Verified AWS-Certified-Data-Analytics-Specialty dumps Q&As - 2022 Latest AWS-Certified-Data-Analytics-Specialty Download [Q22-Q47]

Verified AWS-Certified-Data-Analytics-Specialty  dumps Q&As - 2022 Latest AWS-Certified-Data-Analytics-Specialty  Download

Updated 100% Cover Real AWS-Certified-Data-Analytics-Specialty Exam Questions - 100% Pass Guarantee

AWS Data Analytics Specialty Exam Syllabus Topics:

Section	Objectives
Collection - 18%
Determine the operational characteristics of the collection system	- Evaluate that the data loss is within tolerance limits in the event of failures - Evaluate costs associated with data acquisition, transfer, and provisioning from various sources into the collection system (e.g., networking, bandwidth, ETL/data migration costs) - Assess the failure scenarios that the collection system may undergo, and take remediation actions based on impact - Determine data persistence at various points of data capture - Identify the latency characteristics of the collection system
Select a collection system that handles the frequency, volume, and the source of data	- Describe and characterize the volume and flow characteristics of incoming data (streaming, transactional, batch) - Match flow characteristics of data to potential solutions - Assess the tradeoffs between various ingestion services taking into account scalability, cost, fault tolerance, latency, etc. - Explain the throughput capability of a variety of different types of data collection and identify bottlenecks - Choose a collection solution that satisfies connectivity constraints of the source data system
Select a collection system that addresses the key properties of data, such as order, format, and compression	- Describe how to capture data changes at the source - Discuss data structure and format, compression applied, and encryption requirements - Distinguish the impact of out-of-order delivery of data, duplicate delivery of data, and the tradeoffs between at-most-once, exactly-once, and at-least-once processing - Describe how to transform and filter data during the collection process
Storage and Data Management - 22%
Determine the operational characteristics of the storage solution for analytics	- Determine the appropriate storage service(s) on the basis of cost vs. performance - Understand the durability, reliability, and latency characteristics of the storage solution based on requirements - Determine the requirements of a system for strong vs. eventual consistency of the storage system - Determine the appropriate storage solution to address data freshness requirements
Determine data access and retrieval patterns	- Determine the appropriate storage solution based on update patterns (e.g., bulk, transactional, micro batching) - Determine the appropriate storage solution based on access patterns (e.g., sequential vs. random access, continuous usage vs.ad hoc) - Determine the appropriate storage solution to address change characteristics of data (appendonly changes vs. updates) - Determine the appropriate storage solution for long-term storage vs. transient storage - Determine the appropriate storage solution for structured vs. semi-structured data - Determine the appropriate storage solution to address query latency requirements
Select appropriate data layout, schema, structure, and format	- Determine appropriate mechanisms to address schema evolution requirements - Select the storage format for the task - Select the compression/encoding strategies for the chosen storage format - Select the data sorting and distribution strategies and the storage layout for efficient data access - Explain the cost and performance implications of different data distributions, layouts, and formats (e.g., size and number of files) - Implement data formatting and partitioning schemes for data-optimized analysis
Define data lifecycle based on usage patterns and business requirements	- Determine the strategy to address data lifecycle requirements - Apply the lifecycle and data retention policies to different storage solutions
Determine the appropriate system for cataloging data and managing metadata	- Evaluate mechanisms for discovery of new and updated data sources - Evaluate mechanisms for creating and updating data catalogs and metadata - Explain mechanisms for searching and retrieving data catalogs and metadata - Explain mechanisms for tagging and classifying data
Processing - 24%
Determine appropriate data processing solution requirements	- Understand data preparation and usage requirements - Understand different types of data sources and targets - Evaluate performance and orchestration needs - Evaluate appropriate services for cost, scalability, and availability
Design a solution for transforming and preparing data for analysis	- Apply appropriate ETL/ELT techniques for batch and real-time workloads - Implement failover, scaling, and replication mechanisms - Implement techniques to address concurrency needs - Implement techniques to improve cost-optimization efficiencies - Apply orchestration workflows - Aggregate and enrich data for downstream consumption
Automate and operationalize data processing solutions	- Implement automated techniques for repeatable workflows - Apply methods to identify and recover from processing failures - Deploy logging and monitoring solutions to enable auditing and traceability
Analysis and Visualization - 18%
Determine the operational characteristics of the analysis and visualization solution	- Determine costs associated with analysis and visualization - Determine scalability associated with analysis - Determine failover recovery and fault tolerance within the RPO/RTO - Determine the availability characteristics of an analysis tool - Evaluate dynamic, interactive, and static presentations of data - Translate performance requirements to an appropriate visualization approach (pre-compute and consume static data vs. consume dynamic data)
Select the appropriate data analysis solution for a given scenario	- Evaluate and compare analysis solutions - Select the right type of analysis based on the customer use case (streaming, interactive, collaborative, operational)
Select the appropriate data visualization solution for a given scenario	- Evaluate output capabilities for a given analysis solution (metrics, KPIs, tabular, API) - Choose the appropriate method for data delivery (e.g., web, mobile, email, collaborative notebooks) - Choose and define the appropriate data refresh schedule - Choose appropriate tools for different data freshness requirements (e.g., Amazon Elasticsearch Service vs. Amazon QuickSight vs. Amazon EMR notebooks) - Understand the capabilities of visualization tools for interactive use cases (e.g., drill down, drill through and pivot) - Implement the appropriate data access mechanism (e.g., in memory vs. direct access) - Implement an integrated solution from multiple heterogeneous data sources
Security - 18%

