All About Precise DAS-C01 Study Guides

NEW QUESTION 1
A company is building a data lake and needs to ingest data from a relational database that has time-series data. The company wants to use managed services to accomplish this. The process needs to be scheduled daily and bring incremental data only from the source into Amazon S3.
What is the MOST cost-effective approach to meet these requirements?

• A. Use AWS Glue to connect to the data source using JDBC Driver
• B. Ingest incremental records only using job bookmarks.
• C. Use AWS Glue to connect to the data source using JDBC Driver
• D. Store the last updated key in an Amazon DynamoDB table and ingest the data using the updated key as a filter.
• E. Use AWS Glue to connect to the data source using JDBC Drivers and ingest the entire datase
• F. Use appropriate Apache Spark libraries to compare the dataset, and find the delta.
• G. Use AWS Glue to connect to the data source using JDBC Drivers and ingest the full dat
• H. Use AWSDataSync to ensure the delta only is written into Amazon S3.

Answer: A

Explanation:
https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html

NEW QUESTION 2
A data analytics specialist is setting up workload management in manual mode for an Amazon Redshift environment. The data analytics specialist is defining query monitoring rules to manage system performance and user experience of an Amazon Redshift cluster.
Which elements must each query monitoring rule include?

• A. A unique rule name, a query runtime condition, and an AWS Lambda function to resubmit any failed queries in off hours
• B. A queue name, a unique rule name, and a predicate-based stop condition
• C. A unique rule name, one to three predicates, and an action
• D. A workload name, a unique rule name, and a query runtime-based condition

Answer: C

NEW QUESTION 3
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 4
A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis. The application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon CloudWatch Logs. The enrichment source is stored in an Amazon DynamoDB table.
Which solution meets the requirements for the event collection and enrichment?

• A. Use a CloudWatch Logs subscription to send the data to Amazon Kinesis Data Firehos
• B. Use AWS Lambda to transform the data in the Kinesis Data Firehose delivery stream and enrich it with the data in the DynamoDB tabl
• C. Configure Amazon S3 as the Kinesis Data Firehose delivery destination.
• D. Export the raw logs to Amazon S3 on an hourly basis using the AWS CL
• E. Use AWS Glue crawlers to catalog the log
• F. Set up an AWS Glue connection for the DynamoDB table and set up an AWS Glue ETL job to enrich the dat
• G. Store the enriched data in Amazon S3.
• H. Configure the application to write the logs locally and use Amazon Kinesis Agent to send the data to Amazon Kinesis Data Stream
• I. Configure a Kinesis Data Analytics SQL application with the Kinesis data stream as the sourc
• J. Join the SQL application input stream with DynamoDB records, and then store the enriched output stream in Amazon S3 using Amazon Kinesis Data Firehose.
• K. Export the raw logs to Amazon S3 on an hourly basis using the AWS CL
• L. Use Apache Spark SQL on Amazon EMR to read the logs from Amazon S3 and enrich the records with the data from DynamoD
• M. Store the enriched data in Amazon S3.

Answer: A

Explanation:
https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html#FirehoseExample

NEW QUESTION 5
A company uses the Amazon Kinesis SDK to write data to Kinesis Data Streams. Compliance requirements state that the data must be encrypted at rest using a key that can be rotated. The company wants to meet this encryption requirement with minimal coding effort.
How can these requirements be met?

• A. Create a customer master key (CMK) in AWS KM
• B. Assign the CMK an alia
• C. Use the AWS Encryption SDK, providing it with the key alias to encrypt and decrypt the data.
• D. Create a customer master key (CMK) in AWS KM
• E. Assign the CMK an alia
• F. Enable server-side encryption on the Kinesis data stream using the CMK alias as the KMS master key.
• G. Create a customer master key (CMK) in AWS KM
• H. Create an AWS Lambda function to encrypt and decrypt the dat
• I. Set the KMS key ID in the function’s environment variables.
• J. Enable server-side encryption on the Kinesis data stream using the default KMS key for Kinesis Data Streams.

Answer: B

NEW QUESTION 6
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. Increase the Kinesis Data Streams retention period to reduce throttling.
• B. Replace the Kinesis API-based data ingestion mechanism with Kinesis Agent.
• C. Increase the number of shards in the stream using the UpdateShardCount API.
• D. Choose partition keys in a way that results in a uniform record distribution across shards.
• E. Customize the application code to include retry logic to improve performance.

