Positive Aspects of Valid Dumps AWS-Certified-Data-Analytics-Specialty Exam Dumps! [Jul-2023]

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The DAS-C01 exam consists of 65 multiple-choice and multiple-response questions that candidates must complete within 180 minutes. The exam is available in English, Japanese, Korean, and Simplified Chinese. To pass the exam, candidates must demonstrate their proficiency in areas such as data engineering, data analysis, visualization, and machine learning. They must also be familiar with a wide range of AWS services, including Amazon S3, Amazon Redshift, Amazon Kinesis, Amazon EMR, and Amazon QuickSight.

The AWS Certified Data Analytics - Specialty (DAS-C01) Exam is a certification exam offered by Amazon Web Services (AWS) for candidates who want to demonstrate their skills and expertise in the field of data analytics. This exam is designed for individuals who have a strong understanding of data analysis concepts and tools, and who are interested in working with AWS services to build scalable and efficient data solutions.

The AWS Certified Data Analytics - Specialty exam covers a range of topics, including data collection, storage, processing, and visualization. It also includes questions on data security, compliance, and governance. Candidates are tested on their ability to use AWS services such as Amazon S3, Amazon Redshift, Amazon EMR, Amazon Kinesis, and Amazon QuickSight to build scalable, efficient, and cost-effective analytics solutions.

 

NEW QUESTION # 81
A transport company wants to track vehicular movements by capturing geolocation records. The records are
10 B in size and up to 10,000 records are captured each second. Data transmission delays of a few minutes are acceptable, considering unreliable network conditions. The transport company decided to use Amazon Kinesis Data Streams to ingest the data. The company is looking for a reliable mechanism to send data to Kinesis Data Streams while maximizing the throughput efficiency of the Kinesis shards.
Which solution will meet the company's requirements?

• A. Kinesis Data Firehose
• B. Kinesis SDK
• C. Kinesis Producer Library (KPL)
• D. Kinesis Agent

Answer: C

NEW QUESTION # 82
An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an Amazon S3 bucket and write the data to a different S3 bucket in Apache Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error message in the History tab on the AWS Glue console: "Command Failed with Exit Code 1." Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe threshold of 50% usage quickly and reaches 90-95% soon after. The average memory usage across all executors continues to be less than 4%.
The data engineer also notices the following error while examining the related Amazon CloudWatch Logs.
What should the data engineer do to solve the failure in the MOST cost-effective way?

• A. Modify the AWS Glue ETL code to use the 'groupFiles': 'inPartition' feature.
• B. Change the worker type from Standard to G.2X.
• C. Modify maximum capacity to increase the total maximum data processing units (DPUs) used.
• D. Increase the fetch size setting by using AWS Glue dynamics frame.

Answer: A

Explanation:
https://docs.aws.amazon.com/glue/latest/dg/monitor-profile-debug-oom-abnormalities.html#monitor-debug-oom-fix

NEW QUESTION # 83
A team of data scientists plans to analyze market trend data for their company's new investment strategy. The trend data comes from five different data sources in large volumes. The team wants to utilize Amazon Kinesis to support their use case. The team uses SQL-like queries to analyze trends and wants to send notifications based on certain significant patterns in the trends. Additionally, the data scientists want to save the data to Amazon S3 for archival and historical re-processing, and use AWS managed services wherever possible. The team wants to implement the lowest-cost solution.
Which solution meets these requirements?

• A. Publish data to two Kinesis data streams. Deploy Kinesis Data Analytics to the first stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
• B. Publish data to one Kinesis data stream. Deploy Kinesis Data Analytic to the stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
• C. Publish data to two Kinesis data streams. Deploy a custom application using the Kinesis Client Library (KCL) to the first stream for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
• D. Publish data to one Kinesis data stream. Deploy a custom application using the Kinesis Client Library (KCL) for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.

