Google Google Certifications PROFESSIONAL-DATA-ENGINEER Questions & Answers

• Question 191:

  You work for a large real estate firm and are preparing 6 TB of home sales data lo be used for machine learning You will use SOL to transform the data and use BigQuery ML lo create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

  A. When creating your model, use BigQuerys TRANSFORM clause to define preprocessing stops. At prediction time, use BigQuery"s ML. EVALUATE clause without specifying any transformations on the raw input data.

  B. When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps Before requesting predictions, use a saved query to transform your raw input data, and then use ML. EVALUATE

  C. Use a BigOuery to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction lime, use BigQuery's ML EVALUATE clause without specifying any transformations on the raw input data.

  D. Preprocess all data using Dataflow. At prediction time, use BigOuery"s ML. EVALUATE clause without specifying any further transformations on the input data.

  Correct Answer: A

  https://cloud.google.com/bigquery-ml/docs/bigqueryml-transform Using the TRANSFORM clause, you can specify all preprocessing during model creation. The preprocessing is automatically applied during the prediction and evaluation phases of machine learning

• Question 192:

  You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

  A. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

  B. Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.

  C. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

  D. Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.

  Correct Answer: C

• Question 193:

  You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:

  The user profile: What the user likes and doesn't like to eat The user account information: Name, address, preferred meal times The order information: When orders are made, from where, to whom

  The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?

  A. BigQuery

  B. Cloud SQL

  C. Cloud Bigtable

  D. Cloud Datastore

  Correct Answer: A

• Question 194:

  You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

  A. Change the processing job to use Google Cloud Dataproc instead.

  B. Manually start the Cloud Dataflow job each morning when you get into the office.

  C. Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.

  D. Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

  Correct Answer: C

• Question 195:

  You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

  A. Load the data every 30 minutes into a new partitioned table in BigQuery.

  B. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

  C. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

  D. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

  Correct Answer: A

• Question 196:

  Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low. You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

  A. Introduce data compression for each file to increase the rate file of file transfer.

  B. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

  C. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

  D. Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

  E. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.

  Correct Answer: CE

• Question 197:

  Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?

  A. Rewrite the job in Pig.

  B. Rewrite the job in Apache Spark.

  C. Increase the size of the Hadoop cluster.

  D. Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

  Correct Answer: A

• Question 198:

  You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.

  You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

  A. Redis

  B. HBase

  C. MySQL

  D. MongoDB

  E. Cassandra

  F. HDFS with Hive

  Correct Answer: BDF

• Question 199:

  You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity `Movie' the property `actors' and the property `tags' have multiple values but the property `date released' does not. A typical query would ask for all movies with actor= ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

  

  A. Option A

  B. Option B.

  C. Option C

  D. Option D

  Correct Answer: A

• Question 200:

  Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

  A. The CSV data loaded in BigQuery is not flagged as CSV.

  B. The CSV data has invalid rows that were skipped on import.

  C. The CSV data loaded in BigQuery is not using BigQuery's default encoding.

  D. The CSV data has not gone through an ETL phase before loading into BigQuery.

  Correct Answer: B