Answer: CD

Explanation:
https://aws.amazon.com/blogs/big-data/under-the-hood-scaling-your-kinesis-data-streams/

NEW QUESTION 7
A company is hosting an enterprise reporting solution with Amazon Redshift. The application provides reporting capabilities to three main groups: an executive group to access financial reports, a data analyst group to run long-running ad-hoc queries, and a data engineering group to run stored procedures and ETL processes. The executive team requires queries to run with optimal performance. The data engineering team expects queries to take minutes.
Which Amazon Redshift feature meets the requirements for this task?

• A. Concurrency scaling
• B. Short query acceleration (SQA)
• C. Workload management (WLM)
• D. Materialized views

Answer: D

Explanation:

Materialized views:

NEW QUESTION 8
A retail company has 15 stores across 6 cities in the United States. Once a month, the sales team requests a visualization in Amazon QuickSight that provides the ability to easily identify revenue trends across cities and stores. The visualization also helps identify outliers that need to be examined with further analysis.
Which visual type in QuickSight meets the sales team's requirements?

• A. Geospatial chart
• B. Line chart
• C. Heat map
• D. Tree map

Answer: A

NEW QUESTION 9
A company has collected more than 100 TB of log files in the last 24 months. The files are stored as raw text in a dedicated Amazon S3 bucket. Each object has a key of the form year-month-day_log_HHmmss.txt where HHmmss represents the time the log file was initially created. A table was created in Amazon Athena that points to the S3 bucket. One-time queries are run against a subset of columns in the table several times an hour.
A data analyst must make changes to reduce the cost of running these queries. Management wants a solution with minimal maintenance overhead.
Which combination of steps should the data analyst take to meet these requirements? (Choose three.)

• A. Convert the log files to Apace Avro format.
• B. Add a key prefix of the form date=year-month-day/ to the S3 objects to partition the data.
• C. Convert the log files to Apache Parquet format.
• D. Add a key prefix of the form year-month-day/ to the S3 objects to partition the data.
• E. Drop and recreate the table with the PARTITIONED BY claus
• F. Run the ALTER TABLE ADD PARTITION statement.
• G. Drop and recreate the table with the PARTITIONED BY claus
• H. Run the MSCK REPAIR TABLE statement.

Answer: BCF

NEW QUESTION 10
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 11
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. 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.
• B. Use a lifecycle policy to change the S3 storage class to S3 Standard for the log data.
• C. Increase the read capacity units (RCUs) for the shared Amazon DynamoDB table.
• D. Redeploy the EMR clusters that are running slowly to a different Availability Zone.

Answer: C

Explanation:
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emrfs-metadata.html

NEW QUESTION 12
A company wants to research user turnover by analyzing the past 3 months of user activities. With millions of users, 1.5 TB of uncompressed data is generated each day. A 30-node Amazon Redshift cluster with 2.56 TB of solid state drive (SSD) storage for each node is required to meet the query performance goals.
The company wants to run an additional analysis on a year’s worth of historical data to examine trends indicating which features are most popular. This analysis will be done once a week.
What is the MOST cost-effective solution?

• A. Increase the size of the Amazon Redshift cluster to 120 nodes so it has enough storage capacity to hold 1 year of dat
• B. Then use Amazon Redshift for the additional analysis.
• C. Keep the data from the last 90 days in Amazon Redshif
• D. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by dat
• E. Then use Amazon Redshift Spectrum for the additional analysis.
• F. Keep the data from the last 90 days in Amazon Redshif
• G. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by dat
• H. Then provision a persistent Amazon EMR cluster and use Apache Presto for the additional analysis.
• I. Resize the cluster node type to the dense storage node type (DS2) for an additional 16 TB storage capacity on each individual node in the Amazon Redshift cluste
• J. Then use Amazon Redshift for the additional analysis.

Answer: B

NEW QUESTION 13
A financial company uses Amazon S3 as its data lake and has set up a data warehouse using a multi-node Amazon Redshift cluster. The data files in the data lake are organized in folders based on the data source of each data file. All the data files are loaded to one table in the Amazon Redshift cluster using a separate COPY command for each data file location. With this approach, loading all the data files into Amazon Redshift takes a long time to complete. Users want a faster solution with little or no increase in cost while maintaining the segregation of the data files in the S3 data lake.
Which solution meets these requirements?

• A. Use Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
• B. Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job to load the data into Amazon Redshift.
• C. Use an AWS Glue job to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
• D. Create a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon Redshift.

