[Jan 01, 2024] Fully Updated Free Actual Amazon AWS-Certified-Data-Analytics-Specialty Exam Questions
Free AWS-Certified-Data-Analytics-Specialty Questions for Amazon AWS-Certified-Data-Analytics-Specialty Exam [Jan-2024]
The AWS Certified Data Analytics - Specialty (DAS-C01) certification exam is designed for professionals who want to validate their skills and knowledge in data analytics on the AWS platform. AWS-Certified-Data-Analytics-Specialty exam covers a wide range of topics, including data storage, processing, analysis, visualization, and security. AWS Certified Data Analytics - Specialty (DAS-C01) Exam certification is ideal for professionals who work with large amounts of data and want to enhance their skills in managing and analyzing data on the cloud.
How to Prepare For AWS Certified Data Analytics - Specialty (DAS-C01) Professional Exam
Preparation Guide for AWS Certified Data Analytics - Specialty (DAS-C01) Professional Exam
Introduction for AWS Certified Data Analytics - Specialty (DAS-C01) Professional Exam
Amazon Web Services (AWS) is a subdivision of Amazon producing cloud computing platforms and APIs for individuals, companies, and governments, on a subscription-based model. The cloud computing web services give a variety of basic, complex, technical infrastructure and distributed computing building blocks and instruments. One of these services is Amazon Elastic Compute Cloud (EC2). It enables users to have a virtual cluster of computers, ready all the time, over the Internet. This guide provides a step by step framework of the AWS Certified Data Analytics - Specialty (DAS-C01) Professional certification exam including a broad array of essentials of the test, the exam design, themes, test complexities and readiness techniques, and the intended interest group profile.
Thus, we prepare various AMAZON DAS C01 practice exams and AMAZON DAS C01 practice exams of AWS Accredited Developer proficient inquiries as we understand understudy determinations. Our content, helps candidates' total assessments.
The AWS Certified Data Analytics - Specialty (DAS-C01) examination is designed for people who work in a data analytics-focused position. This certification validates an examinee's thorough knowledge of using AWS services to design, build, secure, and manage analytics solutions that give insight from data. It verifies an examinee's skill to:
- Define AWS data analytics services and know how they integrate
- Describe how AWS data analytics services are utilized in the data lifecycle of collection, storage, processing, and visualization.
The Amazon Web Services Certified Data Analytics - Specialty (DAS-C01) exam is designed for people who possess at least 5 years of practical experience with common data analytics technologies, have a minimum 2 years of hands-on expertise working on AWS and ahve the required knowledge and expertise operating with AWS services to design, build, secure, and maintain analytics solutions. They must effectively exhibit an understanding of the AWS Data Analytics.
The exam proves a candidate's capacity to accomplish the following tasks:
- Develops reliability and confidence by highlighting your capability to design, build, protect, and sustain analytics solutions on AWS that are efficient, and secure
- Describe the significance of the AWS Cloud
- It verifies your expertise in AWS data lakes and analytics services
To prepare for the Amazon DAS-C01 exam, candidates should have a strong foundation in data analytics and AWS services. They should also be familiar with various data storage formats, data processing frameworks, and data visualization tools. Candidates can prepare for AWS-Certified-Data-Analytics-Specialty exam by taking AWS training courses, reading AWS documentation, and practicing with hands-on labs and exercises. By obtaining the Amazon DAS-C01 certification, data analysts and data scientists can demonstrate their proficiency in working with data in the AWS cloud, which can lead to more job opportunities and career advancement in the field of data analytics.
NEW QUESTION # 12
A company has a data lake on AWS that ingests sources of data from multiple business units and uses Amazon Athena for queries. The storage layer is Amazon S3 using the AWS Glue Data Catalog. The company wants to make the data available to its data scientists and business analysts. However, the company first needs to manage data access for Athena based on user roles and responsibilities.
What should the company do to apply these access controls with the LEAST operational overhead?
- A. Define security policy-based rules for the users and applications by role in AWS Identity and Access Management (IAM).
- B. Define security policy-based rules for the users and applications by role in AWS Lake Formation.
- C. Define security policy-based rules for the tables and columns by role in AWS Glue.
- D. Define security policy-based rules for the tables and columns by role in AWS Identity and Access Management (IAM).
Answer: D
NEW QUESTION # 13
A company stores Apache Parquet-formatted files in Amazon S3 The company uses an AWS Glue Data Catalog to store the table metadata and Amazon Athena to query and analyze the data The tables have a large number of partitions The queries are only run on small subsets of data in the table A data analyst adds new time partitions into the table as new data arrives The data analyst has been asked to reduce the query runtime Which solution will provide the MOST reduction in the query runtime?
- A. Convert the Parquet files to the Apache ORC file format. Then attempt to query the data again
- B. Convert the Parquet files to the csv file format..Then attempt to query the data again
- C. Add more partitions to be used over the table. Then filter over two partitions and put all columns in the WHERE clause
- D. Use partition projection to speed up the processing of the partitioned table
Answer: D
NEW QUESTION # 14
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. 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.
