PDE practice questions 101–110

1. Question 101 · 340 in bank

   Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for- like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

   • APut the data into Google Cloud Storage.
   • BUse preemptible virtual machines (VMs) for the Cloud Dataproc cluster.
   • CTune the Cloud Dataproc cluster so that there is just enough disk for all data.
   • DMigrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

   Show answer

   Answer: A

   Explanation

   That would be correct if we wanted to maximize performance without sacrificing cost too much, but we want to minimize the cost. That's the only requirement, hence answer A.

   Source: Adapted from community discussion — MadHolm

   References

   • Google Cloud documentation

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2. Question 102 · 340 in bank

   You are loading CSV files from Cloud Storage to BigQuery. The files have known data quality issues, including mismatched data types, such as STRINGs and INT64s in the same column, and inconsistent formatting of values such as phone numbers or addresses. You need to create the data pipeline to maintain data quality and perform the required cleansing and transformation. What should you do?

   • AUse Data Fusion to transform the data before loading it into BigQuery.
   • BUse Data Fusion to convert the CSV files to a self-describing data format, such as AVRO, before loading the data to BigQuery.
   • CLoad the CSV files into a staging table with the desired schema, perform the transformations with SQL, and then write the results to the final destination table.
   • DCreate a table with the desired schema, load the CSV files into the table, and perform the transformations in place using SQL.

   Show answer

   Answer: A

   Explanation

   I'm kinda inclined towards C as SQL seems a powerful option to treat this kind of use case. Also, I didn't get how the transformations mentioned on this page will help to clean the data (https://cloud.google.com/data-fusion/docs/concepts/transformation-pushdown#supported_transformations) But I guess using Wrangler plugin, this kind of stuff can be done on DataFusion, also the question talks about an pipeline, so A is the final choice.

   Source: Adapted from community discussion — saurabhsingh4k

   References

   • Google Cloud documentation
   • Google Cloud documentation

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3. Question 103 · 340 in bank

   You are developing a new deep learning model that predicts a customer's likelihood to buy on your ecommerce site. After running an evaluation of the model against both the original training data and new test data, you find that your model is overfitting the data. You want to improve the accuracy of the model when predicting new data. What should you do?

   • AIncrease the size of the training dataset, and increase the number of input features.
   • BIncrease the size of the training dataset, and decrease the number of input features.
   • CReduce the size of the training dataset, and increase the number of input features.
   • DReduce the size of the training dataset, and decrease the number of input features.

   Show answer

   Answer: B

   Explanation

   There 2 parts and they are relevant to each other 1. Overfit is fixed by decreasing the number of input features (select only essential features) 2. Accuracy is improved by increasing the amount of training data examples.

   Source: Adapted from community discussion — John_Pongthorn

   References

   • AWS documentation

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4. Question 104 · 340 in bank

   You are implementing a chatbot to help an online retailer streamline their customer service. The chatbot must be able to respond to both text and voice inquiries. You are looking for a low-code or no-cade option, and you want to be able to easily train the chatbot to provide answers to keywords. What should you do?

   • AUse the Cloud Speech-to-Text API to build a Python application in App Engine.
   • BUse the Cloud Speech-to-Text API to build a Python application in a Compute Engine instance.
   • CUse Dialogflow for simple queries and the Cloud Speech-to-Text API for complex queries.
   • DUse Dialogflow to implement the chatbot, defining the intents based on the most common queries collected.

   Show answer

   Answer: D

   Explanation

   D is the answer. https://cloud.google.com/dialogflow/docs Dialogflow is a natural language understanding platform that makes it easy to design and integrate a conversational user interface into your mobile app, web application, device, bot, interactive voice response system, and so on. Using Dialogflow, you can provide new and engaging ways for users to interact with your product. Dialogflow can analyze multiple types of input from your customers, including text or audio inputs (like from a phone or voice recording). It can also respond to your customers in a couple of ways, either through text or with synthetic speech.

   Source: Adapted from community discussion — zellck

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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5. Question 105 · 340 in bank

   An aerospace company uses a proprietary data format to store its flight data. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiently import the data into BigQuery while consuming as few resources as possible. What should you do?

   • AWrite a shell script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source.
   • BUse a standard Dataflow pipeline to store the raw data in BigQuery, and then transform the format later when the data is used.
   • CUse Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format.
   • DUse an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format.

   Show answer

   Answer: D

   Explanation

   This has to be D. How could it even be B? The source is a proprietary format. Dataflow wouldn't have a built-in template to ead the file. You will have to create something custom.

   Source: Adapted from community discussion — beanz00

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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6. Question 106 · 340 in bank

   An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and call the company's Python API to execute trades. You need to efficiently implement a solution. What should you do?

   • AUse a Pub/Sub push subscription to trigger a Cloud Function to pass the data to the Python API.
   • BWrite an application hosted on a Compute Engine instance that makes a push subscription to the Pub/Sub topic.
   • CWrite an application that makes a queue in a NoSQL database.
   • DUse Cloud Composer to subscribe to a Pub/Sub topic and call the Python API.

