Dec-2023 FREE Amazon AWS-Certified-Data-Analytics-Specialty PRACTICE QUESTIONS AND ANSWERS UPDATES [Q23-Q41]

Dec-2023 FREE Amazon AWS-Certified-Data-Analytics-Specialty PRACTICE QUESTIONS AND ANSWERS UPDATES

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The AWS Certified Data Analytics - Specialty (DAS-C01) exam is an ideal certification for professionals who want to demonstrate their expertise in data analytics on AWS. AWS-Certified-Data-Analytics-Specialty exam validates the candidate's skills in designing, building, and maintaining analytics solutions on AWS. It is a great way to showcase your knowledge and understanding of AWS data analytics services, data visualization, machine learning, and data warehousing.

The DAS-C01 certification exam is an excellent way for professionals to demonstrate their expertise in data analytics on the AWS platform. AWS Certified Data Analytics - Specialty (DAS-C01) Exam certification can help you stand out in a crowded job market and increase your earning potential. Additionally, earning the DAS-C01 certification can provide you with a solid foundation for pursuing advanced certifications in data analytics, such as the AWS Certified Big Data - Specialty certification.

 

NEW QUESTION # 23
A company is streaming its high-volume billing data (100 MBps) to Amazon Kinesis Data Streams. A data analyst partitioned the data on account_id to ensure that all records belonging to an account go to the same Kinesis shard and order is maintained. While building a custom consumer using the Kinesis Java SDK, the data analyst notices that, sometimes, the messages arrive out of order for account_id. Upon further investigation, the data analyst discovers the messages that are out of order seem to be arriving from different shards for the same account_id and are seen when a stream resize runs.
What is an explanation for this behavior and what is the solution?

• A. The consumer is not processing the parent shard completely before processing the child shards after a stream resize. The data analyst should process the parent shard completely first before processing the child shards.
• B. The hash key generation process for the records is not working correctly. The data analyst should generate an explicit hash key on the producer side so the records are directed to the appropriate shard accurately.
• C. The records are not being received by Kinesis Data Streams in order. The producer should use the PutRecords API call instead of the PutRecord API call with the SequenceNumberForOrdering parameter.
• D. There are multiple shards in a stream and order needs to be maintained in the shard. The data analyst needs to make sure there is only a single shard in the stream and no stream resize runs.

Answer: D

NEW QUESTION # 24
A mortgage company has a microservice for accepting payments. This microservice uses the Amazon DynamoDB encryption client with AWS KMS managed keys to encrypt the sensitive data before writing the data to DynamoDB. The finance team should be able to load this data into Amazon Redshift and aggregate the values within the sensitive fields. The Amazon Redshift cluster is shared with other data analysts from different business units.
Which steps should a data analyst take to accomplish this task efficiently and securely?

• A. Create an Amazon EMR cluster with an EMR_EC2_DefaultRole role that has access to the KMS key. Create Apache Hive tables that reference the data stored in DynamoDB and the finance table in Amazon Redshift. In Hive, select the data from DynamoDB and then insert the output to the finance table in Amazon Redshift.
• B. Create an Amazon EMR cluster. Create Apache Hive tables that reference the data stored in DynamoDB. Insert the output to the restricted Amazon S3 bucket for the finance team. Use the COPY command with the IAM role that has access to the KMS key to load the data from Amazon S3 to the finance table in Amazon Redshift.
• C. Create an AWS Lambda function to process the DynamoDB stream. Decrypt the sensitive data using the same KMS key. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command to load the data from Amazon S3 to the finance table.
• D. Create an AWS Lambda function to process the DynamoDB stream. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command with the IAM role that has access to the KMS key to load the data from S3 to the finance table.

Answer: D

NEW QUESTION # 25
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 file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• B. 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.
• C. 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.
• 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: B

NEW QUESTION # 26
A company's data analyst needs to ensure that queries executed in Amazon Athena cannot scan more than a prescribed amount of data for cost control purposes. Queries that exceed the prescribed threshold must be canceled immediately.
What should the data analyst do to achieve this?

• A. Configure Athena to invoke an AWS Lambda function that terminates queries when the prescribed threshold is crossed.
• B. Enforce the prescribed threshold on all Amazon S3 bucket policies
• C. For each workgroup, set the workgroup-wide data usage control limit to the prescribed threshold.
• D. For each workgroup, set the control limit for each query to the prescribed threshold.