Answer: B

NEW QUESTION # 84
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 KMS. Create an AWS Lambda function to encrypt and decrypt the data. Set the KMS key ID in the function's environment variables.
• B. Enable server-side encryption on the Kinesis data stream using the default KMS key for Kinesis Data Streams.
• C. Create a customer master key (CMK) in AWS KMS. Assign the CMK an alias. Enable server-side encryption on the Kinesis data stream using the CMK alias as the KMS master key.
• D. Create a customer master key (CMK) in AWS KMS. Assign the CMK an alias. Use the AWS Encryption SDK, providing it with the key alias to encrypt and decrypt the data.

Answer: C

NEW QUESTION # 85
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 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.
• B. 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.
• 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 executor-cores job parameter.

Answer: A

NEW QUESTION # 86
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. 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 Zones. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
• B. Use Spot Instances for core and task nodes and a Reserved Instance for the EMR master node. Configure the EMR cluster with multiple master nodes. Schedule automated snapshots using Amazon EventBridge.
• C. 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 nodes. Create a secondary EMR HBase read- replica cluster in a separate Availability Zone. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
• D. Store the data on an EMR File System (EMRFS) instead of HDFS. Enable EMRFS consistent view. Create an EMR HBase cluster with multiple master nodes. Point the HBase root directory to an Amazon S3 bucket.

Answer: C

NEW QUESTION # 87
An online retail company is migrating its reporting system to AWS. The company's legacy system runs data processing on online transactions using a complex series of nested Apache Hive queries. Transactional data is exported from the online system to the reporting system several times a day. Schemas in the files are stable between updates.
A data analyst wants to quickly migrate the data processing to AWS, so any code changes should be minimized. To keep storage costs low, the data analyst decides to store the data in Amazon S3. It is vital that the data from the reports and associated analytics is completely up to date based on the data in Amazon S3.
Which solution meets these requirements?

• A. Create an Amazon Athena table with CREATE TABLE AS SELECT (CTAS) to ensure data is refreshed from underlying queries against the raw dataset. Create an AWS Glue Data Catalog to manage the Hive metadata over the CTAS table. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
• B. Create an AWS Glue Data Catalog to manage the Hive metadata. Create an AWS Glue crawler over Amazon S3 that runs when data is refreshed to ensure that data changes are updated. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
• C. Create an AWS Glue Data Catalog to manage the Hive metadata. Create an Amazon EMR cluster with consistent view enabled. Run emrfs sync before each analytics step to ensure data changes are updated.
  Create an EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
• D. Use an S3 Select query to ensure that the data is properly updated. Create an AWS Glue Data Catalog to manage the Hive metadata over the S3 Select table. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

Answer: B

NEW QUESTION # 88
A manufacturing company has many loT devices in different facilities across the world The company is using Amazon Kinesis Data Streams to collect the data from the devices The company's operations team has started to observe many WnteThroughputExceeded exceptions The operations team determines that the reason is the number of records that are being written to certain shards The data contains device ID capture date measurement type, measurement value and facility ID The facility ID is used as the partition key Which action will resolve this issue?

• A. Increase the number of shards
• B. Change the partition key from facility ID to capture date
• C. Archive the data on the producers' side
• D. Change the partition key from facility ID to a randomly generated key

Answer: A

NEW QUESTION # 89
A US-based sneaker retail company launched its global website. All the transaction data is stored in Amazon RDS and curated historic transaction data is stored in Amazon Redshift in the us-east-1 Region. The business intelligence (BI) team wants to enhance the user experience by providing a dashboard for sneaker trends.
The BI team decides to use Amazon QuickSight to render the website dashboards. During development, a team in Japan provisioned Amazon QuickSight in ap-northeast-1. The team is having difficulty connecting Amazon QuickSight from ap-northeast-1 to Amazon Redshift in us-east-1.
Which solution will solve this issue and meet the requirements?