Answer: D

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/loading-data-files-using-manifest.html "You can use a manifest to ensure that the COPY command loads all of the required files, and only the required files, for a data load"

NEW QUESTION 14
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 Redshif
• B. Create an external table in Amazon Redshift to point to the S3 locatio
• C. Use Amazon Redshift Spectrum to join to data that is older than 13 months.
• D. Take a snapshot of the Amazon Redshift cluste
• E. Restore the cluster to a new cluster using dense storage nodes with additional storage capacity.
• F. 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.
• G. Unload all the tables in Amazon Redshift to an Amazon S3 bucket using S3 Intelligent-Tierin
• H. Use AWS Glue to crawl the S3 bucket location to create external tables in an AWS Glue Data Catalo
• I. 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 15
A real estate company has a mission-critical application using Apache HBase in Amazon EMR. Amazon EMR is configured with a single master node. The company has over 5 TB of data stored on an Hadoop Distributed File System (HDFS). The company wants a cost-effective solution to make its HBase data highly available.
Which architectural pattern meets company’s requirements?

• A. Use Spot Instances for core and task nodes and a Reserved Instance for the EMR master node.Configurethe EMR cluster with multiple master node
• B. Schedule automated snapshots using AmazonEventBridge.
• C. Store the data on an EMR File System (EMRFS) instead of HDF
• D. Enable EMRFS consistent view.Create an EMR HBase cluster with multiple master node
• E. Point the HBase root directory to an Amazon S3 bucket.
• F. Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view.Run two separate EMR clusters in two different Availability Zone
• G. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
• H. Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view.Create a primary EMR HBase cluster with multiple master node
• I. Create a secondary EMR HBase read- replica cluster in a separate Availability Zon
• J. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.

Answer: D

NEW QUESTION 16
A transportation company uses IoT sensors attached to trucks to collect vehicle data for its global delivery fleet. The company currently sends the sensor data in small .csv files to Amazon S3. The files are then loaded into a 10-node Amazon Redshift cluster with two slices per node and queried using both Amazon Athena and Amazon Redshift. The company wants to optimize the files to reduce the cost of querying and also improve the speed of data loading into the Amazon Redshift cluster.
Which solution meets these requirements?

• A. Use AWS Glue to convert all the files from .csv to a single large Apache Parquet fil
• B. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• C. Use Amazon EMR to convert each .csv file to Apache Avr
• D. COPY the files into Amazon Redshift and query the file with Athena from Amazon S3.
• E. Use AWS Glue to convert the files from .csv to a single large Apache ORC fil
• F. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• G. Use AWS Glue to convert the files from .csv to Apache Parquet to create 20 Parquet file
• H. COPY the files into Amazon Redshift and query the files with Athena from Amazon S3.

Answer: D

NEW QUESTION 17
An IoT company wants to release a new device that will collect data to track sleep overnight on an intelligent mattress. Sensors will send data that will be uploaded to an Amazon S3 bucket. About 2 MB of data is generated each night for each bed. Data must be processed and summarized for each user, and the results need to be available as soon as possible. Part of the process consists of time windowing and other functions. Based on tests with a Python script, every run will require about 1 GB of memory and will complete within a couple of minutes.
Which solution will run the script in the MOST cost-effective way?

• A. AWS Lambda with a Python script
• B. AWS Glue with a Scala job
• C. Amazon EMR with an Apache Spark script
• D. AWS Glue with a PySpark job

Answer: A

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 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. Place the required installation scripts in Amazon S3 and execute them using custom bootstrap actions.
• B. Place the required installation scripts in Amazon S3 and execute them through Apache Spark in Amazon EMR.
• C. Install the required third-party libraries in the existing EMR master nod
• D. Create an AMI out of that master node and use that custom AMI to re-create the EMR cluster.
• E. Use an Amazon DynamoDB table to store the list of required application
• F. Trigger an AWS Lambda function with DynamoDB Streams to install the software.
• G. Launch an Amazon EC2 instance with Amazon Linux and install the required third-party libraries on the instanc
• H. Create an AMI and use that AMI to create the EMR cluster.

Answer: AE

Explanation:
https://aws.amazon.com/about-aws/whats-new/2017/07/amazon-emr-now-supports-launching-clusters-with-cust https://docs.aws.amazon.com/de_de/emr/latest/ManagementGuide/emr-plan-bootstrap.html

NEW QUESTION 20
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 bucke
• B. Add the additional IAM roles to the cluster’s EMR role for the EC2 trust polic
• C. 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 bucke
• E. Add the service role for the EMR cluster EC2 instances to the trust policies for the additional IAM role
• F. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
• G. 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 bucke
• H. Add the service role for the EMR cluster EC2 instances to the trust polices for the additional IAM role
• I. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
• J. 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 bucke
• K. Add the service role for the EMR cluster EC2 instances to the trust polices for the base IAM role
• L. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.

Answer: C

NEW QUESTION 21
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