Ultimate Guide to Prepare Free Amazon AWS-Certified-Data-Analytics-Specialty Exam Questions and Answer
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Eligibility Criteria For Exam
To be eligible for the exam and its related AWS Certified Data Analytics – Specialty certification, one should fulfill the eligibility criteria. These include expertise and a minimum of 2 years' experience in using AWS services to perform diverse tasks with analytics solutions. Having at least 5 years of practical background in the field of data analytics technologies is also required. Still, the main step one has to make is passing DAS-C01 exam.
NEW QUESTION 45
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 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. - 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 YARNMemoryAvailablePercentage metric.
Create an automatic scaling policy to scale in the instance fleet 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: D
NEW QUESTION 46
A company has a business unit uploading .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to do discovery, and create tables and schemas. An AWS Glue job writes processed data from the created tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creating the Amazon Redshift table appropriately. When the AWS Glue job is rerun for any reason in a day, duplicate records are introduced into the Amazon Redshift table.
Which solution will update the Redshift table without duplicates when jobs are rerun?
- A. Modify the AWS Glue job to copy the rows into a staging table. Add SQL commands to replace the existing rows in the main table as postactions in the DynamicFrameWriter class.
- B. Use the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.
- C. Use Apache Spark's DataFrame dropDuplicates() API to eliminate duplicates and then write the data to Amazon Redshift.
- D. Load the previously inserted data into a MySQL database in the AWS Glue job. Perform an upsert operation in MySQL, and copy the results to the Amazon Redshift table.
Answer: A
Explanation:
Explanation
https://aws.amazon.com/premiumsupport/knowledge-center/sql-commands-redshift-glue-job/ See the section Merge an Amazon Redshift table in AWS Glue (upsert)
NEW QUESTION 47
A large retailer has successfully migrated to an Amazon S3 data lake architecture. The company's marketing team is using Amazon Redshift and Amazon QuickSight to analyze data, and derive and visualize insights. To ensure the marketing team has the most up-to-date actionable information, a data analyst implements nightly refreshes of Amazon Redshift using terabytes of updates from the previous day.
After the first nightly refresh, users report that half of the most popular dashboards that had been running correctly before the refresh are now running much slower. Amazon CloudWatch does not show any alerts.
What is the MOST likely cause for the performance degradation?
- A. The dashboards are suffering from inefficient SQL queries.
- B. The cluster is undersized for the queries being run by the dashboards.
- C. The nightly data refreshes are causing a lingering transaction that cannot be automatically closed by Amazon Redshift due to ongoing user workloads.
- D. The nightly data refreshes left the dashboard tables in need of a vacuum operation that could not be automatically performed by Amazon Redshift due to ongoing user workloads.
Answer: D
Explanation:
https://github.com/awsdocs/amazon-redshift-developer-guide/issues/21
NEW QUESTION 48
A company currently uses Amazon Athena to query its global datasets. The regional data is stored in Amazon S3 in the us-east-1 and us-west-2 Regions. The data is not encrypted. To simplify the query process and manage it centrally, the company wants to use Athena in us-west-2 to query data from Amazon S3 in both Regions. The solution should be as low-cost as possible.
What should the company do to achieve this goal?
- A. Enable cross-Region replication for the S3 buckets in us-east-1 to replicate data in us-west-2. Once the data is replicated in us-west-2, run the AWS Glue crawler there to update the AWS Glue Data Catalog in us-west-2 and run Athena queries.
- B. Update AWS Glue resource policies to provide us-east-1 AWS Glue Data Catalog access to us-west-2.
Once the catalog in us-west-2 has access to the catalog in us-east-1, run Athena queries in us-west-2. - C. Run the AWS Glue crawler in us-west-2 to catalog datasets in all Regions. Once the data is crawled, run Athena queries in us-west-2.
- D. Use AWS DMS to migrate the AWS Glue Data Catalog from us-east-1 to us-west-2. Run Athena queries in us-west-2.
Answer: A
NEW QUESTION 49
A large telecommunications company is planning to set up a data catalog and metadata management for multiple data sources running on AWS. The catalog will be used to maintain the metadata of all the objects stored in the data stores. The data stores are composed of structured sources like Amazon RDS and Amazon Redshift, and semistructured sources like JSON and XML files stored in Amazon S3. The catalog must be updated on a regular basis, be able to detect the changes to object metadata, and require the least possible administration.
Which solution meets these requirements?
- A. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for RDS and Amazon Redshift sources and build the Data Catalog. Use AWS crawlers for data stored in Amazon S3 to infer the schema and automatically update the Data Catalog.
- B. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the data catalog in Aurora. Schedule the Lambda functions periodically.
- C. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the DynamoDB catalog. Schedule the Lambda functions periodically.
- D. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and update the Data Catalog with metadata changes. Schedule the crawlers periodically to update the metadata catalog.
Answer: A
NEW QUESTION 50
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 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.
- C. 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.
- D. 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.
Answer: B
Explanation:
https://aws.amazon.com/blogs/aws/new-amazon-kinesis-data-analytics-for-java/
NEW QUESTION 51
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 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 polices for the base IAM roles. 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 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 polices 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 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. - D. 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.
