream for the raw dat
• C. Use a Kinesis Agent to write data to the strea
• D. Create an Amazon Kinesis Data Analytics application that reads data from the raw stream, cleanses it, and stores the output to Amazon S3.
• E. Use Amazon Kinesis Data Firehos
• F. Configure a Firehose delivery stream with a preprocessing AWS Lambda function for data cleansin
• G. Use a Kinesis Agent to write data to the delivery strea
• H. Configure Kinesis Data Firehose to deliver the data to Amazon S3.
• I. Use Amazon Managed Streaming for Apache Kafk
• J. Configure a topic for the raw dat
• K. Use a Kafka producer to write data to the topi
• L. Create an application on Amazon EC2 that reads data from the topic by using the Apache Kafka consumer API, cleanses the data, and writes to Amazon S3.
• M. Use Amazon Simple Queue Service (Amazon SQS). Configure an AWS Lambda function to read events from the SQS queue and upload the events to Amazon S3.

Answer: B

NEW QUESTION 8
A company uses Amazon Elasticsearch Service (Amazon ES) to store and analyze its website clickstream data. The company ingests 1 TB of data daily using Amazon Kinesis Data Firehose and stores one day’s worth of data in an Amazon ES cluster.
The company has very slow query performance on the Amazon ES index and occasionally sees errors from Kinesis Data Firehose when attempting to write to the index. The Amazon ES cluster has 10 nodes running a single index and 3 dedicated master nodes. Each data node has 1.5 TB of Amazon EBS storage attached and the cluster is configured with 1,000 shards. Occasionally, JVMMemoryPressure errors are found in the cluster logs.
Which solution will improve the performance of Amazon ES?

• A. Increase the memory of the Amazon ES master nodes.
• B. Decrease the number of Amazon ES data nodes.
• C. Decrease the number of Amazon ES shards for the index.
• D. Increase the number of Amazon ES shards for the index.

Answer: C

Explanation:
https://aws.amazon.com/premiumsupport/knowledge-center/high-jvm-memory-pressure-elasticsearch/

NEW QUESTION 9
A company has 1 million scanned documents stored as image files in Amazon S3. The documents contain typewritten application forms with information including the applicant first name, applicant last name, application date, application type, and application text. The company has developed a machine learning algorithm to extract the metadata values from the scanned documents. The company wants to allow internal data analysts to analyze and find applications using the applicant name, application date, or application text. The original images should also be downloadable. Cost control is secondary to query performance.
Which solution organizes the images and metadata to drive insights while meeting the requirements?

• A. For each image, use object tags to add the metadat
• B. Use Amazon S3 Select to retrieve the files based on the applicant name and application date.
• C. Index the metadata and the Amazon S3 location of the image file in Amazon Elasticsearch Service.Allow the data analysts to use Kibana to submit queries to the Elasticsearch cluster.
• D. Store the metadata and the Amazon S3 location of the image file in an Amazon Redshift tabl
• E. Allow the data analysts to run ad-hoc queries on the table.
• F. Store the metadata and the Amazon S3 location of the image files in an Apache Parquet file in Amazon S3, and define a table in the AWS Glue Data Catalo
• G. Allow data analysts to use Amazon Athena to submit custom queries.

Answer: B

Explanation:
https://aws.amazon.com/blogs/machine-learning/automatically-extract-text-and-structured-data-from-documents

NEW QUESTION 10
A company uses Amazon Redshift for its data warehousing needs. ETL jobs run every night to load data, apply business rules, and create aggregate tables for reporting. The company's data analysis, data science, and business intelligence teams use the data warehouse during regular business hours. The workload management is set to auto, and separate queues exist for each team with the priority set to NORMAL.
Recently, a sudden spike of read queries from the data analysis team has occurred at least twice daily, and queries wait in line for cluster resources. The company needs a solution that enables the data analysis team to avoid query queuing without impacting latency and the query times of other teams.
Which solution meets these requirements?

• A. Increase the query priority to HIGHEST for the data analysis queue.
• B. Configure the data analysis queue to enable concurrency scaling.
• C. Create a query monitoring rule to add more cluster capacity for the data analysis queue when queries are waiting for resources.
• D. Use workload management query queue hopping to route the query to the next matching queue.

Answer: D

NEW QUESTION 11
A retail company wants to use Amazon QuickSight to generate dashboards for web and in-store sales. A group of 50 business intelligence professionals will develop and use the dashboards. Once ready, the dashboards will be shared with a group of 1,000 users.
The sales data comes from different stores and is uploaded to Amazon S3 every 24 hours. The data is partitioned by year and month, and is stored in Apache Parquet format. The company is using the AWS Glue Data Catalog as its main data catalog and Amazon Athena for querying. The total size of the uncompressed data that the dashboards query from at any point is 200 GB.
Which configuration will provide the MOST cost-effective solution that meets these requirements?

• A. Load the data into an Amazon Redshift cluster by using the COPY comman
• B. Configure 50 author users and 1,000 reader user
• C. Use QuickSight Enterprise editio
• D. Configure an Amazon Redshift data source with a direct query option.
• E. Use QuickSight Standard editio
• F. Configure 50 author users and 1,000 reader user
• G. Configure an Athena data source with a direct query option.
• H. Use QuickSight Enterprise editio
• I. Configure 50 author users and 1,000 reader user
• J. Configure an Athena data source and import the data into SPIC
• K. Automatically refresh every 24 hours.
• L. Use QuickSight Enterprise editio
• M. Configure 1 administrator and 1,000 reader user
• N. Configure an S3 data source and import the data into SPIC
• O. Automatically refresh every 24 hours.

