PROFESSIONAL-DATA-ENGINEER Online Practice Questions and Answers

Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

A. Redefine the schema by evenly distributing reads and writes across the row space of the table.

B. The performance issue should be resolved over time as the site of the BigDate cluster is increased.

C. Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.

D. Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.

The _________ for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

A. Cloud Dataflow connector

B. DataFlow SDK

C. BiqQuery API

D. BigQuery Data Transfer Service

If a dataset contains rows with individual people and columns for year of birth, country, and income, how many of the columns are continuous and how many are categorical?

A. 1 continuous and 2 categorical

B. 3 categorical

C. 3 continuous

D. 2 continuous and 1 categorical

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. Use feature engineering to add features for eyes, noses, and mouths to the input data.

C. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.

D. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

Which of the following statements about Legacy SQL and Standard SQL is not true?

A. Standard SQL is the preferred query language for BigQuery.

B. If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.

C. One difference between the two query languages is how you specify fully-qualified table names (i.e. table names that include their associated project name).

D. You need to set a query language for each dataset and the default is Standard SQL.

You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDFS. Mount the Hive tables locally.

B. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluster. Mount the Hive tables locally.

C. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluster. Then run the Hadoop utility to copy them do HDFS. Mount the Hive tables from HDFS.

D. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive tables. Replicate external Hive tables to the native ones.

E. Load the ORC files into BigQuery. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive tables. Replicate external Hive tables to the native ones.

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.

Your neural network model is taking days to train. You want to increase the training speed. What can you do?

A. Subsample your test dataset.

B. Subsample your training dataset.

C. Increase the number of input features to your model.

D. Increase the number of layers in your neural network.

You are using Cloud Bigtable to persist and serve stock market data for each of the major indices. To serve the trading application, you need to access only the most recent stock prices that are streaming in How should you design your row key and tables to ensure that you can access the data with the most simple query?

A. Create one unique table for all of the indices, and then use the index and timestamp as the row key design

B. Create one unique table for all of the indices, and then use a reverse timestamp as the row key design.

C. For each index, have a separate table and use a timestamp as the row key design

D. For each index, have a separate table and use a reverse timestamp as the row key design

You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data tittered by location_id and device_version with the following query:

You want to optimize your queries for cost and performance. How should you structure your data?

A. Partition table data by create_date, location_id and device_version

B. Partition table data by create_date cluster table data by tocation_id and device_version

C. Cluster table data by create_date location_id and device_version

D. Cluster table data by create_date, partition by location and device_version