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Google Data Engineer Sample Questions (Q35-Q40):

NEW QUESTION # 35
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
B. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
C. Use feature engineering to add features for eyes, noses, and mouths to the input data.
D. Use K-means Clustering to detect faces in the pixels.

Answer: B

Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K- means Clustering is an unsupervised learning method used to categorize unlabeled data. Reference: https://deeplearning4j.org/neuralnet-overview

NEW QUESTION # 36
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

A. Cloud Datastore
B. Cloud SQL
C. Cloud Bigtable
D. Cloud Spanner

Answer: B

NEW QUESTION # 37
You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A. Cloud Scheduler
B. Workflow Templates on Cloud Dataproc
C. Cloud Composer
D. cron

Answer: B

NEW QUESTION # 38
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A. Keep your row key as long as the field permits
B. Include multiple time series values within the row key
C. Keep the row keep as an 8 bit integer
D. Keep your row key reasonably short

Answer: D

Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION # 39
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. Assemble 1,000 files into a tape archive (TAR) fil
C. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
D. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
E. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
F. 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.

Answer: D,E

NEW QUESTION # 40
......

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