• A. Create a VPC endpoint from the Amazon QuickSight VPC to the Amazon Redshift VPC so Amazon QuickSight can access data from Amazon Redshift.
• B. Create an Amazon Redshift endpoint connection string with Region information in the string and use this connection string in Amazon QuickSight to connect to Amazon Redshift.
• C. Create a new security group for Amazon Redshift in us-east-1 with an inbound rule authorizing access from the appropriate IP address range for the Amazon QuickSight servers in ap-northeast-1.
• D. In the Amazon Redshift console, choose to configure cross-Region snapshots and set the destination Region as ap-northeast-1. Restore the Amazon Redshift Cluster from the snapshot and connect to Amazon QuickSight launched in ap-northeast-1.

Answer: A

NEW QUESTION # 90
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, one to three predicates, and an action
• B. A unique rule name, a query runtime condition, and an AWS Lambda function to resubmit any failed queries in off hours
• C. A queue name, a unique rule name, and a predicate-based stop condition
• D. A workload name, a unique rule name, and a query runtime-based condition

Answer: A

NEW QUESTION # 91
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 delete the processed objects or data from Amazon S3 after each run.
• B. Create custom logic on the ETL jobs to track the processed S3 objects.
• C. Enable job bookmarks on the AWS Glue jobs.
• D. Have the ETL jobs read the data from Amazon S3 using a DataFrame.

Answer: A

NEW QUESTION # 92
A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which sub-organization is the strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format.
Which approach can provide the visuals that senior leadership requested with the least amount of effort?

• A. Use Amazon QuickSight with Amazon S3 as the data source. Use heat maps as the visual type.
• B. Use Amazon QuickSight with Amazon S3 as the data source. Use pivot tables as the visual type.
• C. Use Amazon QuickSight with Amazon Athena as the data source. Use pivot tables as the visual type.
• D. Use Amazon QuickSight with Amazon Athena as the data source. Use heat maps as the visual type.

Answer: C

NEW QUESTION # 93
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 the files from .csv to a single large Apache ORC file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• B. Use AWS Glue to convert all the files from .csv to a single large Apache Parquet file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• C. Use AWS Glue to convert the files from .csv to Apache Parquet to create 20 Parquet files. COPY the files into Amazon Redshift and query the files with Athena from Amazon S3.
• D. Use Amazon EMR to convert each .csv file to Apache Avro. COPY the files into Amazon Redshift and query the file with Athena from Amazon S3.

Answer: C

NEW QUESTION # 94
A company's marketing team has asked for help in identifying a high performing long-term storage service for their data based on the following requirements:
* The data size is approximately 32 TB uncompressed.
* There is a low volume of single-row inserts each day.
* There is a high volume of aggregation queries each day.
* Multiple complex joins are performed.
* The queries typically involve a small subset of the columns in a table.
Which storage service will provide the MOST performant solution?

• A. Amazon Redshift
• B. Amazon Elasticsearch
• C. Amazon Neptune
• D. Amazon Aurora MySQL

Answer: A

NEW QUESTION # 95
A company leverages Amazon Athena for ad-hoc queries against data stored in Amazon S3. The company wants to implement additional controls to separate query execution and query history among users, teams, or applications running in the same AWS account to comply with internal security policies.
Which solution meets these requirements?

• A. Create an S3 bucket for each given use case, create an S3 bucket policy that grants permissions to appropriate individual IAM users. and apply the S3 bucket policy to the S3 bucket.
• B. Create an AWS Glue Data Catalog resource policy for each given use case that grants permissions to appropriate individual IAM users, and apply the resource policy to the specific tables used by Athena.
• C. Create an Athena workgroup for each given use case, apply tags to the workgroup, and create an IAM policy using the tags to apply appropriate permissions to the workgroup.
• D. Create an IAM role for each given use case, assign appropriate permissions to the role for the given use case, and add the role to associate the role with Athena.

Answer: D

NEW QUESTION # 96
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 YARNMemoryAvailablePercentage metric.
  Create an automatic scaling policy to scale in the instance groups based on the CloudWatch YARNMemoryAvailablePercentage metric.
• B. 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.
• 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 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.

Answer: B

NEW QUESTION # 97
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 Streams 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 Streams 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 Firehose 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 Firehose to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.

Answer: D

NEW QUESTION # 98
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