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[Hardware] New Google Professional-Data-Engineer Dumps Book & Latest Study Professional

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【Hardware】 New Google Professional-Data-Engineer Dumps Book & Latest Study Professional

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Google Certified Professional Data Engineer Exam Sample Questions (Q278-Q283):NEW QUESTION # 278
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?
  • A. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
  • B. Use Cloud Dataproc to run your transformations. Use the diagnosecommand to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
  • C. Use 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.
  • D. Use 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.
Answer: B
Explanation:
Explanation

NEW QUESTION # 279
Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?
  • A. categorical_column_with_unknown_values
  • B. categorical_column_with_vocabulary_list
  • C. sparse_column_with_keys
  • D. categorical_column_with_hash_bucket
Answer: D
Explanation:
If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0.
What if we don't know the set of possible values in advance? Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.
Reference: https://www.tensorflow.org/tutorials/wide

NEW QUESTION # 280
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. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in
    parallel.
  • C. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
  • D. 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.
  • E. 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.
Answer: B,D

NEW QUESTION # 281
Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks.
She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?
  • A. Host a visualization tool on a VM on Google Compute Engine.
  • B. Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.
  • C. Run a local version of Jupiter on the laptop.
  • D. Grant the user access to Google Cloud Shell.
Answer: D
Explanation:
Explanation/Reference:

NEW QUESTION # 282
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?
  • A. Use the Google Cloud Billing API to see what account the warehouse is being billed to.
  • B. Get the identity and access management IIAM) policy of each table
  • C. Use Google Stackdriver Audit Logs to review data access.
  • D. Use Stackdriver Monitoring to see the usage of BigQuery query slots.
Answer: D

NEW QUESTION # 283
......
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