Answer: D

Explanation:
Explanation
https://docs.aws.amazon.com/athena/latest/ug/manage-queries-control-costs-with-workgroups.html

NEW QUESTION # 27
A company has a marketing department and a finance department. The departments are storing data in Amazon S3 in their own AWS accounts in AWS Organizations. Both departments use AWS Lake Formation to catalog and secure their data. The departments have some databases and tables that share common names.
The marketing department needs to securely access some tables from the finance department.
Which two steps are required for this process? (Choose two.)

• A. The marketing department creates an IAM role that has permissions to the Lake Formation tables.
• B. The finance department creates cross-account IAM permissions to the table for the marketing department role.
• C. The finance department grants Lake Formation permissions for the tables to the external account for the marketing department.

Answer: B,C

Explanation:
Explanation
Granting Lake Formation Permissions
Creating an IAM role (AWS CLI)

NEW QUESTION # 28
A company has an application that ingests streaming dat
a. The company needs to analyze this stream over a 5-minute timeframe to evaluate the stream for anomalies with Random Cut Forest (RCF) and summarize the current count of status codes. The source and summarized data should be persisted for future use.
Which approach would enable the desired outcome while keeping data persistence costs low?

• A. Ingest the data stream with Amazon Kinesis Data Streams. Have an AWS Lambda consumer evaluate the stream, collect the number status codes, and evaluate the data against a previously trained RCF model. Persist the source and results as a time series to Amazon DynamoDB.
• B. Ingest the data stream with Amazon Kinesis Data Streams. Have a Kinesis Data Analytics application evaluate the stream over a 5-minute window using the RCF function and summarize the count of status codes. Persist the source and results to Amazon S3 through output delivery to Kinesis Data Firehouse.
• C. Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 1 minute or 1 MB in Amazon S3. Ensure Amazon S3 triggers an event to invoke an AWS Lambda consumer that evaluates the batch data, collects the number status codes, and evaluates the data against a previously trained RCF model. Persist the source and results as a time series to Amazon DynamoDB.
• D. Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 5 minutes or 1 MB into Amazon S3. Have a Kinesis Data Analytics application evaluate the stream over a 1-minute window using the RCF function and summarize the count of status codes. Persist the results to Amazon S3 through a Kinesis Data Analytics output to an AWS Lambda integration.

Answer: B

NEW QUESTION # 29
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. Upload the individual files to Amazon S3 and run the COPY command as soon as the files become available.
• C. 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.
• D. 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.

Answer: D

NEW QUESTION # 30
A mortgage company has a microservice for accepting payments. This microservice uses the Amazon DynamoDB encryption client with AWS KMS managed keys to encrypt the sensitive data before writing the data to DynamoDB. The finance team should be able to load this data into Amazon Redshift and aggregate the values within the sensitive fields. The Amazon Redshift cluster is shared with other data analysts from different business units.
Which steps should a data analyst take to accomplish this task efficiently and securely?

• A. Create an Amazon EMR cluster. Create Apache Hive tables that reference the data stored in DynamoDB. Insert the output to the restricted Amazon S3 bucket for the finance team. Use the COPY command with the IAM role that has access to the KMS key to load the data from Amazon S3 to the finance table in Amazon Redshift.
• B. Create an AWS Lambda function to process the DynamoDB stream. Decrypt the sensitive data using the same KMS key. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command to load the data from Amazon S3 to the finance table.
• C. Create an AWS Lambda function to process the DynamoDB stream. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command with the IAM role that has access to the KMS key to load the data from S3 to the finance table.
• D. Create an Amazon EMR cluster with an EMR_EC2_DefaultRole role that has access to the KMS key.
  Create Apache Hive tables that reference the data stored in DynamoDB and the finance table in Amazon Redshift. In Hive, select the data from DynamoDB and then insert the output to the finance table in Amazon Redshift.

Answer: C

NEW QUESTION # 31
A gaming company is collecting cllckstream data into multiple Amazon Kinesis data streams. The company uses Amazon Kinesis Data Firehose delivery streams to store the data in JSON format in Amazon S3 Data scientists use Amazon Athena to query the most recent data and derive business insights. The company wants to reduce its Athena costs without having to recreate the data pipeline. The company prefers a solution that will require less management effort Which set of actions can the data scientists take immediately to reduce costs?

