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下載AWS Certified Data Analytics - Specialty (DAS-C01) Exam考試題庫
NEW QUESTION 51
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. Create a manifest file with row-level security.
- B. Separate the data by product and use S3 bucket policies for authorization.
- C. Separate the data by product and use IAM policies for authorization.
- D. Create dataset rules with row-level security.
Answer: D
Explanation:
https://docs.aws.amazon.com/quicksight/latest/user/restrict-access-to-a-data-set-using-row-level-security.html
NEW QUESTION 52
A financial company uses Amazon S3 as its data lake and has set up a data warehouse using a multi-node Amazon Redshift cluster. The data files in the data lake are organized in folders based on the data source of each data file. All the data files are loaded to one table in the Amazon Redshift cluster using a separate COPY command for each data file location. With this approach, loading all the data files into Amazon Redshift takes a long time to complete. Users want a faster solution with little or no increase in cost while maintaining the segregation of the data files in the S3 data lake.
Which solution meets these requirements?
- A. Use Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
- B. Create a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon Redshift.
- C. Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job 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.
Answer: A
NEW QUESTION 53
A company that monitors weather conditions from remote construction sites is setting up a solution to collect temperature data from the following two weather stations.
Station A, which has 10 sensors
Station B, which has five sensors
These weather stations were placed by onsite subject-matter experts.
Each sensor has a unique ID. The data collected from each sensor will be collected using Amazon Kinesis Data Streams.
Based on the total incoming and outgoing data throughput, a single Amazon Kinesis data stream with two shards is created. Two partition keys are created based on the station names. During testing, there is a bottleneck on data coming from Station A, but not from Station B.
Upon review, it is confirmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput.
How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?
- A. Increase the number of shards in Kinesis Data Streams to increase the level of parallelism.
- B. Create a separate Kinesis data stream for Station A with two shards, and stream Station A sensor data to the new stream.
- C. Reduce the number of sensors in Station A from 10 to 5 sensors.
- D. Modify the partition key to use the sensor ID instead of the station name.
Answer: D
Explanation:
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-resharding.html
"Splitting increases the number of shards in your stream and therefore increases the data capacity of the stream. Because you are charged on a per-shard basis, splitting increases the cost of your stream"
NEW QUESTION 54
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