Online Access Free Professional-Data-Engineer Practice Test

You created an analytics environment on Google Cloud so that your data scientist team can explore data without impacting the on-premises Apache Hadoop solution. The data in the on- premises Hadoop Distributed File System (HDFS) cluster is in Optimized Row Columnar (ORC) formatted files with multiple columns of Hive partitioning. The data scientist team needs to be able to explore the data in a similar way as they used the on-premises HDFS cluster with SQL on the Hive query engine. You need to choose the most cost-effective storage and processing solution.
What should you do?

• A.Import the ORC files to Bigtable tables for the data scientist team.
• B.Import the ORC files to BigQuery tables for the data scientist team.
• C.Copy the ORC files on Cloud Storage, then deploy a Dataproc cluster for the data scientist team.
• D.Copy the ORC files on Cloud Storage, then create external BigQuery tables for the data scientist team.

Your large e-commerce company uses BigQuery for its data warehousing needs. They have two primary types of workloads:
- Analyst dashboards: Interactive queries run by business analysts
throughout the day, requiring low latency for real-time insights.
- ETL jobs: Large, complex batch queries that load and transform data
overnight. These jobs are cost-sensitive and can tolerate longer
execution times, and they do not have the same performance requirements as the dashboards.
Analysts are reporting inconsistent performance of their dashboards, particularly during peak business hours. You need to ensure reliable performance for both workload types while optimizing costs. What should you do?

• A.Run all analyst dashboard queries as interactive queries and configure ETL jobs to run as batch queries.
• B.Use BigQuery editions to create an autoscaling reservation with dedicated slots for analyst dashboards, while running ETL jobs with a separate reservation or using the on-demand billing model.
• C.Implement a custom autoscaling solution using Cloud Run functions to dynamically adjust available BigQuery slots based on time of day.
• D.Migrate all ETL jobs to Cloud SQL to reduce BigQuery's workload.

You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

• A.Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.
• B.Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
• C.Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.
• D.Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.

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

Which of these is NOT a way to customize the software on Dataproc cluster instances?

• A.Configure the cluster using Cloud Deployment Manager
• B.Log into the master node and make changes from there
• C.Set initialization actions
• D.Modify configuration files using cluster properties