• A. Change the Kinesis Data Firehose output format to Apache Parquet Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size For the existing data, run an AWS Glue ETL job to combine and convert small JSON files to large Parquet files and add the YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table.
• B. Create an Apache Spark Job that combines and converts JSON files to Apache Parquet files Launch an Amazon EMR ephemeral cluster daily to run the Spark job to create new Parquet files in a different S3 location Use ALTER TABLE SET LOCATION to reflect the new S3 location on the existing Athena table.
• C. Create a Kinesis data stream as a delivery target for Kinesis Data Firehose Run Apache Flink on Amazon Kinesis Data Analytics on the stream to read the streaming data, aggregate ikand save it to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table
• D. Integrate an AWS Lambda function with Kinesis Data Firehose to convert source records to Apache Parquet and write them to Amazon S3 In parallel, run an AWS Glue ETL job to combine and convert existing JSON files to large Parquet files Create a custom S3 object YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table.

Answer: D

NEW QUESTION # 32
A financial company uses Apache Hive on Amazon EMR for ad-hoc queries. Users are complaining of sluggish performance.
A data analyst notes the following:
Approximately 90% of queries are submitted 1 hour after the market opens.
Hadoop Distributed File System (HDFS) utilization never exceeds 10%.
Which solution would help address the performance issues?

• A. Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance groups based on the CloudWatch CapacityRemainingGB metric.
• B. Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch YARNMemoryAvailablePercentage metric.
• C. Create instance 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.
• 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 CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB metric.

Answer: C

Explanation:
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-instances-guidelines.html

NEW QUESTION # 33
A smart home automation company must efficiently ingest and process messages from various connected devices and sensors. The majority of these messages are comprised of a large number of small files. These messages are ingested using Amazon Kinesis Data Streams and sent to Amazon S3 using a Kinesis data stream consumer application. The Amazon S3 message data is then passed through a processing pipeline built on Amazon EMR running scheduled PySpark jobs.
The data platform team manages data processing and is concerned about the efficiency and cost of downstream data processing. They want to continue to use PySpark.
Which solution improves the efficiency of the data processing jobs and is well architected?

• A. Set up AWS Glue Python jobs to merge the small data files in Amazon S3 into larger files and transform them to Apache Parquet format. Migrate the downstream PySpark jobs from Amazon EMR to AWS Glue.
• B. Set up an AWS Lambda function with a Python runtime environment. Process individual Kinesis data stream messages from the connected devices and sensors using Lambda.
• C. Launch an Amazon Redshift cluster. Copy the collected data from Amazon S3 to Amazon Redshift and move the data processing jobs from Amazon EMR to Amazon Redshift.
• D. Send the sensor and devices data directly to a Kinesis Data Firehose delivery stream to send the data to Amazon S3 with Apache Parquet record format conversion enabled. Use Amazon EMR running PySpark to process the data in Amazon S3.

Answer: A

Explanation:
Explanation
https://aws.amazon.com/it/about-aws/whats-new/2020/04/aws-glue-now-supports-serverless-streaming-etl/

NEW QUESTION # 34
A company has several Amazon EC2 instances sitting behind an Application Load Balancer (ALB) The company wants its IT Infrastructure team to analyze the IP addresses coming into the company's ALB The ALB is configured to store access logs in Amazon S3 The access logs create about 1 TB of data each day, and access to the data will be infrequent The company needs a solution that is scalable, cost-effective and has minimal maintenance requirements Which solution meets these requirements?

• A. Use Amazon EMR and Apache Hive to query the S3 data
• B. Use Amazon Redshift Spectrum to query the S3 data
• C. Use Amazon Athena to query the S3 data
• D. Copy the data into Amazon Redshift and query the data

Answer: B

NEW QUESTION # 35
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 AppFlow to ingest Amazon Connect data and Amazon Kinesis Data Firehose to ingest Salesforce data.
• B. Use Amazon Kinesis Data Firehose to ingest Amazon Connect data and Amazon Kinesis Data Streams to ingest Salesforce data.
• C. Use Amazon Kinesis Data Firehose to ingest Amazon Connect data and Amazon AppFlow to ingest Salesforce data.
• D. Use Amazon Kinesis Data Streams to ingest Amazon Connect data and Amazon AppFlow to ingest Salesforce data.

