AWS Certified Data Analytics Specialty Practice Exam Part 2
Notes: Hi all, AWS Data Analytics Specialty Practice Exam Part 2 will familiarize you with types of questions you may encounter on the certification exam and help you determine your readiness or if you need more preparation and/or experience. Successful completion of the practice exam does not guarantee you will pass the certification exam as the actual exam is longer and covers a wider range of topics. We highly recommend you should take AWS Data Analytics Specialty DAS-C01 Actual Exam Version because it include real questions and highlighted answers are collected in our exam. It will help you pass exam in easier way.
21. 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 bucket. Add the additional IAM roles to the cluster’s EMR role for the EC2 trust policy. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
B. 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 bucket. Add the service role for the EMR cluster EC2 instances to the trust policies for the additional IAM roles. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
C. 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 bucket. Add the service role for the EMR cluster EC2 instances to the trust policies for the additional IAM roles. 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 full access to Amazon S3. Create three additional IAM roles, each granting access to each team’s specific bucket. Add the service role for the EMR cluster EC2 instances to the trust policies for the base IAM roles. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
22. A company is planning to create a data lake in Amazon S3. The company wants to create tiered storage based on access patterns and cost objectives. The solution must include support for JDBC connections from legacy clients, metadata management that allows federation for access control, and batch-based ETL using PySpark and Scala Operational management should be limited. Which combination of components can meet these requirements? (Choose three.)
A. AWS Glue Data Catalog for metadata management.
B. Amazon EMR with Apache Spark for ETL.
C. AWS Glue for Scala-based ETL.
D. Amazon EMR with Apache Hive for JDBC clients.
E. Amazon Athena for querying data in Amazon S3 using JDBC drivers.
F. Amazon EMR with Apache Hive, using an Amazon RDS with MySQL-compatible backed metastore.
23. A company wants to optimize the cost of its data and analytics platform. The company is ingesting a number of .csv and JSON files in Amazon S3 from various data sources. Incoming data is expected to be 50 GB each day. The company is using Amazon Athena to query the raw data in Amazon S3 directly. Most queries aggregate data from the past 12 months, and data that is older than 5 years is infrequently queried. The typical query scans about 500 MB of data and is expected to return results in less than 1 minute. The raw data must be retained indefinitely for compliance requirements. Which solution meets the company’s requirements?
A. Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after object creation. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after object creation.
B. Use an AWS Glue ETL job to partition and convert the data into a row-based data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after object creation. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after object creation.
C. Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accessed. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after the last date the object was accessed.
D. Use an AWS Glue ETL job to partition and convert the data into a row-based data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accessed. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after the last date the object was accessed.
24. A regional energy company collects voltage data from sensors attached to buildings. To address any known dangerous conditions, the company wants to be alerted when a sequence of two voltage drops is detected within 10 minutes of a voltage spike at the same building. It is important to ensure that all messages are delivered as quickly as possible. The system must be fully managed and highly available. The company also needs a solution that will automatically scale up as it covers additional cities with this monitoring feature. The alerting system is subscribed to an Amazon SNS topic for remediation. Which solution meets these requirements?
A. Create an Amazon Managed Streaming for Kafka cluster to ingest the data, and use an Apache Spark Streaming with Apache Kafka consumer API in an automatically scaled Amazon EMR cluster to process the incoming data. Use the Spark Streaming application to detect the known event sequence and send the SNS message.
B. Create a REST-based web service using Amazon API Gateway in front of an AWS Lambda function. Create an Amazon RDS for PostgreSQL database with sufficient Provisioned IOPS (PIOPS). In the Lambda function, store incoming events in the RDS database and query the latest data to detect the known event sequence and send the SNS message.
C. Create an Amazon Kinesis Data Firehose delivery stream to capture the incoming sensor data. Use an AWS Lambda transformation function to detect the known event sequence and send the SNS message.
D. Create an Amazon Kinesis data stream to capture the incoming sensor data and create another stream for alert messages. Set up AWS Application Auto Scaling on both. Create a Kinesis Data Analytics for Java application to detect the known event sequence, and add a message to the message stream. Configure an AWS Lambda function to poll the message stream and publish to the SNS topic.
25. 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 triggers 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 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 .
C. 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 .
D. 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.
26. An ecommerce company stores customer purchase data in Amazon RDS. The company wants a solution to store and analyze historical data. The most recent 6 months of data will be queried frequently for analytics workloads. This data is several terabytes large. Once a month, historical data for the last 5 years must be accessible and will be joined with the more recent data. The company wants to optimize performance and cost. Which storage solution will meet these requirements?
A. Create a read replica of the RDS database to store the most recent 6 months of data. Copy the historical data into Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3 and Amazon RDS. Run historical queries using Amazon Athena.
B. Use an ETL tool to incrementally load the most recent 6 months of data into an Amazon Redshift cluster. Run more frequent queries against this cluster. Create a read replica of the RDS database to run queries on the historical data.
C. Incrementally copy data from Amazon RDS to Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3. Use Amazon Athena to query the data.
D. Incrementally copy data from Amazon RDS to Amazon S3. Load and store the most recent 6 months of data in Amazon Redshift. Configure an Amazon Redshift Spectrum table to connect to all historical data.
27. 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 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.
C. 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.
D. 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.
28. A company wants to use an automatic machine learning (ML) Random Cut Forest (RCF) algorithm to visualize complex real-world scenarios, such as detecting seasonality and trends, excluding outers, and imputing missing values. The team working on this project is non-technical and is looking for an out-of-the-box solution that will require the LEAST amount of management overhead. Which solution will meet these requirements?
