PDE practice questions 281–290

1. Question 281 · 340 in bank

   You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?

   • ACreate a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
   • BCreate encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
   • CCreate encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
   • DCreate encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.

   Show answer

   Answer: B

   Explanation

   A makes no sense, you need to use your own keys. You don’t create keys locally and upload them, you should import it to make it work..using the kms public key…not just “uploading” it. C is also out. IT’s between B and D Cloud KMS is a cloud-hosted key management service that lets you manage cryptographic keys for your cloud services the same way you do on-premises, You can generate, use, rotate, and destroy cryptographic keys from there. Since you want to encrypt data at rest, is B, you don’t use them for any API calls. https://cloud.google.com/compute/docs/disks/customer-managed-encryption

   Source: Adapted from community discussion — MaxNRG

   References

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

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2. Question 282 · 340 in bank

   You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?

   • ABuild and train a complex classification model with Spark MLlib to generate labels and filter the results. Deploy the models using Cloud Dataproc. Call the model from your application.
   • BBuild and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.
   • CBuild an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
   • DBuild an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.

   Show answer

   Answer: C

   Explanation

   Answer: C A & B - Need to build your own model, so discarded as options C D can do the job here using Cloud Video Intelligence API. BigTable is better option. So C is correct

   Source: Adapted from community discussion — [Removed]

   References

   • Google Cloud documentation
   • Google Cloud documentation

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3. Question 283 · 340 in bank

   You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

   • AUse Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
   • BUse Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
   • CUse Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
   • DUse Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.

   Show answer

   Answer: C

   Explanation

   @admin why all the answers are wrong. I paid 30 euros for this web and its garbage. Dataproc has no sense in this scenario, because you want to have minimal intervention/operation. D is not a good practice, the answer is C.

   Source: Adapted from community discussion — odacir

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4. Question 284 · 340 in bank

   Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log data. How should you set up the log data transfer into Google Cloud?

   • AUse Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
   • BUse Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.
   • CUse BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
   • DUse BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional storage bucket as a final destination.

   Show answer

   Answer: A

   Explanation

   Correct Answer: A Destination is GCS and having multi-regional so A is the best option available. Even since BigQuery Data Transfer Service initially supports Google application sources like Google Ads, Campaign Manager, Google Ad Manager and YouTube but it does not support destination anything other than bq data set

   Source: Adapted from community discussion — VishalB

   References

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

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5. Question 285 · 340 in bank

   You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

   • ALinear regression
   • BLogistic classification
   • CRecurrent neural network
   • DFeedforward neural network

   Show answer

   Answer: A

   Explanation

   Correct answer is A. A tip here to decide when a liner regression should be used or logistics regression needs to be used. If you are forecasting that is the values in the column that you are predicting is numeric, it is always liner regression. If you are classifying, that is buy or no buy, yes or no, you will be using logistics regression.

   Source: Adapted from community discussion — Radhika7983

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6. Question 286 · 340 in bank

   You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

   • ATransform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
   • BTransform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.
   • CCompress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
   • DCompress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.

   Show answer

   Answer: B

   Explanation

   B. The question is focused on designing storage for very large files, with support for compression, ANSI SQL queries, and parallel loading from the input locations. This can be met using GCS for storage and Bigquery permanent tables with external data source in GCS.

   Source: Adapted from community discussion — Ganshank

   References

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

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7. Question 287 · 340 in bank

   You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

   • ACall the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
   • BCall the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
   • CBuild and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.
   • DBuild and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.

   Show answer

   Answer: A

   Explanation

   Correct Answer : A Entity analysis -> Identify entities within documents receipts, invoices, and contracts and label them by types such as date, person, contact information, organization, location, events, products, and media. Sentiment analysis -> Understand the overall opinion, feeling, or attitude sentiment expressed in a block of text. -- Avoid Custom models

   Source: Adapted from community discussion — VishalB

   References

   • Google Cloud documentation
   • Google Cloud documentation

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8. Question 288 · 340 in bank

   You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

   • AUse Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.
   • BUse Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.
   • CUse Cloud Storage for storage. Link as permanent tables in BigQuery for query.
   • DUse Cloud Storage for storage. Link as temporary tables in BigQuery for query.

   Show answer

   Answer: C

   Explanation

   answer C: BigQuery can access data in external sources, known as federated sources. Instead of first loading data into BigQuery, you can create a reference to an external source. External sources can be Cloud Bigtable, Cloud Storage, and Google Drive. When accessing external data, you can create either permanent or temporary external tables. Permanent tables are those that are created in a dataset and linked to an external source. Dataset-level access controls can be applied to these tables. When you are using a temporary table, a table is created in a special dataset and will be available for approxi- mately 24 hours. Temporary tables are useful for one-time operations, such as loading data into a data warehouse. "Dan Sullivan" Book

   Source: Adapted from community discussion — daghayeghi

   References

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

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9. Question 289 · 340 in bank

   You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on non-key columns. What should you do?

   • AUse Cloud SQL for storage. Add secondary indexes to support query patterns.
   • BUse Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
   • CUse Cloud Spanner for storage. Add secondary indexes to support query patterns.
   • DUse Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

   Show answer

   Answer: C

   Explanation

   A is not correct because Cloud SQL does not natively scale horizontally. B is not correct because Cloud SQL does not natively scale horizontally. C is correct because Cloud Spanner scales horizontally, and you can create secondary indexes for the range queries that are required. D is not correct because Dataflow is a data pipelining tool to move and transform data, but the use case is centered around querying.

   Source: Adapted from community discussion — sumanshu

   References

   • Google Cloud documentation
   • Google Cloud documentation

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10. Question 290 · 340 in bank

    Your financial services company is moving to cloud technology and wants to store 50 TB of financial time-series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data. Which product should they use to store the data?

    • ACloud Bigtable
    • BGoogle BigQuery
    • CGoogle Cloud Storage
    • DGoogle Cloud Datastore

    Show answer

    Answer: A

    Explanation

    A is the answer. https://cloud.google.com/dataproc/docs/concepts/connectors/cloud-bigtable Bigtable is Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail. Bigtable is designed to handle massive workloads at consistent low latency and high throughput, so it's a great choice for both operational and analytical applications, including IoT, user analytics, and financial data analysis. Bigtable is an excellent option for any Apache Spark or Hadoop uses that require Apache HBase. Bigtable supports the Apache HBase 1.0+ APIs and offers a Bigtable HBase client in Maven, so it is easy to use Bigtable with Dataproc.

    Source: Adapted from community discussion — zellck

    References

    • Google Cloud documentation
    • Google Cloud documentation

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PDE practice, question 281. 340 questions available.