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Google Certified Professional Data Engineer Exam Sample Questions (Q13-Q18): NEW QUESTION # 13 You have an upstream process that writes data to Cloud Storage. This data is then read by an Apache Spark job that runs on Dataproc. These jobs are run in the us-central1 region, but the data could be stored anywhere in the United States. You need to have a recovery process in place in case of a catastrophic single region failure. You need an approach with a maximum of 15 minutes of data loss (RPO=15 mins). You want to ensure that there is minimal latency when reading the data. What should you do?
A. 1. Create a Cloud Storage bucket in the US multi-region.2. Run the Dataproc cluster in a zone in the ua- central1 region, reading data from the US multi-region bucket.3. In case of a regional failure, redeploy the Dataproc cluster to the us-central2 region and continue reading from the same bucket. B. 1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.2. Enable turbo replication.3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the us-south1 region.4. In case of a regional failure, redeploy your Dataproc duster to the us-south1 region and continue reading from the same bucket. C. 1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.2. Enable turbo replication.3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the same region.4. In case of a regional failure, redeploy the Dataproc clusters to the us-south1 region and read from the same bucket. D. 1. Create two regional Cloud Storage buckets, one in the us-central1 region and one in the us-south1 region.2. Have the upstream process write data to the us-central1 bucket. Use the Storage Transfer Service to copy data hourly from the us-central1 bucket to the us-south1 bucket.3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in that region.4. In case of regional failure, redeploy your Dataproc clusters to the us-south1 region and read from the bucket in that region instead. Answer: C
Explanation: To ensure data recovery with minimal data loss and low latency in case of a single region failure, the best approach is to use a dual-region bucket with turbo replication. Here's why option B is the best choice: Dual-Region Bucket: A dual-region bucket provides geo-redundancy by replicating data across two regions, ensuring high availability and resilience against regional failures. The chosen regions (us-central1 and us-south1) provide geographic diversity within the United States. Turbo Replication: Turbo replication ensures that data is replicated between the two regions within 15 minutes, meeting the Recovery Point Objective (RPO) of 15 minutes. This minimizes data loss in case of a regional failure. Running Dataproc Cluster: Running the Dataproc cluster in the same region as the primary data storage (us-central1) ensures minimal latency for normal operations. In case of a regional failure, redeploying the Dataproc cluster to the secondary region (us-south1) ensures continuity with minimal data loss. Steps to Implement: Create a Dual-Region Bucket: Set up a dual-region bucket in the Google Cloud Console, selecting us-central1 and us-south1 regions. Enable turbo replication to ensure rapid data replication between the regions. Deploy Dataproc Cluster: Deploy the Dataproc cluster in the us-central1 region to read data from the bucket located in the same region for optimal performance. Set Up Failover Plan: Plan for redeployment of the Dataproc cluster to the us-south1 region in case of a failure in the us-central1 region. Ensure that the failover process is well-documented and tested to minimize downtime and data loss. Reference Links: Google Cloud Storage Dual-Region Turbo Replication in Google Cloud Storage Dataproc Documentation
NEW QUESTION # 14 You have two projects where you run BigQuery jobs: * One project runs production jobs that have strict completion time SLAs. These are high priority jobs that must have the required compute resources available when needed. These jobs generally never go below a 300 slot utilization, but occasionally spike up an additional 500 slots. * The other project is for users to run ad-hoc analytical queries. This project generally never uses more than 200 slots at a time. You want these ad-hoc queries to be billed based on how much data users scan rather than by slot capacity. You need to ensure that both projects have the appropriate compute resources available. What should you do?
