PDE practice questions 271–280

1. Question 271 · 340 in bank

   Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

   • AThey have not assigned the timestamp, which causes the job to fail
   • BThey have not set the triggers to accommodate the data coming in late, which causes the job to fail
   • CThey have not applied a global windowing function, which causes the job to fail when the pipeline is created
   • DThey have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

   Show answer

   Answer: D

   Explanation

   Answer: D Description: Caution: Beam’s default windowing behavior is to assign all elements of a PCollection to a single, global window and discard late data, even for unbounded PCollections. Before you use a grouping transform such as GroupByKey on an unbounded PCollection, you must do at least one of the following: —->>>>>>Set a non-global windowing function. See Setting your PCollection’s windowing function. Set a non-default trigger. This allows the global window to emit results under other conditions, since the default windowing behavior (waiting for all data to arrive) will never occur. —->>>>If you don’t set a non-global windowing function or a non-default trigger for your unbounded PCollection and subsequently use a grouping transform such as GroupByKey or Combine, your pipeline will generate an error upon construction and your job will fail. So it looks like D

   Source: Adapted from community discussion — [Removed]

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2. Question 272 · 340 in bank

   You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

   • AModify the transformMapReduce jobs to apply sensor calibration before they do anything else.
   • BIntroduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
   • CAdd sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
   • DDevelop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

   Show answer

   Answer: B

   Explanation

   Should go with B. Two reasons, it is a cleaner approach with single job to handle the calibration before the data is used in the pipeline. Second, doing this step in later stages can be complex and maintenance of those jobs in the future will become challenging.

   Source: Adapted from community discussion — SteelWarrior

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3. Question 273 · 340 in bank

   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?

   • ABigQuery
   • BCloud SQL
   • CCloud BigTable
   • DCloud Datastore

   Show answer

   Answer: B

   Explanation

   B. Cloud SQL would be the most appropriate choice for the online retailer in this scenario. Cloud SQL is a fully-managed relational database service that allows for easy management and analysis of data using SQL. It is well-suited for applications built on Google App Engine and can handle the transactional workload of an e-commerce application, as well as the analytical workload of a BI tool.

   Source: Adapted from community discussion — PolyMoe

   References

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

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4. Question 274 · 340 in bank

   Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

   • AIssue a command to restart the database servers.
   • BRetry the query with exponential backoff, up to a cap of 15 minutes.
   • CRetry the query every second until it comes back online to minimize staleness of data.
   • DReduce the query frequency to once every hour until the database comes back online.

   Show answer

   Answer: B

   Explanation

   Correct answer is B. App engine create applications that use Cloud SQL database connections effectively. Below is what is written in google cloud documnetation. If your application attempts to connect to the database and does not succeed, the database could be temporarily unavailable. In this case, sending too many simultaneous connection requests might waste additional database resources and increase the time needed to recover. Using exponential backoff prevents your application from sending an unresponsive number of connection requests when it can't connect to the database. This retry only makes sense when first connecting, or when first grabbing a connection from the pool. If errors happen in the middle of a transaction, the application must do the retrying, and it must retry from the beginning of a transaction. So even if your pool is configured properly, the application might still see errors if connections are lost. reference link is https://cloud.google.com/sql/docs/mysql/manage-connections

   Source: Adapted from community discussion — Radhika7983

   References

   • Google Cloud documentation
   • Google Cloud documentation

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5. Question 275 · 340 in bank

   You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

   • AConvert all daily log tables into date-partitioned tables
   • BConvert the sharded tables into a single partitioned table
   • CEnable query caching so you can cache data from previous months
   • DCreate separate views to cover each month, and query from these views

   Show answer

   Answer: B

   Explanation

   Answer: B Description: Google says that when you have multiple wildcard tables, best option is to shard it into single partitioned table. Time and cost efficient

   Source: Adapted from community discussion — [Removed]

   References

   • Google Cloud documentation
   • Google Cloud documentation

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6. Question 276 · 340 in bank

   Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?

   • AMigrate the workload to Google Cloud Dataflow
   • BUse pre-emptible virtual machines (VMs) for the cluster
   • CUse a higher-memory node so that the job runs faster
   • DUse SSDs on the worker nodes so that the job can run faster

   Show answer

   Answer: B

   Explanation

   I believe Exam Topics ought to provide brief explanation or supporting link to picked correct answers such as this one. Option A may be correct from the view point that Dataflow is a Serverless service that is fast, cost-effective and the fact that Preemptible VMs though can give large price discount may not always be available. It will be great to know the reason(s) behind Exam Topic selected option.

   Source: Adapted from community discussion — abi01a

   References

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

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7. Question 277 · 340 in bank

   Your company receives both batch- and stream-based event data. You want to process the data using Google Cloud Dataflow over a predictable time period. However, you realize that in some instances data can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is late or out of order?

   • ASet a single global window to capture all the data.
   • BSet sliding windows to capture all the lagged data.
   • CUse watermarks and timestamps to capture the lagged data.
   • DEnsure every datasource type (stream or batch) has a timestamp, and use the timestamps to define the logic for lagged data.

   Show answer

   Answer: C

   Explanation

   Answer: C Description: A watermark is a threshold that indicates when Dataflow expects all of the data in a window to have arrived. If new data arrives with a timestamp that's in the window but older than the watermark, the data is considered late data.

   Source: Adapted from community discussion — [Removed]

   References

   • Google Cloud documentation

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8. Question 278 · 340 in bank

   You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm. To do this you need to add a synthetic feature. What should the value of that feature be?

   

   • AX2+Y2
   • BX2
   • CY2
   • Dcos(X)

   Show answer

   Answer: A

   Explanation

   The 2 variables that make a circle in http://playground.tensorflow.org are x1^2 and x2^2. Sin(x) or cos(x) would just make horizontal stripes. To do this you’d use those 2 variables, learning rate 0,3 for example, classification type, no regularization needed and any activation function will work fine.

   Source: Adapted from community discussion — medeis_jar

   References

   • Google developer documentation

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9. Question 279 · 340 in bank

   You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application. What should you do?

   • ACreate groups for your users and give those groups access to the dataset
   • BIntegrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
   • CCreate a service account and grant dataset access to that account. Use the service account's private key to access the dataset
   • DCreate a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset

   Show answer

   Answer: C

   Explanation

   Option A: Create groups for your users and give those groups access to the dataset, is not the best option because it still requires users to authenticate to BigQuery Option B: Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request is not the best option because it still requires users to authenticate to BigQuery. Option D: Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset is not a secure option because it involves storing sensitive information in a file on the file system, which can be easily accessed by unauthorized users.

   Source: Adapted from community discussion — samdhimal

   References

   • Google Cloud documentation

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10. Question 280 · 340 in bank

    You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

    • AUse Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataproc job.
    • BUse Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.
    • CUse Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.
    • DUse Cloud Dataflow to find null values in sample source data. Convert all nulls to 0 using a custom script.

    Show answer

    Answer: B

    Explanation

    Instead of having to write the custom script from scratch (option D), dataprep already has preconfigured tools for your use to perform the necessary data wrangling. As mentioned by jvg637, real-values have to be "0". Considering both points above, answer should be 'B'

    Source: Adapted from community discussion — Snobid

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PDE practice, question 271. 340 questions available.