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Amazon AWS Certified Data Analytics – Specialty is no doubt a highly valued and industry recognized certification. It will speak on your behalf concerning validation of your expertise in designing, building, maintaining, and securing analytics solutions efficiently. This makes you an asset that most organizations are looking for, thus positioning you for better positions. You will be a highly qualified professional for the titles, such as a Data Scientist, a Solutions Architect, a Data Analysts, and a Data Platform Engineer, among others. As for the salary, the potential candidates are expected to earn $90,000-$150,000 per year. The amount of the payment will depend on your job role, related tasks, working experience, and other criteria.

You can read the AWS Certified Data Analytics Specialty Exam topics below

Candidates must know the exam topics before they start of preparation. Because it will really help them in hitting the core. Our AWS Certified Data Analytics - Specialty exam dumps will include the following topics:

• Domain 5: Visualization 12%
• Domain 4: Analysis 17%
• Domain 2: Storage 17%
• Domain 3: Processing 17%

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Amazon AWS Certified Data Analytics - Specialty (DAS-C01) Exam Sample Questions (Q88-Q93):

NEW QUESTION # 88
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 Firehose 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 Streams 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 # 89
A data analyst is designing a solution to interactively query datasets with SQL using a JDBC connection.
Users will join data stored in Amazon S3 in Apache ORC format with data stored in Amazon Elasticsearch Service (Amazon ES) and Amazon Aurora MySQL.
Which solution will provide the MOST up-to-date results?

• A. Use Amazon DMS to stream data from Amazon ES and Aurora MySQL to Amazon Redshift. Query the data with Amazon Redshift.
• B. Use AWS Glue jobs to ETL data from Amazon ES and Aurora MySQL to Amazon S3. Query the data with Amazon Athena.
• C. Query all the datasets in place with Apache Presto running on Amazon EMR.
• D. Query all the datasets in place with Apache Spark SQL running on an AWS Glue developer endpoint.

Answer: D
NEW QUESTION # 90
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. 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.
• B. 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.
• C. 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.
• D. 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.

Answer: B
NEW QUESTION # 91
An ecommerce company is migrating its business intelligence environment from on premises to the AWS Cloud. The company will use Amazon Redshift in a public subnet and Amazon QuickSight. The tables already are loaded into Amazon Redshift and can be accessed by a SQL tool.
The company starts QuickSight for the first time. During the creation of the data source, a data analytics specialist enters all the information and tries to validate the connection. An error with the following message occurs: "Creating a connection to your data source timed out." How should the data analytics specialist resolve this error?

• A. Add the QuickSight IP address range into the Amazon Redshift security group.
• B. Create an IAM role for QuickSight to access Amazon Redshift.
• C. Grant the SELECT permission on Amazon Redshift tables.
• D. Use a QuickSight admin user for creating the dataset.

Answer: C Explanation:
Connection to the database times out
Your client connection to the database appears to hang or time out when running long queries, such as a COPY command. In this case, you might observe that the Amazon Redshift console displays that the query has completed, but the client tool itself still appears to be running the query. The results of the query might be missing or incomplete depending on when the connection stopped.
NEW QUESTION # 92
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. Modify the partition key to use the sensor ID instead of the station name.
• D. Reduce the number of sensors in Station A from 10 to 5 sensors.

Answer: C Explanation:
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 # 93
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