   Show answer

   Answer: A

   Explanation

   Because trading platform requires securely transmission to queuing If you use cloud compose then we need some other job to trigger composer … would that be cloud composer api or cloud function …

   Source: Adapted from community discussion — GCPCloudArchitectUser

   References

   • Google Cloud documentation

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7. Question 107 · 340 in bank

   Your company wants to be able to retrieve large result sets of medical information from your current system, which has over 10 TBs in the database, and store the data in new tables for further query. The database must have a low-maintenance architecture and be accessible via SQL. You need to implement a cost-effective solution that can support data analytics for large result sets. What should you do?

   • AUse Cloud SQL, but first organize the data into tables. Use JOIN in queries to retrieve data.
   • BUse BigQuery as a data warehouse. Set output destinations for caching large queries.
   • CUse a MySQL cluster installed on a Compute Engine managed instance group for scalability.
   • DUse Cloud Spanner to replicate the data across regions. Normalize the data in a series of tables.

   Show answer

   Answer: B

   Explanation

   Option B is the best approach - use BigQuery as a data warehouse, and set output destinations for caching large queries. The key reasons why BigQuery fits the requirements: It is a fully managed data warehouse built to scale to handle massive datasets and perform fast SQL analytics It has a low maintenance architecture with no infrastructure to manage SQL capabilities allow easy querying of the medical data Output destinations allow configurable caching for fast retrieval of large result sets It provides a very cost-effective solution for these large scale analytics use cases In contrast, Cloud Spanner and Cloud SQL would not scale as cost effectively for 10TB+ data volumes. Self-managed MySQL on Compute Engine also requires more maintenance. Hence, leveraging BigQuery as a fully managed data warehouse is the optimal solution here.

   Source: Adapted from community discussion — MaxNRG

   References

   • Google Cloud documentation

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8. Question 108 · 340 in bank

   You have 15 TB of data in your on-premises data center that you want to transfer to Google Cloud. Your data changes weekly and is stored in a POSIX-compliant source. The network operations team has granted you 500 Mbps bandwidth to the public internet. You want to follow Google-recommended practices to reliably transfer your data to Google Cloud on a weekly basis. What should you do?

   • AUse Cloud Scheduler to trigger the gsutil command. Use the -m parameter for optimal parallelism.
   • BUse Transfer Appliance to migrate your data into a Google Kubernetes Engine cluster, and then configure a weekly transfer job.
   • CInstall Storage Transfer Service for on-premises data in your data center, and then configure a weekly transfer job.
   • DInstall Storage Transfer Service for on-premises data on a Google Cloud virtual machine, and then configure a weekly transfer job.

   Show answer

   Answer: C

   Explanation

   C is the answer. https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets#storage-transfer-service-for-large-transfers-of-on-premises-data Like gsutil, Storage Transfer Service for on-premises data enables transfers from network file system (NFS) storage to Cloud Storage. Although gsutil can support small transfer sizes (up to 1 TB), Storage Transfer Service for on-premises data is designed for large-scale transfers (up to petabytes of data, billions of files).

   Source: Adapted from community discussion — zellck

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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9. Question 109 · 340 in bank

   You are designing a system that requires an ACID-compliant database. You must ensure that the system requires minimal human intervention in case of a failure. What should you do?

   • AConfigure a Cloud SQL for MySQL instance with point-in-time recovery enabled.
   • BConfigure a Cloud SQL for PostgreSQL instance with high availability enabled.
   • CConfigure a Bigtable instance with more than one cluster.
   • DConfigure a BigQuery table with a multi-region configuration.

   Show answer

   Answer: B

   Explanation

   We exclude [C[ as non ACID and [D] for being invalid (location is configured on Dataset level, not Table). Then, let's focus on "minimal human intervention in case of a failure" requirement in order to eliminate one answer among [A] and [B]. Basically, we have to compare point-in-time recovery with high availability. It doesn't matter whether it's about MySQL or PostgreSQL since both databases support those features. - Point-in-time recovery logs are created automatically, but restoring an instance in case of failure requires manual steps (described here: https://cloud.google.com/sql/docs/mysql/backup-recovery/pitr#perform-pitr) - High availability, in case of failure requires no human intervention: "If an HA-configured instance becomes unresponsive, Cloud SQL automatically switches to serving data from the standby instance." (from https://cloud.google.com/sql/docs/postgres/high-availability#failover-overview) So answer [B] wins.

   Source: Adapted from community discussion — NicolasN

   References

   • Google Cloud documentation
   • Google Cloud documentation

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10. Question 110 · 340 in bank

    You are implementing workflow pipeline scheduling using open source-based tools and Google Kubernetes Engine (GKE). You want to use a Google managed service to simplify and automate the task. You also want to accommodate Shared VPC networking considerations. What should you do?

    • AUse Dataflow for your workflow pipelines. Use Cloud Run triggers for scheduling.
    • BUse Dataflow for your workflow pipelines. Use shell scripts to schedule workflows.
    • CUse Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the host project.
    • DUse Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the service project.

    Show answer

    Answer: D

    Explanation

    D. Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the service project. Shared VPC requires that you designate a host project to which networks and subnetworks belong and a service project, which is attached to the host project. When Cloud Composer participates in a Shared VPC, the Cloud Composer environment is in the service project. Reference: https://cloud.google.com/composer/docs/how-to/managing/configuring-shared-vpc

    Source: Adapted from community discussion — AWSandeep

    References

    • Google Cloud documentation
    • Google Cloud documentation

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PDE practice, question 101. 340 questions available.