A. Use an AWS Glue ML transform to create a forecast and then use Amazon QuickSight to visualize the data.
B. Use Amazon QuickSight to visualize the data and then use ML-powered forecasting to forecast the key business metrics.
C. Use a pre-build ML AMI from the AWS Marketplace to create forecasts and then use Amazon QuickSight to visualize the data.
D. Use calculated fields to create a new forecast and then use Amazon QuickSight to visualize the data.
29. A retail company’s data analytics team recently created multiple product sales analysis dashboards for the average selling price per product using Amazon QuickSight. The dashboards were created from .csv files uploaded to Amazon S3. The team is now planning to share the dashboards with the respective external product owners by creating individual users in Amazon QuickSight. For compliance and governance reasons, restricting access is a key requirement. The product owners should view only their respective product analysis in the dashboard reports. Which approach should the data analytics team take to allow product owners to view only their products in the dashboard?
A. Separate the data by product and use S3 bucket policies for authorization.
B. Separate the data by product and use IAM policies for authorization.
C. Create a manifest file with row-level security.
D. Create dataset rules with row-level security.
30. A company has developed an Apache Hive script to batch process data started in Amazon S3. The script needs to run once every day and store the output in Amazon S3. The company tested the script, and it completes within 30 minutes on a small local three-node cluster.
Which solution is the MOST cost-effective for scheduling and executing the script?
A. Create an AWS Lambda function to spin up an Amazon EMR cluster with a Hive execution step. Set KeepJobFlowAliveWhenNoSteps to false and disable the termination protection flag. Use Amazon CloudWatch Events to schedule the Lambda function to run daily.
B. Use the AWS Management Console to spin up an Amazon EMR cluster with Python Hue. Hive, and Apache Oozie. Set the termination protection flag to true and use Spot Instances for the core nodes of the cluster. Configure an Oozie workflow in the cluster to invoke the Hive script daily.
C. Create an AWS Glue job with the Hive script to perform the batch operation. Configure the job to run once a day using a time-based schedule.
D. Use AWS Lambda layers and load the Hive runtime to AWS Lambda and copy the Hive script. Schedule the Lambda function to run daily by creating a workflow using AWS Step Functions.
31. 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. Compress .csv files and use an INSERT statement to ingest data into Amazon Redshift.
B. Split large .csv files, then use a COPY command to load data into Amazon Redshift.
C. Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.
D. Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.
32. 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. Enable concurrency scaling in the workload management (WLM) queue.
B. Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.
C. Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.
D. Use a snapshot, restore, and resize operation. Switch to the new target cluster.
33. A company analyzes its data in an Amazon Redshift data warehouse, which currently has a cluster of three dense storage nodes. Due to a recent business acquisition, the company needs to load an additional 4 TB of user data into Amazon Redshift. The engineering team will combine all the user data and apply complex calculations that require I/O intensive resources. The company needs to adjust the cluster’s capacity to support the change in analytical and storage requirements. Which solution meets these requirements?
A. Resize the cluster using elastic resize with dense compute nodes.
B. Resize the cluster using classic resize with dense compute nodes.
C. Resize the cluster using elastic resize with dense storage nodes.
D. Resize the cluster using classic resize with dense storage nodes.
34. A company stores its sales and marketing data that includes personally identifiable information (PII) in Amazon S3. The company allows its analysts to launch their own Amazon EMR cluster and run analytics reports with the data. To meet compliance requirements, the company must ensure the data is not publicly accessible throughout this process. A data engineer has secured Amazon S3 but must ensure the individual EMR clusters created by the analysts are not exposed to the public internet. Which solution should the data engineer meet this compliance requirement with the LEAST amount of effort?
A. Create an EMR security configuration and ensure the security configuration is associated with the EMR clusters when they are created.
B. Check the security group of the EMR clusters regularly to ensure it does not allow inbound traffic from IPv4 0.0.0.0/0 or IPv6 ::/0.
C. Enable the block public access setting for Amazon EMR at the account level before any EMR cluster is created.
D. Use AWS WAF to block public internet access to the EMR clusters across the board.
35. 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.
36. 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 Aurora MySQL
B. Amazon Redshift
C. Amazon Neptune
D. Amazon Elasticsearch
37. A technology company is creating a dashboard that will visualize and analyze time-sensitive data. The data will come in through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support near-real-time data. Which visualization solution will meet these requirements?
A. Select Amazon Elasticsearch Service (Amazon ES) as the endpoint for Kinesis Data Firehose. Set up a Kibana dashboard using the data in Amazon ES with the desired analyses and visualizations.
B. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Read data into an Amazon SageMaker Jupyter notebook and carry out the desired analyses and visualizations.
C. Select Amazon Redshift as the endpoint for Kinesis Data Firehose. Connect Amazon QuickSight with SPICE to Amazon Redshift to create the desired analyses and visualizations.
D. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Use AWS Glue to catalog the data and Amazon Athena to query it. Connect Amazon QuickSight with SPICE to Athena to create the desired analyses and visualizations.
38. 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 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.
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 CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance groups based on the CloudWatch CapacityRemainingGB metric.
D. 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.
39. 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.
40. A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run. Which approach would allow the developers to solve the issue with minimal coding effort?
A. Have the ETL jobs read the data from Amazon S3 using a DataFrame.
B. Enable job bookmarks on the AWS Glue jobs.
C. Create custom logic on the ETL jobs to track the processed S3 objects.
D. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.
