PDE practice questions 61–70

1. Question 61 · 340 in bank

   You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

   • AConsume the stream of data in Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
   • BConsume the stream of data in Dataflow using Kafka IO. Set a fixed time window of 1 hour. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
   • CUse Kafka Connect to link your Kafka message queue to Pub/Sub. Use a Dataflow template to write your messages from Pub/Sub to Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Bigtable in the last hour. If that number falls below 4000, send an alert.
   • DUse Kafka Connect to link your Kafka message queue to Pub/Sub. Use a Dataflow template to write your messages from Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.

   Show answer

   Answer: A

   Explanation

   A is the answer. https://cloud.google.com/dataflow/docs/concepts/streaming-pipelines#windows Windowing functions divide unbounded collections into logical components, or windows. Windowing functions group unbounded collections by the timestamps of the individual elements. Each window contains a finite number of elements. You set the following windows with the Apache Beam SDK or Dataflow SQL streaming extensions: - Hopping windows (called sliding windows in Apache Beam) A hopping window represents a consistent time interval in the data stream. Hopping windows can overlap, whereas tumbling windows are disjoint. For example, a hopping window can start every thirty seconds and capture one minute of data. The frequency with which hopping windows begin is called the period. This example has a one-minute window and thirty-second period.

   Source: Adapted from community discussion — zellck

   References

   • Google Cloud documentation
   • Google Cloud documentation

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2. Question 62 · 340 in bank

   You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?

   • ACreate a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.
   • BCreate a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.
   • CCreate a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.
   • DCreate a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.

   Show answer

   Answer: A

   Explanation

   Seems to depend on the date the question was published. A) is mentioned in legacy config "The legacy configuration for high availability used a failover replica instance." https://cloud.google.com/sql/docs/mysql/configure-legacy-ha B) Read replica is mentioned here https://cloud.google.com/sql/docs/mysql/high-availability scroll down to the diagrams C) external read replica seems to be wrong, never heard of it D) Aligns which Google steps, but automatic backup is not mentioned for HA https://cloud.google.com/sql/docs/mysql/configure-ha#ha-create just regional instance mentioned I would pick D now, since regional instance is described as HA, although the automatic backup seems like a bonus.

   Source: Adapted from community discussion — hdmi_switch

   References

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

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3. Question 63 · 340 in bank

   Your company is selecting a system to centralize data ingestion and delivery. You are considering messaging and data integration systems to address the requirements. The key requirements are: ✑ The ability to seek to a particular offset in a topic, possibly back to the start of all data ever captured ✑ Support for publish/subscribe semantics on hundreds of topics Retain per-key ordering -

   

   Which system should you choose?

   • AApache Kafka
   • BCloud Storage
   • CDataflow
   • DFirebase Cloud Messaging

   Show answer

   Answer: A

   Explanation

   Ability to Seek to a Particular Offset: Kafka allows consumers to seek to a specific offset in a topic, enabling you to read data from a specific point, including back to the start of all data ever captured. This is a fundamental capability of Kafka. Support for Publish/Subscribe Semantics: Kafka supports publish/subscribe semantics through topics. You can have hundreds of topics in Kafka, and consumers can subscribe to these topics to receive messages in a publish/subscribe fashion. Retain Per-Key Ordering: Kafka retains the order of messages within a partition. If you have a key associated with your messages, you can ensure per-key ordering by sending messages with the same key to the same partition. Scalability: Kafka is designed to handle high-throughput data streaming and is capable of scaling to meet your needs. Apache Kafka aligns well with the requirements you've outlined for centralized data ingestion and delivery. It's a robust choice for scenarios that involve data streaming, publish/subscribe, and retaining message ordering.

   Source: Adapted from community discussion — barnac1es

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4. Question 64 · 340 in bank

   You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

   • ADeploy a Dataproc cluster. Use a standard persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
   • BDeploy a Dataproc cluster. Use an SSD persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
   • CInstall Hadoop and Spark on a 10-node Compute Engine instance group with standard instances. Install the Cloud Storage connector, and store the data in Cloud Storage. Change references in scripts from hdfs:// to gs://
   • DInstall Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances. Store data in HDFS. Change references in scripts from hdfs:// to gs://

   Show answer

   Answer: A

   Explanation

   Confused between A and B. For r/w intensive jobs need to use SSDs. But questions doesnt state anything about the nature of the jobs. So better to start with a default option. Choose A

   Source: Adapted from community discussion — [Removed]

   References

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

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5. Question 65 · 340 in bank

   Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?