Answer: C

NEW QUESTION 12
A company has developed an Apache Hive script to batch process data stared in Amazon S3. The script needs to run once every day and store the output in Amazon S3. The company tested the script, and it completes within 30 minutes on a small local three-node cluster.
Which solution is the MOST cost-effective for scheduling and executing the script?

• A. Create an AWS Lambda function to spin up an Amazon EMR cluster with a Hive execution ste
• B. Set KeepJobFlowAliveWhenNoSteps to false and disable the termination protection fla
• C. Use Amazon CloudWatch Events to schedule the Lambda function to run daily.
• D. Use the AWS Management Console to spin up an Amazon EMR cluster with Python Hu
• E. Hive, and Apache Oozi
• F. Set the termination protection flag to true and use Spot Instances for the core nodes of the cluste
• G. Configure an Oozie workflow in the cluster to invoke the Hive script daily.
• H. Create an AWS Glue job with the Hive script to perform the batch operatio
• I. Configure the job to run once a day using a time-based schedule.
• J. Use AWS Lambda layers and load the Hive runtime to AWS Lambda and copy the Hive script.Schedule the Lambda function to run daily by creating a workflow using AWS Step Functions.

Answer: C

NEW QUESTION 13
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. 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.
• B. Run the AWS Glue crawler in us-west-2 to catalog datasets in all Region
• C. Once the data is crawled, run Athena queries in us-west-2.
• D. 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.
• E. 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.

Answer: B

NEW QUESTION 14
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 fleet configurations for core and task node
• B. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metri
• C. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB metric.
• D. Create instance fleet configurations for core and task node
• E. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metri
• F. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch YARNMemoryAvailablePercentage metric.
• G. Create instance group configurations for core and task node
• H. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metri
• I. Create anautomatic scaling policy to scale in the instance groups based on the CloudWatch CapacityRemainingGB metric.
• J. Create instance group configurations for core and task node
• K. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metri
• L. 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 15
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. Run an AWS Glue crawler that connects to one or more data stores, determines the data structures, and writes tables in the Data Catalog.
• B. 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.
• C. Use the AWS Glue API CreateTable operation to create a table in the Data Catalo
• D. Create an AWS Glue crawler and specify the table as the source.
• E. Create an Apache Hive catalog in Amazon EMR with the table schema definition in Amazon S3, and update the table partition with a scheduled jo
• F. Migrate the Hive catalog to the Data Catalog.

Answer: C

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 16
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. Use Amazon Kinesis Data Firehose to upload compressed and batched clickstream records to Amazon Elasticsearch Service.
• B. Upload clickstream records to Amazon S3 as compressed file
• C. Then use AWS Lambda to send data to Amazon Elasticsearch Service from Amazon S3.
• D. Use Amazon Elasticsearch Service deployed on Amazon EC2 to aggregate, filter, and process the data.Refresh content performance dashboards in near-real time.
• E. Use Kibana to aggregate, filter, and visualize the data stored in Amazon Elasticsearch Servic
• F. Refresh content performance dashboards in near-real time.
• G. 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: AD

NEW QUESTION 17
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 18
A data analyst is using AWS Glue to organize, cleanse, validate, and format a 200 GB dataset. The data analyst triggered the job to run with the Standard worker type. After 3 hours, the AWS Glue job status is still RUNNING. Logs from the job run show no error codes. The data analyst wants to improve the job execution time without overprovisioning.
Which actions should the data analyst take?

• A. Enable job bookmarks in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the executor-cores job parameter.
• B. Enable job metrics in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the maximum capacity job parameter.
• C. Enable job metrics in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the spark.yarn.executor.memoryOverhead job parameter.
• D. Enable job bookmarks in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the num-executors job parameter.

Answer: B

NEW QUESTION 19
A marketing company wants to improve its reporting and business intelligence capabilities. During the planning phase, the company interviewed the relevant stakeholders and discovered that:
The operations team reports are run hourly for the current month’s data.
The sales team wants to use multiple Amazon QuickSight dashboards to show a rolling view of the last 30 days based on several categories.
The sales team also wants to view the data as soon as it reaches the reporting backend.
The finance team’s reports are run daily for last month’s data and once a month for the last 24 months of data.
Currently, there is 400 TB of data in the system with an expected additional 100 TB added every month. The company is looking for a solution that is as cost-effective as possible.
Which solution meets the company’s requirements?

• A. Store the last 24 months of data in Amazon Redshif
• B. Configure Amazon QuickSight with Amazon Redshift as the data source.
• C. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Set up an external schema and table for Amazon Redshift Spectru
• D. Configure Amazon QuickSight with Amazon Redshift as the data source.
• E. Store the last 24 months of data in Amazon S3 and query it using Amazon Redshift Spectrum.Configure Amazon QuickSight with Amazon Redshift Spectrum as the data source.
• F. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Use a long- running Amazon EMR with Apache Spark cluster to query the data as neede
• G. Configure Amazon QuickSight with Amazon EMR as the data source.

Answer: B

NEW QUESTION 20
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 21
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