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  • [Apr-2024] Updated and Accurate DAS-C01 Questions & Answers for passing the exam Quickly [Q61-Q84]

[Apr-2024] Updated and Accurate DAS-C01 Questions & Answers for passing the exam Quickly [Q61-Q84]

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[Apr-2024] Updated and Accurate DAS-C01 Questions & Answers for passing the exam Quickly

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Amazon AWS Certified Data Analytics Specialty (DAS-C01) Certification Exam is a professional certification designed for individuals who are interested in pursuing a career in data analytics. AWS Certified Data Analytics - Specialty (DAS-C01) Exam certification is intended for those who possess a deep understanding of data analytics and want to demonstrate their expertise to potential employers.

 

NEW QUESTION # 61
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 compute nodes 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 slices 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: B


NEW QUESTION # 62
A company wants to improve the data load time of a sales data dashboard. Data has been collected as .csv files and stored within an Amazon S3 bucket that is partitioned by date. The data is then loaded to an Amazon Redshift data warehouse for frequent analysis. The data volume is up to 500 GB per day.
Which solution will improve the data loading performance?

  • A. Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.
  • B. Split large .csv files, then use a COPY command to load data into Amazon Redshift.
  • C. Compress .csv files and use an INSERT statement to ingest data into Amazon Redshift.
  • D. Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.

Answer: B

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/c_loading-data-best-practices.html


NEW QUESTION # 63
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 table. 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 exists. Change the SQL query in the application to include the new field in the SELECT statement.
  • B. 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 application. 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.
  • C. Store the mapping file in an Amazon S3 bucket and configure it as a reference data source for the Kinesis Data Analytics application. 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.
  • D. Store the contents of the mapping file in an Amazon DynamoDB table. Change the Kinesis Data Analytics 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 exists. Forward the record from the Lambda function to the original application destination.

Answer: C


NEW QUESTION # 64
A company is planning to do a proof of concept for a machine learning (ML) project using Amazon SageMaker with a subset of existing on-premises data hosted in the company's 3 TB data warehouse. For part of the project, AWS Direct Connect is established and tested. To prepare the data for ML, data analysts are performing data curation. The data analysts want to perform multiple step, including mapping, dropping null fields, resolving choice, and splitting fields. The company needs the fastest solution to curate the data for this project.
Which solution meets these requirements?

  • A. Ingest data into Amazon S3 using AWS DataSync and use Apache Spark scrips to curate the data in an Amazon EMR cluster. Store the curated data in Amazon S3 for ML processing.
  • B. Take a full backup of the data store and ship the backup files using AWS Snowball. Upload Snowball data into Amazon S3 and schedule data curation jobs using AWS Batch to prepare the data for ML.
  • C. Ingest data into Amazon S3 using AWS DMS. Use AWS Glue to perform data curation and store the data in Amazon S3 for ML processing.
  • D. Create custom ETL jobs on-premises to curate the data. Use AWS DMS to ingest data into Amazon S3 for ML processing.

Answer: C


NEW QUESTION # 65
A company uses Amazon Elasticsearch Service (Amazon ES) to store and analyze its website clickstream dat a. 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. Decrease the number of Amazon ES data nodes.
  • B. Increase the memory of the Amazon ES master 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 # 66
A large energy company is using Amazon QuickSight to build dashboards and report the historical usage data of its customers This data is hosted in Amazon Redshift The reports need access to all the fact tables' billions ot records to create aggregation in real time grouping by multiple dimensions A data analyst created the dataset in QuickSight by using a SQL query and not SPICE Business users have noted that the response time is not fast enough to meet their needs Which action would speed up the response time for the reports with the LEAST implementation effort?

  • A. Use Amazon Redshift to create a materialized view that joins the fact table with the dimensions
  • B. Use Amazon Redshift to create a stored procedure that joins the fact table with the dimensions Load the data into a new table
  • C. Use AWS Glue to create an Apache Spark job that joins the fact table with the dimensions. Load the data into a new table
  • D. Use QuickSight to modify the current dataset to use SPICE

Answer: D


NEW QUESTION # 67
A bank wants to migrate a Teradata data warehouse to the AWS Cloud The bank needs a solution for reading large amounts of data and requires the highest possible performance. The solution also must maintain the separation of storage and compute Which solution meets these requirements?

