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Professional Data Engineer: Professional Data Engineer on Google Cloud Platform
Page 6 out of 21 pages Questions 51-60 out of 205 questions
Question#51

You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants.
What should you do?

  • A. Increase the size of the dataset by collecting additional data.
  • B. Train a linear regression to predict a credit default risk score.
  • C. Remove the bias from the data and collect applications that have been declined loans.
  • D. Match loan applicants with their social profiles to enable feature engineering.
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B

Question#52

You need to migrate a 2TB relational database to Google Cloud Platform. You do not have the resources to significantly refactor the application that uses this database and cost to operate is of primary concern.
Which service do you select for storing and serving your data?

  • A. Cloud Spanner
  • B. Cloud Bigtable
  • C. Cloud Firestore
  • D. Cloud SQL
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D

Question#53

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?

  • A. Export Bigtable dump to GCS and run your analytical job on top of the exported files.
  • B. 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.
  • C. 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.
  • D. Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.
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B

Question#54

You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?

  • A. Batch job, PubSubIO, side-inputs
  • B. Streaming job, PubSubIO, JdbcIO, side-outputs
  • C. Streaming job, PubSubIO, BigQueryIO, side-inputs
  • D. Streaming job, PubSubIO, BigQueryIO, side-outputs
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C

Question#55

You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of your Cloud Bigtable cluster. Which two actions can you take to accomplish this? (Choose two.)

  • A. Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100.
  • B. Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Write pressure index is above 100.
  • C. Monitor the latency of write operations. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.
  • D. Monitor storage utilization. Increase the size of the Cloud Bigtable cluster when utilization increases above 70% of max capacity.
  • E. Monitor latency of read operations. Increase the size of the Cloud Bigtable cluster of read operations take longer than 100 ms.
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AC

Question#56

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?

  • A. Issue a command to restart the database servers.
  • B. Retry the query with exponential backoff, up to a cap of 15 minutes.
  • C. Retry the query every second until it comes back online to minimize staleness of data.
  • D. Reduce the query frequency to once every hour until the database comes back online.
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B

Question#57

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?

  • A. Linear regression
  • B. Logistic classification
  • C. Recurrent neural network
  • D. Feedforward neural network
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A

Question#58

You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

  • A. Include ORDER BY DESK on timestamp column and LIMIT to 1.
  • B. Use GROUP BY on the unique ID column and timestamp column and SUM on the values.
  • C. Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.
  • D. Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.
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D

Question#59

Your company is using WILDCARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:

Which table name will make the SQL statement work correctly?

  • A. 'bigquery-public-data.noaa_gsod.gsod'
  • B. bigquery-public-data.noaa_gsod.gsod*
  • C. 'bigquery-public-data.noaa_gsod.gsod'*
  • D. 'bigquery-public-data.noaa_gsod.gsod*`
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D
Reference:
https://cloud.google.com/bigquery/docs/wildcard-tables

Question#60

Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

  • A. Disable writes to certain tables.
  • B. Restrict access to tables by role.
  • C. Ensure that the data is encrypted at all times.
  • D. Restrict BigQuery API access to approved users.
  • E. Segregate data across multiple tables or databases.
  • F. Use Google Stackdriver Audit Logging to determine policy violations.
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BDF

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