- B. 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.
- 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. 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.
Answer: A
Explanation:
https://aws.amazon.com/blogs/machine-learning/automatically-extract-text-and-structured-data-from-documents-with-amazon-textract/
NEW QUESTION # 15
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 Producer Library (KPL)
- B. Kinesis SDK
- C. Kinesis Agent
- D. Kinesis Data Firehose
Answer: A
NEW QUESTION # 16
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 Java. 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 "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. - C. 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.
- D. 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.
Answer: D
NEW QUESTION # 17
A company uses Amazon Redshift as its data warehouse. A new table has columns that contain sensitive data.
The data in the table will eventually be referenced by several existing queries that run many times a day.
A data analyst needs to load 100 billion rows of data into the new table. Before doing so, the data analyst must ensure that only members of the auditing group can read the columns containing sensitive data.
How can the data analyst meet these requirements with the lowest maintenance overhead?
- A. Load all the data into the new table and grant all users read-only permissions to non-sensitive columns.
Attach an IAM policy to the auditing group with explicit ALLOW access to the sensitive data columns. - B. Load all the data into the new table and grant the auditing group permission to read from the table. Load all the data except for the columns containing sensitive data into a second table. Grant the appropriate users read-only permissions to the second table.
- C. Load all the data into the new table and grant the auditing group permission to read from the table.
Create a view of the new table that contains all the columns, except for those considered sensitive, and grant the appropriate users read-only permissions to the table. - D. Load all the data into the new table and grant the auditing group permission to read from the table. Use the GRANT SQL command to allow read-only access to a subset of columns to the appropriate users.
Answer: D
Explanation:
Explanation
https://aws.amazon.com/blogs/big-data/achieve-finer-grained-data-security-with-column-level-access-control-in-
NEW QUESTION # 18
An online retail company with millions of users around the globe wants to improve its ecommerce analytics capabilities. Currently, clickstream data is uploaded directly to Amazon S3 as compressed files. Several times each day, an application running on Amazon EC2 processes the data and makes search options and reports available for visualization by editors and marketers. The company wants to make website clicks and aggregated data available to editors and marketers in minutes to enable them to connect with users more effectively.
Which options will help meet these requirements in the MOST efficient way? (Choose two.)
- A. Use Amazon Elasticsearch Service deployed on Amazon EC2 to aggregate, filter, and process the data.
Refresh content performance dashboards in near-real time. - B. Use Kibana to aggregate, filter, and visualize the data stored in Amazon Elasticsearch Service. Refresh content performance dashboards in near-real time.
- C. Upload clickstream records to Amazon S3 as compressed files. Then use AWS Lambda to send data to Amazon Elasticsearch Service from Amazon S3.
- D. Use Amazon Kinesis Data Firehose to upload compressed and batched clickstream records to Amazon Elasticsearch Service.
- E. Upload clickstream records from Amazon S3 to Amazon Kinesis Data Streams and use a Kinesis Data Streams consumer to send records to Amazon Elasticsearch Service.
Answer: B,D
NEW QUESTION # 19
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 cites 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 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.
- 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 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.
- 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.
Answer: D
NEW QUESTION # 20
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 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.
- B. 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.
- C. 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.
- D. 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.
Answer: A
NEW QUESTION # 21
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 Firehose to stream data to Amazon Redshift. Use Amazon Redshift 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 Streams 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 S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.
Answer: C
NEW QUESTION # 22
A company is migrating its existing on-premises ETL jobs to Amazon EMR. The code consists of a series of jobs written in Java. The company needs to reduce overhead for the system administrators without changing the underlying code. Due to the sensitivity of the data, compliance requires that the company use root device volume encryption on all nodes in the cluster. Corporate standards require that environments be provisioned though AWS CloudFormation when possible.
Which solution satisfies these requirements?
- A. Install open-source Hadoop on Amazon EC2 instances with encrypted root device volumes. Configure the cluster in the CloudFormation template.
- B. Create a custom AMI with encrypted root device volumes. Configure Amazon EMR to use the custom AMI using the CustomAmild property in the CloudFormation template.
- C. Use a CloudFormation template to launch an EMR cluster. In the configuration section of the cluster, define a bootstrap action to encrypt the root device volume of every node.
- D. Use a CloudFormation template to launch an EMR cluster. In the configuration section of the cluster, define a bootstrap action to enable TLS.
Answer: B
NEW QUESTION # 23
A real estate company has a mission-critical application using Apache HBase in Amazon EMR. Amazon EMR is configured with a single master node. The company has over 5 TB of data stored on an Hadoop Distributed File System (HDFS). The company wants a cost-effective solution to make its HBase data highly available.
Which architectural pattern meets company's requirements?
- A. Use Spot Instances for core and task nodes and a Reserved Instance for the EMR master node. Configure the EMR cluster with multiple master nodes. Schedule automated snapshots using Amazon EventBridge.
- B. 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.
- C. 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.
- D. 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.
Answer: D
NEW QUESTION # 24
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 YARNMemoryAvailablePercentage metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch YARNMemoryAvailablePercentage 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 CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB 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 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.