  • A. Use Amazon Redshift with dense compute nodes to query the data in Amazon Redshift managed storage
  • B. Use PrestoDB on Amazon EMR to query the data in Amazon S3
  • C. Use Amazon Athena to query the data in Amazon S3
  • D. Use Amazon Redshift with RA3 nodes to query the data in Amazon Redshift managed storage

Answer: D


NEW QUESTION # 68
A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries are queued and take a long time to execute. The company needs to optimize query execution and avoid any downtime.
What is the MOST cost-effective solution?

  • A. Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.
  • B. Enable concurrency scaling in the workload management (WLM) queue.
  • C. Use a snapshot, restore, and resize operation. Switch to the new target cluster.
  • D. Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.

Answer: B

Explanation:
Explanation
https://docs.aws.amazon.com/redshift/latest/dg/cm-c-implementing-workload-management.html


NEW QUESTION # 69
A large ecommerce company uses Amazon DynamoDB with provisioned read capacity and auto scaled write capacity to store its product catalog. The company uses Apache HiveQL statements on an Amazon EMR cluster to query the DynamoDB table. After the company announced a sale on all of its products, wait times for each query have increased. The data analyst has determined that the longer wait times are being caused by throttling when querying the table.
Which solution will solve this issue?

  • A. Increase the number of EMR nodes that are in the cluster.
  • B. Increase the DynamoDB table's provisioned write throughput.
  • C. Increase the DynamoDB table's provisioned read throughput.
  • D. Increase the size of the EMR nodes that are provisioned.

Answer: C


NEW QUESTION # 70
A company is reading data from various customer databases that run on Amazon RDS. The databases contain many inconsistent fields For example, a customer record field that is place_id in one database is location_id in another database. The company wants to link customer records across different databases, even when many customer record fields do not match exactly Which solution will meet these requirements with the LEAST operational overhead?

  • A. Create an Amazon EMR cluster to process and analyze data in the databases Connect to the Apache Zeppelin notebook, and use the FindMatches transform to find duplicate records in the data.
  • B. Create an AWS Glue crawler to crawl the data in the databases Use Amazon SageMaker to construct Apache Spark ML pipelines to find duplicate records in the data
  • C. Create an Amazon EMR cluster to process and analyze data in the databases. Connect to the Apache Zeppelin notebook, and use Apache Spark ML to find duplicate records in the data. Evaluate and tune the model by evaluating performance and results of finding duplicates
  • D. Create an AWS Glue crawler to crawl the databases. Use the FindMatches transform to find duplicate records in the data Evaluate and tune the transform by evaluating performance and results of finding matches

Answer: D


NEW QUESTION # 71
A manufacturing company uses Amazon Connect to manage its contact center and Salesforce to manage its customer relationship management (CRM) data. The data engineering team must build a pipeline to ingest data from the contact center and CRM system into a data lake that is built on Amazon S3.
What is the MOST efficient way to collect data in the data lake with the LEAST operational overhead?

  • A. Use Amazon Kinesis Data Firehose to ingest Amazon Connect data and Amazon Kinesis Data Streams to ingest Salesforce data.
  • B. Use Amazon Kinesis Data Streams to ingest Amazon Connect data and Amazon AppFlow to ingest Salesforce data.
  • C. Use Amazon AppFlow to ingest Amazon Connect data and Amazon Kinesis Data Firehose to ingest Salesforce data.
  • D. Use Amazon Kinesis Data Firehose to ingest Amazon Connect data and Amazon AppFlow to ingest Salesforce data.

Answer: A


NEW QUESTION # 72
A market data company aggregates external data sources to create a detailed view of product consumption in different countries. The company wants to sell this data to external parties through a subscription. To achieve this goal, the company needs to make its data securely available to external parties who are also AWS users.
What should the company do to meet these requirements with the LEAST operational overhead?