Answer: B
NEW QUESTION 52
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 classic resize with dense storage 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 elastic resize with dense compute nodes.
Answer: C
NEW QUESTION 53
Once a month, a company receives a 100 MB .csv file compressed with gzip. The file contains 50,000 property listing records and is stored in Amazon S3 Glacier. The company needs its data analyst to query a subset of the data for a specific vendor.
What is the most cost-effective solution?
- A. Query the data from Amazon S3 Glacier directly with Amazon Glacier Select.
- B. Load the data to Amazon S3 and query it with Amazon Redshift Spectrum.
- C. Load the data into Amazon S3 and query it with Amazon S3 Select.
- D. Load the data to Amazon S3 and query it with Amazon Athena.
Answer: C
NEW QUESTION 54
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 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 YARNMemoryAvailablePercentage metric.
Create an automatic scaling policy to scale in the instance fleet 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 YARNMemoryAvailablePercentage metric.
Create an automatic scaling policy to scale in the instance groups based on the CloudWatch YARNMemoryAvailablePercentage metric.
Answer: D
Explanation:
Explanation
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-instances-guidelines.html
NEW QUESTION 55
An ecommerce company ingests a large set of clickstream data in JSON format and stores the data in Amazon S3. Business analysts from multiple product divisions need to use Amazon Athena to analyze the dat a. The company's analytics team must design a solution to monitor the daily data usage for Athena by each product division. The solution also must produce a warning when a divisions exceeds its quota Which solution will meet these requirements with the LEAST operational overhead?
- A. Use a CREATE TABLE AS SELECT (CTAS) statement to create separate tables for each product division Use AWS Budgets to track Athena usage Configure a threshold for the budget Use Amazon Simple Notification Service (Amazon SNS) to send notifications when thresholds are breached.
- B. Create an AWS account for each division Provide cross-account access to an AWS Glue Data Catalog to all the accounts. Set an Amazon CloudWatch alarm to monitor Athena usage. Use Amazon Simple Notification Service (Amazon SNS) to send notifications.
- C. Create an Athena workgroup for each division Configure a data usage control for each workgroup and a time period of 1 day Configure an action to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic
- D. Create an AWS account for each division Configure an AWS Glue Data Catalog in each account Set an Amazon CloudWatch alarm to monitor Athena usage Use Amazon Simple Notification Service (Amazon SNS) to send notifications.
Answer: C
NEW QUESTION 56
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 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.
- 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 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.
- D. 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.
Answer: B
NEW QUESTION 57
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 S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.
- C. 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.
- 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: A
NEW QUESTION 58
A streaming application is reading data from Amazon Kinesis Data Streams and immediately writing the data to an Amazon S3 bucket every 10 seconds. The application is reading data from hundreds of shards. The batch interval cannot be changed due to a separate requirement. The data is being accessed by Amazon Athena.
Users are seeing degradation in query performance as time progresses.
Which action can help improve query performance?
- A. Add more memory and CPU capacity to the streaming application.
- B. Increase the number of shards in Kinesis Data Streams.
- C. Merge the files in Amazon S3 to form larger files.
- D. Write the files to multiple S3 buckets.
Answer: C
Explanation:
Explanation
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/
NEW QUESTION 59
A manufacturing company has been collecting IoT sensor data from devices on its factory floor for a year and is storing the data in Amazon Redshift for daily analysis. A data analyst has determined that, at an expected ingestion rate of about 2 TB per day, the cluster will be undersized in less than 4 months. A long-term solution is needed. The data analyst has indicated that most queries only reference the most recent 13 months of data, yet there are also quarterly reports that need to query all the data generated from the past 7 years. The chief technology officer (CTO) is concerned about the costs, administrative effort, and performance of a long-term solution.
Which solution should the data analyst use to meet these requirements?
- A. Unload all the tables in Amazon Redshift to an Amazon S3 bucket using S3 Intelligent-Tiering. Use AWS Glue to crawl the S3 bucket location to create external tables in an AWS Glue Data Catalog.
Create an Amazon EMR cluster using Auto Scaling for any daily analytics needs, and use Amazon Athena for the quarterly reports, with both using the same AWS Glue Data Catalog. - B. Create a daily job in AWS Glue to UNLOAD records older than 13 months to Amazon S3 and delete those records from Amazon Redshift. Create an external table in Amazon Redshift to point to the S3 location. Use Amazon Redshift Spectrum to join to data that is older than 13 months.
- C. Execute a CREATE TABLE AS SELECT (CTAS) statement to move records that are older than 13 months to quarterly partitioned data in Amazon Redshift Spectrum backed by Amazon S3.
- D. Take a snapshot of the Amazon Redshift cluster. Restore the cluster to a new cluster using dense storage nodes with additional storage capacity.
Answer: B
NEW QUESTION 60
A software company hosts an application on AWS, and new features are released weekly. As part of the application testing process, a solution must be developed that analyzes logs from each Amazon EC2 instance to ensure that the application is working as expected after each deployment. The collection and analysis solution should be highly available with the ability to display new information with minimal delays.