AWS Certified Data Analytics - Specialty Exam Intro

The AWS Certified Big Data - Specialty (BDS-C00) is designed for people performing complex Big Data analyzes. This exam validates a candidate's technical skills and experience in designing and implementing AWS services to achieve data value. Validate candidate's ability to: Implement AWS Big Data core services in accordance with best practices of basic architecture Design and manage Big Data leveraging tools to automate data analysis.

 

NEW QUESTION 22
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. Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.
• C. Split large .csv files, then use a COPY command to load data into Amazon Redshift.
• D. Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.

Answer: B

 

NEW QUESTION 23
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 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. 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 25
A company is planning to do a proof of concept for a machine earning (ML) project using Amazon SageMaker with a subset of existing on-premises data hosted in the company's 3 TB data warehouse. For part of the project, AWS Direct Connect is established and tested. To prepare the data for ML, data analysts are performing data curation. The data analysts want to perform multiple step, including mapping, dropping null fields, resolving choice, and splitting fields. The company needs the fastest solution to curate the data for this project.
Which solution meets these requirements?

• A. Create custom ETL jobs on-premises to curate the data. Use AWS DMS to ingest data into Amazon S3 for ML processing.
• B. Ingest data into Amazon S3 using AWS DMS. Use AWS Glue to perform data curation and store the data in Amazon 3 for ML processing.
• C. Take a full backup of the data store and ship the backup files using AWS Snowball. Upload Snowball data into Amazon S3 and schedule data curation jobs using AWS Batch to prepare the data for ML.
• D. Ingest data into Amazon S3 using AWS DataSync and use Apache Spark scrips to curate the data in an Amazon EMR cluster. Store the curated data in Amazon S3 for ML processing.

Answer: B

 

NEW QUESTION 26
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. Redeploy the EMR clusters that are running slowly to a different Availability Zone.
• B. Increase the read capacity units (RCUs) for the shared Amazon DynamoDB table.
• C. Use a lifecycle policy to change the S3 storage class to S3 Standard for the log data.
• D. 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.

Answer: A

 

NEW QUESTION 27
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. Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.
• C. Split large .csv files, then use a COPY command to load data into Amazon Redshift.
• D. Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.

Answer: C

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/c_loading-data-best-practices.html

 

NEW QUESTION 28
A US-based sneaker retail company launched its global website. All the transaction data is stored in Amazon RDS and curated historic transaction data is stored in Amazon Redshift in the us-east-1 Region. The business intelligence (BI) team wants to enhance the user experience by providing a dashboard for sneaker trends.
The BI team decides to use Amazon QuickSight to render the website dashboards. During development, a team in Japan provisioned Amazon QuickSight in ap-northeast-1. The team is having difficulty connecting Amazon QuickSight from ap-northeast-1 to Amazon Redshift in us-east-1.
Which solution will solve this issue and meet the requirements?

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

Answer: A

 

NEW QUESTION 29
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 30
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 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.
• B. 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.
• C. 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.
• D. 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.

Answer: C

Explanation:
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-after-resharding.html the parent shards that remain after the reshard could still contain data that you haven't read yet that was added to the stream before the reshard. If you read data from the child shards before having read all data from the parent shards, you could read data for a particular hash key out of the order given by the data records' sequence numbers. Therefore, assuming that the order of the data is important, you should, after a reshard, always continue to read data from the parent shards until it is exhausted. Only then should you begin reading data from the child shards.

 

NEW QUESTION 31
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. Create dataset rules with row-level security.
• C. Separate the data by product and use IAM policies for authorization.
• D. Create a manifest file with row-level security.

Answer: B

Explanation:
https://docs.aws.amazon.com/quicksight/latest/user/restrict-access-to-a-data-set-using-row-level-security.html

 

NEW QUESTION 32
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. Amazon EMR with Apache Spark for ETL
• B. Amazon EMR with Apache Hive for JDBC clients
• C. Amazon Athena for querying data in Amazon S3 using JDBC drivers
• D. AWS Glue Data Catalog for metadata management
• E. Amazon EMR with Apache Hive, using an Amazon RDS with MySQL-compatible backed metastore
• F. AWS Glue for Scala-based ETL

Answer: A,C,E

 

NEW QUESTION 33
A company recently created a test AWS account to use for a development environment The company also created a production AWS account in another AWS Region As part of its security testing the company wants to send log data from Amazon CloudWatch Logs in its production account to an Amazon Kinesis data stream in its test account Which solution will allow the company to accomplish this goal?

• A. Create a subscription filter in the production accounts CloudWatch Logs to target the Kinesis data stream in the test account as its destination In the test account create an 1AM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account
• B. In the test account, create an 1AM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account Create a destination data stream in Kinesis Data Streams in the test account with an 1AM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account
• C. Create a destination data stream in Kinesis Data Streams in the test account with an 1AM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account Create a subscription filter in the production accounts CloudWatch Logs to target the Kinesis data stream in the test account as its destination
• D. In the test account create an 1AM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account Create a destination data stream in Kinesis Data Streams in the test account with an 1AM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account

Answer: C

 

NEW QUESTION 34
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:
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 35
A company's marketing team has asked for help in identifying a high performing long-term storage service for their data based on the following requirements:
* The data size is approximately 32 TB uncompressed.
* There is a low volume of single-row inserts each day.
* There is a high volume of aggregation queries each day.
* Multiple complex joins are performed.
* The queries typically involve a small subset of the columns in a table.
Which storage service will provide the MOST performant solution?

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

Answer: A

 

NEW QUESTION 36
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
• 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

Explanation:
data.

 

NEW QUESTION 37
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 38
A retail company is building its data warehouse solution using Amazon Redshift. As a part of that effort, the company is loading hundreds of files into the fact table created in its Amazon Redshift cluster. The company wants the solution to achieve the highest throughput and optimally use cluster resources when loading data into the company's fact table.
How should the company meet these requirements?

• A. Use a single COPY command to load the data into the Amazon Redshift cluster.
• B. Use S3DistCp to load multiple files into the Hadoop Distributed File System (HDFS) and use an HDFS connector to ingest the data into the Amazon Redshift cluster.
• C. Use LOAD commands equal to the number of Amazon Redshift cluster nodes and load the data in parallel into each node.
• D. Use multiple COPY commands to load the data into the Amazon Redshift cluster.

Answer: A

Explanation:
Explanation
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-single-copy-command.html

 

NEW QUESTION 39
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 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.
• B. 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.
• C. 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.
• D. 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.

Answer: C

Explanation:
Explanation
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-after-resharding.html the parent shards that remain after the reshard could still contain data that you haven't read yet that was added to the stream before the reshard. If you read data from the child shards before having read all data from the parent shards, you could read data for a particular hash key out of the order given by the data records' sequence numbers.
Therefore, assuming that the order of the data is important, you should, after a reshard, always continue to read data from the parent shards until it is exhausted. Only then should you begin reading data from the child shards.

 

NEW QUESTION 40
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 41
A large company has a central data lake to run analytics across different departments. Each department uses a separate AWS account and stores its data in an Amazon S3 bucket in that account. Each AWS account uses the AWS Glue Data Catalog as its data catalog. There are different data lake access requirements based on roles. Associate analysts should only have read access to their departmental data. Senior data analysts can have access in multiple departments including theirs, but for a subset of columns only.
Which solution achieves these required access patterns to minimize costs and administrative tasks?

• A. Set up an individual AWS account for the central data lake and configure a central S3 bucket. Use an AWS Lake Formation blueprint to move the data from the various buckets into the central S3 bucket.
  On each individual bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls for both associate and senior analysts to view specific tables and columns.
• B. Consolidate all AWS accounts into one account. Create different S3 buckets for each department and move all the data from every account to the central data lake account. Migrate the individual data catalogs into a central data catalog and apply fine-grained permissions to give to each user the required access to tables and databases in AWS Glue and Amazon S3.
• C. Set up an individual AWS account for the central data lake. Use AWS Lake Formation to catalog the cross- account locations. On each individual S3 bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls to allow senior analysts to view specific tables and columns.
• D. Keep the account structure and the individual AWS Glue catalogs on each account. Add a central data lake account and use AWS Glue to catalog data from various accounts. Configure cross-account access for AWS Glue crawlers to scan the data in each departmental S3 bucket to identify the schema and populate the catalog. Add the senior data analysts into the central account and apply highly detailed access controls in the Data Catalog and Amazon S3.

Answer: D

 

NEW QUESTION 42
A financial services company needs to aggregate daily stock trade data from the exchanges into a data store.
The company requires that data be streamed directly into the data store, but also occasionally allows data to be modified using SQL. The solution should integrate complex, analytic queries running with minimal latency.
The solution must provide a business intelligence dashboard that enables viewing of the top contributors to anomalies in stock prices.
Which solution meets the company's requirements?

• A. Use Amazon Kinesis Data Streams to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.
• B. Use Amazon Kinesis Data Streams to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.
• C. Use Amazon Kinesis Data Firehose to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.
• D. Use Amazon Kinesis Data Firehose to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.

Answer: A

 

NEW QUESTION 43
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 44
An education provider's learning management system (LMS) is hosted in a 100 TB data lake that is built on Amazon S3. The provider's LMS supports hundreds of schools. The provider wants to build an advanced analytics reporting platform using Amazon Redshift to handle complex queries with optimal performance.
System users will query the most recent 4 months of data 95% of the time while 5% of the queries will leverage data from the previous 12 months.
Which solution meets these requirements in the MOST cost-effective way?

• A. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Ensure the S3 Standard storage class is in use with objects in the data lake.
• B. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift federated queries to join cluster data with the data lake to reduce costs. Ensure the S3 Standard storage class is in use with objects in the data lake.
• C. Leverage DS2 nodes for the Amazon Redshift cluster. Migrate all data from Amazon S3 to Amazon Redshift. Decommission the data lake.
• D. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Use S3 lifecycle management rules to store data from the previous 12 months in Amazon S3 Glacier storage.

Answer: A

 

NEW QUESTION 45
A retail company has 15 stores across 6 cities in the United States. Once a month, the sales team requests a visualization in Amazon QuickSight that provides the ability to easily identify revenue trends across cities and stores. The visualization also helps identify outliers that need to be examined with further analysis.
Which visual type in QuickSight meets the sales team's requirements?

• A. Tree map
• B. Geospatial chart
• C. Line chart
• D. Heat map

Answer: B

 

NEW QUESTION 46
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 API CreateTable operation to create a table in the Data Catalog. Create an AWS Glue crawler and specify the table as the source.
• B. 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.
• C. Run an AWS Glue crawler that connects to one or more data stores, determines the data structures, and writes tables in the Data Catalog.
• D. 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.

Answer: A

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 47
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2. Data Analytics Fundamentals Digital Course by AWS Training

This is a digital training that is self-paced and allows candidates to learn more about planning data analysis solutions & processes. This course walks individuals through the five subjects of the AWS Certified Data Analytics exam, covering basic architecture, potential use cases, and value prepositions, among the rest. It provides detailed information about the AWS services and will the most beneficial for data scientists, data analysts, and data architects who want to enhance their skills in AWS solutions and their implementation. Some of the skills one will master during this program include the following:

• Knowledge of the types of data such as structured, unstructured, and semi-structured
• The use of Amazon Kinesis for processing streaming data
• Analyzing how Amazon EMR, Amazon Redshift, and AWS Glue work to process, transform, and cleanse data in data analysis solutions
• Understanding different ways used to analyze data and make reports with Amazon Athena and Amazon QuickSight tools
• Explain AWS services and their work to visualize data
• Classifying the characteristics of data analysis solutions
• Discovering different concepts of data schemas and defining how data and information is stored in the metastores

It is recommended to take this course after experiencing database concepts. The applicant should have a basic understanding of data storage, analytics, and processing. Sessions are offered in Japanese, Portuguese, Spanish, Indonesian, Thai, Vietnamese, Korean, Simplified Chinese, and English.

 

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