Answer: B

NEW QUESTION # 36
A data analyst is designing a solution to interactively query datasets with SQL using a JDBC connection.
Users will join data stored in Amazon S3 in Apache ORC format with data stored in Amazon Elasticsearch Service (Amazon ES) and Amazon Aurora MySQL.
Which solution will provide the MOST up-to-date results?

• A. Use Amazon DMS to stream data from Amazon ES and Aurora MySQL to Amazon Redshift. Query the data with Amazon Redshift.
• B. Query all the datasets in place with Apache Spark SQL running on an AWS Glue developer endpoint.
• C. Use AWS Glue jobs to ETL data from Amazon ES and Aurora MySQL to Amazon S3. Query the data with Amazon Athena.
• D. Query all the datasets in place with Apache Presto running on Amazon EMR.

Answer: B

NEW QUESTION # 37
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. 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 Catalog. Allow data analysts to use Amazon Athena to submit custom queries.
• B. 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.
• C. Store the metadata and the Amazon S3 location of the image file in an Amazon Redshift table. Allow the data analysts to run ad-hoc queries on the table.
• D. For each image, use object tags to add the metadata. Use Amazon S3 Select to retrieve the files based on the applicant name and application date.

Answer: D

NEW QUESTION # 38
A data analyst is designing a solution to interactively query datasets with SQL using a JDBC connection. Users will join data stored in Amazon S3 in Apache ORC format with data stored in Amazon Elasticsearch Service (Amazon ES) and Amazon Aurora MySQL.
Which solution will provide the MOST up-to-date results?

• A. Use Amazon DMS to stream data from Amazon ES and Aurora MySQL to Amazon Redshift. Query the data with Amazon Redshift.
• B. Query all the datasets in place with Apache Spark SQL running on an AWS Glue developer endpoint.
• C. Use AWS Glue jobs to ETL data from Amazon ES and Aurora MySQL to Amazon S3. Query the data with Amazon Athena.
• D. Query all the datasets in place with Apache Presto running on Amazon EMR.

Answer: B

NEW QUESTION # 39
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 data. The Lambda function will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon S3.
• B. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon S3 to trigger an event for AWS Lambda to process. The Lambda function will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon DynamoDB.
• C. Send the data to Amazon Kinesis Data Streams and configure an Amazon Kinesis Analytics for Java application as the consumer. The application will consume the data and process it to identify potential playback issues. 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 consumer. The application will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon DynamoDB.

Answer: C

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

NEW QUESTION # 40
A company hosts an Apache Flink application on premises. The application processes data from several Apache Kafka clusters. The data originates from a variety of sources, such as web applications mobile apps and operational databases The company has migrated some of these sources to AWS and now wants to migrate the Flink application. The company must ensure that data that resides in databases within the VPC does not traverse the internet The application must be able to process all the data that comes from the company's AWS solution, on-premises resources and the public internet Which solution will meet these requirements with the LEAST operational overhead?

• A. Create an Amazon Kinesis Data Analytics application by uploading the compiled Flink jar file Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in the company's VPC to collect data that comes from applications and databases within the VPC Use Amazon Kinesis Data Streams to collect data that comes from the public internet Configure the Kinesis Data Analytics application to have sources from Kinesis Data Streams. Amazon MSK and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect
• B. Create an Amazon Kinesis Data Analytics application by uploading the compiled Flink jar file Use Amazon Kinesis Data Streams to collect data that comes from applications and databases within the VPC and the public internet Configure the Kinesis Data Analytics application to have sources from Kinesis Data Streams and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect
• C. Implement Flink on Amazon EC2 within the company's VPC Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in the VPC to collect data that comes from applications and databases within the VPC Use Amazon Kinesis Data Streams to collect data that comes from the public internet Configure Flink to have sources from Kinesis Data Streams Amazon MSK and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect
• D. Implement Flink on Amazon EC2 within the company's VPC Use Amazon Kinesis Data Streams to collect data that comes from applications and databases within the VPC and the public internet Configure Flink to have sources from Kinesis Data Streams and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect

Answer: A

NEW QUESTION # 41
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