Which method should the company use to collect and analyze the logs?
- A. Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and visualize using Amazon QuickSight.
- B. Use Amazon CloudWatch subscriptions to get access to a real-time feed of logs and have the logs delivered to Amazon Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and Kibana.
- C. Enable detailed monitoring on Amazon EC2, use Amazon CloudWatch agent to store logs in Amazon S3, and use Amazon Athena for fast, interactive log analytics.
- D. Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to Kinesis Data Firehose to further push the data to Amazon Elasticsearch Service and Kibana.
Answer: B
NEW QUESTION 61
A global pharmaceutical company receives test results for new drugs from various testing facilities worldwide. The results are sent in millions of 1 KB-sized JSON objects to an Amazon S3 bucket owned by the company. The data engineering team needs to process those files, convert them into Apache Parquet format, and load them into Amazon Redshift for data analysts to perform dashboard reporting. The engineering team uses AWS Glue to process the objects, AWS Step Functions for process orchestration, and Amazon CloudWatch for job scheduling.
More testing facilities were recently added, and the time to process files is increasing.
What will MOST efficiently decrease the data processing time?
- A. Use the AWS Glue dynamic frame file grouping option while ingesting the raw input files. Process the files and load them into Amazon Redshift tables.
- B. Use AWS Lambda to group the small files into larger files. Write the files back to Amazon S3. Process the files using AWS Glue and load them into Amazon Redshift tables.
- C. Use Amazon EMR instead of AWS Glue to group the small input files. Process the files in Amazon EMR and load them into Amazon Redshift tables.
- D. Use the Amazon Redshift COPY command to move the files from Amazon S3 into Amazon Redshift tables directly. Process the files in Amazon Redshift.
Answer: B
NEW QUESTION 62
An online gaming company is using an Amazon Kinesis Data Analytics SQL application with a Kinesis data stream as its source. The source sends three non-null fields to the application: player_id, score, and us_5_digit_zip_code.
A data analyst has a .csv mapping file that maps a small number of us_5_digit_zip_code values to a territory code. The data analyst needs to include the territory code, if one exists, as an additional output of the Kinesis Data Analytics application.
How should the data analyst meet this requirement while minimizing costs?
- A. Store the contents of the mapping file in an Amazon DynamoDB table. Change the Kinesis Data Analytics application to send its output to an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Forward the record from the Lambda function to the original application destination.
- B. Store the contents of the mapping file in an Amazon DynamoDB table. Preprocess the records as they arrive in the Kinesis Data Analytics application with an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Change the SQL query in the application to include the new field in the SELECT statement.
- C. Store the mapping file in an Amazon S3 bucket and configure it as a reference data source for the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the reference table and add the territory code field to the SELECT columns.
- D. Store the mapping file in an Amazon S3 bucket and configure the reference data column headers for the
.csv file in the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the file's S3 Amazon Resource Name (ARN), and add the territory code field to the SELECT columns.
Answer: C
NEW QUESTION 63
A company wants to run analytics on its Elastic Load Balancing logs stored in Amazon S3. A data analyst needs to be able to query all data from a desired year, month, or day. The data analyst should also be able to query a subset of the columns. The company requires minimal operational overhead and the most cost-effective solution.
Which approach meets these requirements for optimizing and querying the log data?
- A. Launch a long-running Amazon EMR cluster that continuously transforms new log files from Amazon S3 into its Hadoop Distributed File System (HDFS) storage and partitions by year, month, and day. Use Apache Presto to query the optimized format.
- B. Use an AWS Glue job nightly to transform new log files into Apache Parquet format and partition by year, month, and day. Use AWS Glue crawlers to detect new partitions. Use Amazon Athena to query data.
- C. Launch a transient Amazon EMR cluster nightly to transform new log files into Apache ORC format and partition by year, month, and day. Use Amazon Redshift Spectrum to query the data.
- D. Use an AWS Glue job nightly to transform new log files into .csv format and partition by year, month, and day. Use AWS Glue crawlers to detect new partitions. Use Amazon Athena to query data.
Answer: C
NEW QUESTION 64
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AWS Certified Data Analytics - Specialty (DAS-C01) Professional Exam Certified Professional salary
The estimated average salary of AWS Certified Data Analytics - Specialty (DAS-C01) Professional Exam is listed below:
- England: 91,773 POUND
- Europe: 106,687 EURO
- India: 60,858 INR
- United States: $129,868 USD
The Amazon Web Services (AWS) Certified Data Analytics – Specialty (DAS-C01) is an exam designed for individuals who are good at performing the data analytics role. It tests the knowledge of candidates in using AWS services to build, design, maintain, and secure analytics solutions. Those who apply for this test should understand the differences between various data analytics services and how they can fit in the data lifecycle, storage, collection, visualization, and processing.
Online Exam Practice Tests with detailed explanations!: https://testking.exams-boost.com/AWS-Certified-Data-Analytics-Specialty-valid-materials.html