  • A. Store the data in Amazon S3. Share the data by using S3 bucket ACLs.
  • B. Upload the data to AWS Data Exchange for storage. Share the data by using the AWS Data Exchange sharing wizard.
  • C. Upload the data to AWS Data Exchange for storage. Share the data by using presigned URLs for security.
  • D. Store the data in Amazon S3. Share the data by using presigned URLs for security.

Answer: D


NEW QUESTION # 73
A company wants to improve user satisfaction for its smart home system by adding more features to its recommendation engine. Each sensor asynchronously pushes its nested JSON data into Amazon Kinesis Data Streams using the Kinesis Producer Library (KPL) in Jav a. Statistics from a set of failed sensors showed that, when a sensor is malfunctioning, its recorded data is not always sent to the cloud.
The company needs a solution that offers near-real-time analytics on the data from the most updated sensors. Which solution enables the company to meet these requirements?

  • A. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Java. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL script. Direct the output of KDA application to a Kinesis Data Firehose delivery stream, enable the data transformation feature to flatten the JSON file, and set the Kinesis Data Firehose destination to an Amazon Elasticsearch Service cluster.
  • B. Set the RecordMaxBufferedTime property of the KPL to "-1" to disable the buffering on the sensor side. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL script. Push the enriched data to a fleet of Kinesis data streams and enable the data transformation feature to flatten the JSON file. Instantiate a dense storage Amazon Redshift cluster and use it as the destination for the Kinesis Data Firehose delivery stream.
  • C. Set the RecordMaxBufferedTime property of the KPL to "0" to disable the buffering on the sensor side. Connect for each stream a dedicated Kinesis Data Firehose delivery stream and enable the data transformation feature to flatten the JSON file before sending it to an Amazon S3 bucket. Load the S3 data into an Amazon Redshift cluster.
  • D. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Java. Use AWS Glue to fetch and process data from the stream using the Kinesis Client Library (KCL). Instantiate an Amazon Elasticsearch Service cluster and use AWS Lambda to directly push data into it.

Answer: A

Explanation:
https://docs.aws.amazon.com/streams/latest/dev/developing-producers-with-kpl.html The KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly.


NEW QUESTION # 74
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

  • A. Keep all files about the same size.
  • B. Partition the data based on the most common query predicates
  • C. Use file formats that are not splittable
  • D. Use a columnar storage file format
  • E. Split the data into KB-sized files.

Answer: A,B,E


NEW QUESTION # 75
A marketing company collects clickstream data The company sends the data to Amazon Kinesis Data Firehose and stores the data in Amazon S3 The company wants to build a series of dashboards that will be used by hundreds of users across different departments The company will use Amazon QuickSight to develop these dashboards The company has limited resources and wants a solution that could scale and provide daily updates about clickstream activity Which combination of options will provide the MOST cost-effective solution? (Select TWO )

  • A. Use Amazon Athena to query the clickstream data in Amazon S3
  • B. Use S3 analytics to query the clickstream data
  • C. Use Amazon Redshift to store and query the clickstream data
  • D. Use QuickSight with a direct SQL query
  • E. Use the QuickSight SPICE engine with a daily refresh

Answer: B,D


NEW QUESTION # 76
A central government organization is collecting events from various internal applications using Amazon Managed Streaming for Apache Kafka (Amazon MSK). The organization has configured a separate Kafka topic for each application to separate the dat a. For security reasons, the Kafka cluster has been configured to only allow TLS encrypted data and it encrypts the data at rest.
A recent application update showed that one of the applications was configured incorrectly, resulting in writing data to a Kafka topic that belongs to another application. This resulted in multiple errors in the analytics pipeline as data from different applications appeared on the same topic. After this incident, the organization wants to prevent applications from writing to a topic different than the one they should write to.
Which solution meets these requirements with the least amount of effort?

