Google exactexam professional-data-engineer Questions Bank by Elowen q161 vce pdf

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Exam Name:	Google Professional Data Engineer Exam
Exam Code:	Professional-Data-Engineer Dumps
Vendor:	Google	Certification:	Google Cloud Certified
Questions:	383 Q&A's	Shared By:	elowen

Question 28

You’re using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes. You’ve recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload.

What should you do?

Options:

Export Bigtable dump to GCS and run your analytical job on top of the exported files.

Add a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.

Add a second cluster to an existing instance with a single-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.

Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.

Discussion

Question 29

Your infrastructure team has set up an interconnect link between Google Cloud and the on-premises network. You are designing a high-throughput streaming pipeline to ingest data in streaming from an Apache Kafka cluster hosted on-premises. You want to store the data in BigQuery, with as minima! latency as possible. What should you do?

Options:

Use a proxy host in the VPC in Google Cloud connecting to Kafka. Write a Dataflow pipeline, read data from the proxy host, and write the data to BigQuery.

Setup a Kafka Connect bridge between Kafka and Pub/Sub. Use a Google-provided Dataflow template to read the data from Pub/Sub, and write the data to BigQuery.

Setup a Kafka Connect bridge between Kafka and Pub/Sub. Write a Dataflow pipeline, read the data from Pub/Sub, and write the data toBigQuery.

Use Dataflow, write a pipeline that reads the data from Kafka, and writes the data to BigQuery.

Discussion

Answer:

Explanation:

Here's a detailed breakdown of why this solution is optimal and why others fall short:

Why Option C is the Best Solution:

Kafka Connect Bridge:This bridge acts as a reliable and scalable conduit between your on-premises Kafka cluster and Google Cloud's Pub/Sub messaging service. It handles the complexities of securely transferring data over the interconnect link.

Pub/Sub as a Buffer:Pub/Sub serves as a highly scalable buffer, decoupling the Kafka producer from the Dataflow consumer. This is crucial for handling fluctuations in message volume and ensuring smooth data flow even during spikes.

Custom Dataflow Pipeline:Writing a custom Dataflow pipeline gives you the flexibility to implement any necessary transformations or enrichments to the data before it's written to BigQuery. This is often required in real-world streaming scenarios.

Minimal Latency:By using Pub/Sub as a buffer and Dataflow for efficient processing, you minimize the latency between the data being produced in Kafka and being available for querying in BigQuery.

Why Other Options Are Not Ideal:

Option A:Using a proxy host introduces an additional point of failure and can create a bottleneck, especially with high-throughput streaming.

Option B:While Google-provided Dataflow templates can be helpful, they might lack the customization needed for specific transformations or handling complex data structures.

Option D:Dataflow doesn't natively connect to on-premises Kafka clusters. Directly reading from Kafka would require complex networking configurations and could lead to performance issues.

Additional Considerations:

Schema Management:Ensure that the schema of the data being produced in Kafka is compatible with the schema expected in BigQuery. Consider using tools like Schema Registry for schema evolution management.

Monitoring:Set up robust monitoring and alerting to detect any issues in the pipeline, such as message backlogs or processing errors.

By following Option C, you leverage the strengths of Kafka Connect, Pub/Sub, and Dataflow to create a high-throughput, low-latency streaming pipeline that seamlessly integrates your on-premises Kafka data with BigQuery.

Question 30

You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the “Trust No One” (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your data. What should you do?

Options:

Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.

Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket. Manually destroy the key previously used for encryption, and rotate the key once and rotate the key once.

Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.

Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.

Discussion

Question 31

An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.

They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

Options:

BigQuery

Cloud SQL

Cloud BigTable

Cloud Datastore

Discussion

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