Answer: C
Explanation:
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-instances-guidelines.html
NEW QUESTION # 25
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. Create custom logic on the ETL jobs to track the processed S3 objects.
- B. Have the ETL jobs read the data from Amazon S3 using a DataFrame.
- C. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.
- D. Enable job bookmarks on the AWS Glue jobs.
Answer: C
NEW QUESTION # 26
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: D
NEW QUESTION # 27
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 Redshift as the endpoint for Kinesis Data Firehose. Connect Amazon QuickSight with SPICE to Amazon Redshift to create the desired analyses and visualizations.
- B. 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.
- C. 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.
- 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.
Answer: B
NEW QUESTION # 28
A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as .csv files that are stored in Amazon S3. The company's analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall dat a. The amount of data that is ingested into Amazon S3 has increased substantially over time, and the query latency also has increased.
Which solutions could the company implement to improve query performance? (Choose two.)
- A. Run a daily AWS Glue ETL job to compress the data files by using the .lzo format. Query the compressed data.
- B. Use Athena to extract the data and store it in Apache Parquet format on a daily basis. Query the extracted data.
- C. Run a daily AWS Glue ETL job to compress the data files by using the .gzip format. Query the compressed data.
- D. Run a daily AWS Glue ETL job to convert the data files to Apache Parquet and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data on a daily basis.
- E. Use MySQL Workbench on an Amazon EC2 instance, and connect to Athena by using a JDBC or ODBC connector. Run the query from MySQL Workbench instead of Athena directly.
Answer: B,D
Explanation:
Reference:
https://aws.amazon.com/blogs/big-data/work-with-partitioned-data-in-aws-glue/
NEW QUESTION # 29
A healthcare company uses AWS data and analytics tools to collect, ingest, and store electronic health record (EHR) data about its patients. The raw EHR data is stored in Amazon S3 in JSON format partitioned by hour, day, and year and is updated every hour. The company wants to maintain the data catalog and metadata in an AWS Glue Data Catalog to be able to access the data using Amazon Athena or Amazon Redshift Spectrum for analytics.
When defining tables in the Data Catalog, the company has the following requirements:
Choose the catalog table name and do not rely on the catalog table naming algorithm. Keep the table updated with new partitions loaded in the respective S3 bucket prefixes.
Which solution meets these requirements with minimal effort?
- A. Use the AWS Glue console to manually create a table in the Data Catalog and schedule an AWS Lambda function to update the table partitions hourly.
- B. Use the AWS Glue API CreateTable operation to create a table in the Data Catalog. Create an AWS Glue crawler and specify the table as the source.
- C. Create an Apache Hive catalog in Amazon EMR with the table schema definition in Amazon S3, and update the table partition with a scheduled job. Migrate the Hive catalog to the Data Catalog.
- D. Run an AWS Glue crawler that connects to one or more data stores, determines the data structures, and writes tables in the Data Catalog.
Answer: B
Explanation:
Explanation
Updating Manually Created Data Catalog Tables Using Crawlers: To do this, when you define a crawler, instead of specifying one or more data stores as the source of a crawl, you specify one or more existing Data Catalog tables. The crawler then crawls the data stores specified by the catalog tables. In this case, no new tables are created; instead, your manually created tables are updated.
NEW QUESTION # 30
An Amazon Redshift database contains sensitive user data. Logging is necessary to meet compliance requirements. The logs must contain database authentication attempts, connections, and disconnections. The logs must also contain each query run against the database and record which database user ran each query.
Which steps will create the required logs?
- A. Allow access to the Amazon Redshift database using AWS IAM only. Log access using AWS CloudTrail.
- B. Enable and download audit reports from AWS Artifact.
- C. Enable Amazon Redshift Enhanced VPC Routing. Enable VPC Flow Logs to monitor traffic.
- D. Enable audit logging for Amazon Redshift using the AWS Management Console or the AWS CLI.
Answer: D
NEW QUESTION # 31
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 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.
- B. 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.
- C. 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. - D. 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.
Answer: D
NEW QUESTION # 32
A company is building a data lake and needs to ingest data from a relational database that has time-series dat a. The company wants to use managed services to accomplish this. The process needs to be scheduled daily and bring incremental data only from the source into Amazon S3.
What is the MOST cost-effective approach to meet these requirements?
- A. Use AWS Glue to connect to the data source using JDBC Drivers. Ingest incremental records only using job bookmarks.
- B. Use AWS Glue to connect to the data source using JDBC Drivers. Store the last updated key in an Amazon DynamoDB table and ingest the data using the updated key as a filter.
- C. Use AWS Glue to connect to the data source using JDBC Drivers and ingest the full data. Use AWS DataSync to ensure the delta only is written into Amazon S3.
- D. Use AWS Glue to connect to the data source using JDBC Drivers and ingest the entire dataset. Use appropriate Apache Spark libraries to compare the dataset, and find the delta.
Answer: A
Explanation:
https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html
NEW QUESTION # 33
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