  • A. Install Kafka Connect on each application instance and configure each Kafka Connect instance to write to a specific topic only.
  • B. Create a different Amazon EC2 security group for each application. Create an Amazon MSK cluster and Kafka topic for each application. Configure each security group to have access to the specific cluster.
  • C. Create a different Amazon EC2 security group for each application. Configure each security group to have access to a specific topic in the Amazon MSK cluster. Attach the security group to each application based on the topic that the applications should read and write to.
  • D. Use Kafka ACLs and configure read and write permissions for each topic. Use the distinguished name of the clients' TLS certificates as the principal of the ACL.

Answer: A


NEW QUESTION # 77
A company has a business unit uploading .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to do discovery, and create tables and schemas. An AWS Glue job writes processed data from the created tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creating the Amazon Redshift table appropriately. When the AWS Glue job is rerun for any reason in a day, duplicate records are introduced into the Amazon Redshift table.
Which solution will update the Redshift table without duplicates when jobs are rerun?

  • A. Load the previously inserted data into a MySQL database in the AWS Glue job. Perform an upsert operation in MySQL, and copy the results to the Amazon Redshift table.
  • B. Use Apache Spark's DataFrame dropDuplicates() API to eliminate duplicates and then write the data to Amazon Redshift.
  • C. Use the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.
  • D. Modify the AWS Glue job to copy the rows into a staging table. Add SQL commands to replace the existing rows in the main table as postactions in the DynamicFrameWriter class.

Answer: D

Explanation:
https://aws.amazon.com/premiumsupport/knowledge-center/sql-commands-redshift-glue-job/ See the section Merge an Amazon Redshift table in AWS Glue (upsert)


NEW QUESTION # 78
A healthcare company ingests patient data from multiple data sources and stores it in an Amazon S3 staging bucket. An AWS Glue ETL job transforms the data, which is written to an S3-based data lake to be queried using Amazon Athen a. The company wants to match patient records even when the records do not have a common unique identifier.
Which solution meets this requirement?

  • A. Train and use the AWS Glue FindMatches ML transform in the ETLjob
  • B. Use Amazon Macie pattern matching as part of the ETLjob
  • C. Partition tables and use the ETL job to partition the data on patient name
  • D. Train and use the AWS Glue PySpark filter class in the ETLjob

Answer: A


NEW QUESTION # 79
A company developed a new elections reporting website that uses Amazon Kinesis Data Firehose to deliver full logs from AWS WAF to an Amazon S3 bucket. The company is now seeking a low-cost option to perform this infrequent data analysis with visualizations of logs in a way that requires minimal development effort.
Which solution meets these requirements?

  • A. Create a second Kinesis Data Firehose delivery stream to deliver the log files to Amazon Elasticsearch Service (Amazon ES). Use Amazon ES to perform text-based searches of the logs for ad-hoc analyses and use Kibana for data visualizations.
  • B. Create an Amazon EMR cluster and use Amazon S3 as the data source. Create an Apache Spark job to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.
  • C. Create an AWS Lambda function to convert the logs into .csv format. Then add the function to the Kinesis Data Firehose transformation configuration. Use Amazon Redshift to perform ad-hoc analyses of the logs using SQL queries and use Amazon QuickSight to develop data visualizations.
  • D. Use an AWS Glue crawler to create and update a table in the Glue data catalog from the logs. Use Athena to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.

Answer: D

Explanation:
https://aws.amazon.com/blogs/big-data/analyzing-aws-waf-logs-with-amazon-es-amazon-athena-and-amazon-quicksight/


NEW QUESTION # 80
A company has a process that writes two datasets in CSV format to an Amazon S3 bucket every 6 hours. The company needs to join the datasets, convert the data to Apache Parquet, and store the data within another bucket for users to query using Amazon Athen a. The data also needs to be loaded to Amazon Redshift for advanced analytics. The company needs a solution that is resilient to the failure of any individual job component and can be restarted in case of an error.
Which solution meets these requirements with the LEAST amount of operational overhead?

