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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A) Use BigQuery'ssupport for external data sources to query.
B) Denormalize the data as must as possible.
C) Use BigQuery UPDATE to further reduce the size of the dataset.
D) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
E) Preserve the structure of the data as much as possible.
F) Develop a data pipeline where status updates are appended to BigQuery instead of updated.

2. You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

A) Cloud Dataflow
B) Cloud Functions
C) Cloud Scheduler
D) Cloud Composer

3. Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A) Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
B) Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
C) Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
D) Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes

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

5. You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A) Use Cloud Vision AutoML, but reduce your dataset twice.
B) Use Cloud Vision AutoML with the existing dataset.
C) Use Cloud Vision API by providing custom labels as recognition hints.
D) Train your own image recognition model leveraging transfer learning techniques.

Solutions: