Free Amazon AWS-Certified-Data-Analytics-Specialty Exam Dumps Questions & Answers

A company uses Amazon Redshift for its data warehousing needs. ETL jobs run every night to load data, apply business rules, and create aggregate tables for reporting. The company's data analysis, data science, and business intelligence teams use the data warehouse during regular business hours. The workload management is set to auto, and separate queues exist for each team with the priority set to NORMAL.
Recently, a sudden spike of read queries from the data analysis team has occurred at least twice daily, and queries wait in line for cluster resources. The company needs a solution that enables the data analysis team to avoid query queuing without impacting latency and the query times of other teams.
Which solution meets these requirements?

• A.  Configure the data analysis queue to enable concurrency scaling.
• B.  Create a query monitoring rule to add more cluster capacity for the data analysis queue when queries are waiting for resources.
• C.  Use workload management query queue hopping to route the query to the next matching queue.
• D.  Increase the query priority to HIGHEST for the data analysis queue.

A financial company uses Amazon S3 as its data lake and has set up a data warehouse using a multi-node Amazon Redshift cluster. The data files in the data lake are organized in folders based on the data source of each data file. All the data files are loaded to one table in the Amazon Redshift cluster using a separate COPY command for each data file location. With this approach, loading all the data files into Amazon Redshift takes a long time to complete. Users want a faster solution with little or no increase in cost while maintaining the segregation of the data files in the S3 data lake.
Which solution meets these requirements?

• A.  Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job to load the data into Amazon Redshift.
• B.  Use Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
• C.  Create a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon Redshift.
• D.  Use an AWS Glue job to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.

A manufacturing company uses Amazon S3 to store its dat
a. The company wants to use AWS Lake Formation to provide granular-level security on those data assets. The data is in Apache Parquet format. The company has set a deadline for a consultant to build a data lake.
How should the consultant create the MOST cost-effective solution that meets these requirements?

• A.  Create multiple IAM roles for different users and groups. Assign IAM roles to different data assets in Amazon S3 to create table-based and column-based access controls.
• B.  Install Apache Ranger on an Amazon EC2 instance and integrate with Amazon EMR. Using Ranger policies, create role-based access control for the existing data assets in Amazon S3.
• C.  Run Lake Formation blueprints to move the data to Lake Formation. Once Lake Formation has the data, apply permissions on Lake Formation.
• D.  To create the data catalog, run an AWS Glue crawler on the existing Parquet data. Register the Amazon S3 path and then apply permissions through Lake Formation to provide granular-level security.

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

A company that produces network devices has millions of users. Data is collected from the devices on an hourly basis and stored in an Amazon S3 data lake.
The company runs analyses on the last 24 hours of data flow logs for abnormality detection and to troubleshoot and resolve user issues. The company also analyzes historical logs dating back 2 years to discover patterns and look for improvement opportunities.
The data flow logs contain many metrics, such as date, timestamp, source IP, and target IP. There are about 10 billion events every day.
How should this data be stored for optimal performance?

• A.  In Apache Parquet partitioned by source IP and sorted by date
• B.  In compressed nested JSON partitioned by source IP and sorted by date
• C.  In Apache ORC partitioned by date and sorted by source IP
• D.  In compressed .csv partitioned by date and sorted by source IP