   • APerform hyperparameter tuning
   • BTrain a classifier with deep neural networks, because neural networks would always beat SVMs
   • CDeploy the model and measure the real-world AUC; it's always higher because of generalization
   • DScale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC

   Show answer

   Answer: A

   Explanation

   Neural Network can be better, but not always. That isn't true that more complex architecture/method are always better, especially we can't say that when we know nothing about dataset. Generally SVM is a quite good ml method for many problems. A is the correct answer. Why B is a wrong one I've just described above. C is wrong because we can't assume that our model will be work better with new data and generally models don't work better. D doesn't make sense, it's not about preprocessing features (which is usefull), but only about a kind of postprocessing.

   Source: Adapted from community discussion — zonc

   References

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

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6. Question 66 · 340 in bank

   You need to deploy additional dependencies to all nodes of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

   • ADeploy the Cloud SQL Proxy on the Cloud Dataproc master
   • BUse an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
   • CCopy all dependencies to a Cloud Storage bucket within your VPC security perimeter
   • DUse Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

   Show answer

   Answer: C

   Explanation

   Correct: C If you create a Dataproc cluster with internal IP addresses only, attempts to access the Internet in an initialization action will fail unless you have configured routes to direct the traffic through a NAT or a VPN gateway. Without access to the Internet, you can enable Private Google Access, and place job dependencies in Cloud Storage; cluster nodes can download the dependencies from Cloud Storage from internal IPs.

   Source: Adapted from community discussion — [Removed]

   References

   • Google Cloud documentation
   • Google Cloud documentation

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7. Question 67 · 340 in bank

   You need to choose a database for a new project that has the following requirements: ✑ Fully managed ✑ Able to automatically scale up ✑ Transactionally consistent ✑ Able to scale up to 6 TB ✑ Able to be queried using SQL Which database do you choose?

   • ACloud SQL
   • BCloud Bigtable
   • CCloud Spanner
   • DCloud Datastore

   Show answer

   Answer: A

   Explanation

   Correct: A It asks for scaling up which can be done in cloud sql, horizontal scaling is not possible in cloud sql Automatic storage increase If you enable this setting, Cloud SQL checks your available storage every 30 seconds. If the available storage falls below a threshold size, Cloud SQL automatically adds additional storage capacity. If the available storage repeatedly falls below the threshold size, Cloud SQL continues to add storage until it reaches the maximum of 30 TB.

   Source: Adapted from community discussion — [Removed]

   References

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

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8. Question 68 · 340 in bank

   Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing. What should you do first?

   • AUse Google Stackdriver Audit Logs to review data access.
   • BGet the identity and access management IIAM) policy of each table
   • CUse Stackdriver Monitoring to see the usage of BigQuery query slots.
   • DUse the Google Cloud Billing API to see what account the warehouse is being billed to.

   Show answer

   Answer: A

   Explanation

   To begin securing your data warehouse in Google BigQuery and gain insights into what everyone is doing with the datasets, the first step you should take is: A. Use Google Stackdriver Audit Logs to review data access. Reviewing the audit logs provides visibility into who is accessing your data, when they are doing so, and what actions they are taking within BigQuery. This is crucial for understanding current data usage and potential security risks. Option B (getting the IAM policy of each table) is important but more focused on controlling access rather than discovering what everyone is currently doing. Option C (using Stackdriver Monitoring to see query slots usage) can help with monitoring and optimizing your BigQuery usage but doesn't provide a comprehensive view of what users are doing with the data. Option D (using the Google Cloud Billing API) is more related to tracking billing information rather than understanding what users are doing with the data.

   Source: Adapted from community discussion — rtcpost

   References

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

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9. Question 69 · 340 in bank

   You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

   • ACloud SQL
   • BCloud Bigtable
   • CCloud Spanner
   • DCloud Datastore

   Show answer

   Answer: A

   Explanation

   A is the answer. https://cloud.google.com/sql/docs/features#features Up to 64 TB of storage available, with the ability to automatically increase storage size as needed.

   Source: Adapted from community discussion — zellck

   References

   • Google Cloud documentation
   • Google Cloud documentation

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10. Question 70 · 340 in bank

    You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

    • ACreate a table in BigQuery, and append the new samples for CPU and memory to the table
    • BCreate a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
    • CCreate a narrow table in Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
    • DCreate a wide table in Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

    Show answer

    Answer: C

    Explanation

    Answer C A tall and narrow table has a small number of events per row, which could be just one event, whereas a short and wide table has a large number of events per row. As explained in a moment, tall and narrow tables are best suited for time-series data. For time series, you should generally use tall and narrow tables. This is for two reasons: Storing one event per row makes it easier to run queries against your data. Storing many events per row makes it more likely that the total row size will exceed the recommended maximum (see Rows can be big but are not infinite). https://cloud.google.com/bigtable/docs/schema-design-time-series#patterns_for_row_key_design

    Source: Adapted from community discussion — psu

    References

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

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PDE practice, question 61. 340 questions available.