  • A. Use AWS Step Functions to orchestrate an Amazon EMR cluster running Apache Spark. Use PySpark to generate data frames of the datasets in Amazon S3, transform the data, join the data, write the data back to Amazon S3, and load the data to Amazon Redshift.
  • B. Create an AWS Glue job using Python Shell that generates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift. Use an AWS Glue workflow to orchestrate the AWS Glue job at the desired frequency.
  • C. Create an AWS Glue job using PySpark that creates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift. Use an AWS Glue workflow to orchestrate the AWS Glue job.
  • D. Use AWS Step Functions to orchestrate the AWS Glue job. Create an AWS Glue job using Python Shell that creates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift.

Answer: C

Explanation:
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analytics1. It can process datasets from various sources and formats, such as CSV and Parquet, and write them to different destinations, such as Amazon S3 and Amazon Redshift2.
AWS Glue provides two types of jobs: Spark and Python Shell. Spark jobs run on Apache Spark, a distributed processing framework that supports a wide range of data processing tasks3. Python Shell jobs run Python scripts on a managed serverless infrastructure4. Spark jobs are more suitable for complex data transformations and joins than Python Shell jobs.
AWS Glue provides dynamic frames, which are an extension of Apache Spark data frames. Dynamic frames handle schema variations and errors in the data more easily than data frames. They also provide a set of transformations that can be applied to the data, such as join, filter, map, etc.
AWS Glue provides workflows, which are directed acyclic graphs (DAGs) that orchestrate multiple ETL jobs and crawlers. Workflows can handle dependencies, retries, error handling, and concurrency for ETL jobs and crawlers. They can also be triggered by schedules or events.
By creating an AWS Glue job using PySpark that creates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift, the company can perform the required ETL tasks with a single job. By using an AWS Glue workflow to orchestrate the AWS Glue job, the company can schedule and monitor the job execution with minimal operational overhead.


NEW QUESTION # 81
A company owns facilities with IoT devices installed across the world. The company is using Amazon Kinesis Data Streams to stream data from the devices to Amazon S3. The company's operations team wants to get insights from the IoT data to monitor data quality at ingestion. The insights need to be derived in near-real time, and the output must be logged to Amazon DynamoDB for further analysis.
Which solution meets these requirements?

  • A. Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using the default output from Kinesis Data Analytics.
  • B. Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using an AWS Lambda function.
  • C. Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function.
    Save the output to DynamoDB by using the default output from Kinesis Data Firehose.
  • D. Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function.
    Save the data to Amazon S3. Then run an AWS Glue job on schedule to ingest the data into DynamoDB.

Answer: C


NEW QUESTION # 82
A company ingests a large set of sensor data in nested JSON format from different sources and stores it in an Amazon S3 bucket. The sensor data must be joined with performance data currently stored in an Amazon Redshift cluster.
A business analyst with basic SQL skills must build dashboards and analyze this data in Amazon QuickSight. A data engineer needs to build a solution to prepare the data for use by the business analyst. The data engineer does not know the structure of the JSON file. The company requires a solution with the least possible implementation effort.
Which combination of steps will create a solution that meets these requirements? (Select THREE.)

  • A. Use an AWS Glue ETL job with the Regionalize class to un-nest the data and write to Amazon Redshift tables.
  • B. Use an AWS Glue ETL job to convert the data into Apache Parquet format and write to Amazon S3.
  • C. Use an AWS Glue ETL job with the ApplyMapping class to un-nest the data and write to Amazon Redshift tables.
  • D. Use QuickSight to create an Amazon Athena data source to read the Apache Parquet files in Amazon S3.
  • E. Use QuickSight to create an Amazon Redshift data source to read the native Amazon Redshift tables.
  • F. Use an AWS Glue crawler to catalog the data.

Answer: A,E,F


NEW QUESTION # 83
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. Decrease the number of Amazon ES data nodes.
  • B. Increase the memory of the Amazon ES master 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


NEW QUESTION # 84
......


The AWS Certified Data Analytics - Specialty (DAS-C01) exam is designed to test the candidate's knowledge and skills on various AWS services related to data analytics. These services include Amazon S3, Amazon Redshift, Amazon EMR, Amazon Kinesis, Amazon Athena, and Amazon QuickSight. DAS-C01 exam also covers topics such as data governance, data quality, and compliance.

 

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