A. Create two reservations, one for each of the projects. For the SLA project, use an Enterprise Edition with a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, configure on-demand billing. B. Create two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 800 slots. For the ad-hoc project, enable autoscaling up to 200 slots. C. Create two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, set a reservation baseline of 0 slots and set the ignoreidleslot3 flag to False. D. Create a single Enterprise Edition reservation for both projects. Set a baseline of 300 slots. Enable autoscaling up to 700 slots. Answer: A
Explanation: To ensure that both production jobs with strict SLAs and ad-hoc queries have appropriate compute resources available while adhering to cost efficiency, setting up separate reservations and billing models for each project is the best approach. Here's why option B is the best choice: Separate Reservations for SLA and Ad-hoc Projects: Creating two separate reservations allows for dedicated resource management tailored to the needs of each project. The production project requires guaranteed slots with the ability to scale up as needed, while the ad-hoc project benefits from on-demand billing based on data scanned. Enterprise Edition Reservation for SLA Project: Setting a baseline of 300 slots ensures that the SLA project has the minimum required resources. Enabling autoscaling up to 500 additional slots allows the project to handle occasional spikes in workload without compromising on SLAs. On-Demand Billing for Ad-hoc Project: Using on-demand billing for the ad-hoc project ensures cost efficiency, as users are billed based on the amount of data scanned rather than reserved slot capacity. This model suits the less predictable and often lower-utilization nature of ad-hoc queries. Steps to Implement: Set Up Enterprise Edition Reservation for SLA Project: Create a reservation with a baseline of 300 slots. Enable autoscaling to allow up to an additional 500 slots as needed. Configure On-Demand Billing for Ad-hoc Project: Ensure that the ad-hoc project is set up to use on-demand billing, which charges based on data scanned by the queries. Monitor and Adjust: Continuously monitor the usage and performance of both projects to ensure that the configurations meet the needs and make adjustments as necessary. Reference: BigQuery Slot Reservations BigQuery On-Demand Pricing
NEW QUESTION # 15 You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules: No interaction by the user on the site for 1 hour
Has added more than $30 worth of products to the basket
Has not completed a transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?
A. Use a session window with a gap time duration of 60 minutes. B. Use a fixed-time window with a duration of 60 minutes. C. Use a sliding time window with a duration of 60 minutes. D. Use a global window with a time based trigger with a delay of 60 minutes. Answer: D
NEW QUESTION # 16 Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?
A. Randomization B. Field promotion C. Hashing D. Salting Answer: B
Explanation: Explanation By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries. Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti
NEW QUESTION # 17 You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?
A. In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool. B. In the Stackdriver logging admin interface, and enable a log sink export to BigQuery. C. Make a call to the Stackdriver API to list all logs, and apply an advanced filter. D. Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool. Answer: B
Explanation: Topic 1, Flowlogistic Case Study Company Overview Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping. Company Background The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources. Solution Concept Flowlogistic wants to implement two concepts using the cloud: Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets toexpand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed. Existing Technical Environment Flowlogistic architecture resides in a single data center: Databases 8 physical servers in 2 clusters SQL Server – user data, inventory, static data 3 physical servers Cassandra – metadata, tracking messages 10 Kafka servers – tracking message aggregation and batch insert Application servers – customer front end, middleware for order/customs 60 virtual machines across 20 physical servers Tomcat – Java services Nginx – static content Batch servers Storage appliances iSCSI for virtual machine (VM) hosts Fibre Channel storage area network (FC SAN) – SQL server storage Network-attached storage (NAS) image storage, logs, backups Apache Hadoop /Spark servers Core Data Lake Data analysis workloads 20 miscellaneous servers Jenkins, monitoring, bastion hosts, Business Requirements Build a reliable and reproducible environment with scaled panty of production. Aggregate data in a centralized Data Lake for analysis Use historical data to perform predictive analytics on future shipments Accurately track every shipment worldwide using proprietary technology Improve business agility and speed of innovation through rapid provisioning of new resources Analyze and optimize architecture for performance in the cloud Migrate fully to the cloud if all other requirements are met Technical Requirements Handle both streaming and batch data Migrate existing Hadoop workloads Ensure architecture is scalable and elastic to meet the changing demands of the company. Use managed services whenever possible Encrypt data flight and at rest Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around. We need to organize our information so we can more easily understand where our customers are and what they are shipping. CTO Statement IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology. CFO Statement Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
NEW QUESTION # 18 ......
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