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NEW QUESTION: 1
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on- premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?
A. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
B. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
C. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
D. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
Answer: C
Explanation:
Cloud composer is used to schedule the interdependent jobs.

NEW QUESTION: 2

A. Option D
B. Option A
C. Option C
D. Option B
Answer: A
Explanation:
Explanation
This security setting determines the number of unique new passwords that have to be associated with a user account before an old password can be reused. The value must be between 0 and 24 passwords.
This policy enables administrators to enhance security by ensuring that old passwords are not reused continually.

NEW QUESTION: 3
The Mirror Health Plan uses a form of computer/telephony integration (CTI) to manage telephone calls coming into its member services department. When a member calls the plan's central telephone number, a device answers the call with a recorded message and
A. a member outreach program
B. an interactive voice response (IVR) system
C. an automatic call distributor (ACD)
D. a complaint resolution procedure (CRP)
Answer: C

NEW QUESTION: 4
You are designing a service that aggregates clickstream data in batch and delivers reports to subscribers via email only once per week. Data is extremely spikey, geographically distributed, high-scale, and unpredictable.
How should you design this system?
A. Use a CloudFront distribution with access log delivery to S3. Clicks should be recorded as querystring GETs to the distribution. Reports are built and sent by periodically running EMRjobs over the access logs in S3.
C Use API Gateway invoking Lambdas which PutRecords into Kinesis, and EMR running Spark performing GetRecords on Kinesis to scale with spikes. Spark on EMR outputs the analysis to S3, which are sent out via email.
D- Use AWS Elasticsearch service and EC2 Auto Scaling groups. The Autoscaling groups scale based on click throughput and stream into the Elasticsearch domain, which is also scalable. Use Kibana to generate reports periodically.
B. Use a large RedShift cluster to perform the analysis, and a fleet of Lambdas to perform record inserts into the RedShift tables. Lambda will scale rapidly enough for the traffic spikes.
Answer: A
Explanation:
Explanation
When you look at building reports or analyzing data from a large data set, you need to consider CMR because this service is built on the Hadoop framework which is used to processes large data sets.
The ideal approach to getting data onto CMR is to use S3. Since the Data is extremely spikey and geographically distributed, using edge locations via Cloudfront distributions is the best way to fetch the data.
Option A is invalid because RedShift is more of a petabyte storage cluster.
Option C is invalid because having both Kinesis and CMR for the job analysis is redundant.
Option D is invalid because Elastic Search is not an option for processing records.
For more information on Amazon CMR, please visit the below URL:
* https://aws.amazon.com/emr/