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Professional Data Engineer Practice Questions

Professional Data Engineer

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Question 1

Your retail company is concerned about their BigQuery analytics spend. The company runs several queries that require the use of the same aggregation for the store ID and real-time sales volume. You need to implement the optimal solution that minimizes analytics spend and returns faster results. What should you do?

Create a materialized view to minimize repetitive computations.
Create a new table from a CSV file with the repeated aggregation for the other queries to reference for faster processing.
Leverage partitioning to minimize the number of bytes read.
Use join acceleration with primary and foreign keys to increase query joining to live data.

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Question 2

You are provided a dataset of customer interactions logs, currently stored as compressed JSON files in Cloud Storage. The data is highly nested and semi-structured and needs to be loaded into BigQuery for training a BigQuery ML model. You want to leverage BigQuery's native capabilities for handling this data type, while still maintaining its nested structure. What should you do?

Flatten the JSON data into a wide, denormalized CSV format, then use a batch load job to load the data into BigQuery.
Load the JSON files directly into a BigQuery table using the JSON data type, leverage JSON functions in the BigQuery ML CREATE MODEL query for feature engineering.
Convert the JSON data into Avro format in Cloud Storage, and load it into BigQuery.
Stream the JSON data directly into a BigQuery table with a STRING column, use JSON_EXTRACT functions in the BigQuery ML CREATE MODEL query.

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Question 3

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?

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

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Question 4

You have designed an Apache Beam processing pipeline that reads from a Pub/Sub topic. The topic has a message retention duration of one day, and writes to a Cloud Storage bucket. You need to select a bucket location and processing strategy to prevent data loss in case of a regional outage with an RPO of 15 minutes. What should you do?

Use a multi-regional Cloud Storage bucket. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs. Seek the subscription back in time by 60 minutes to recover the acknowledged messages. Start the Dataflow job in a secondary region.
Use a regional Cloud Storage bucket. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs. Seek the subscription back in time by one day to recover the acknowledged messages. Start the Dataflow job in a secondary region and write in a bucket in the same region.
Use a dual-region Cloud Storage bucket. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs. Seek the subscription back in time by 15 minutes to recover the acknowledged messages. Start the Dataflow job in a secondary region.
Use a dual-region Cloud Storage bucket with turbo replication enabled. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs. Seek the subscription back in time by 60 minutes to recover the acknowledged messages. Start the Dataflow job in a secondary region.

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Question 5

You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization's data. You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness. What should you do?

Create a new dataset for sharing in each individual team's project. Grant the subscribing team the bigquery.dataViewer role on the dataset.
Use BigQuery sharing (Analytics Hub) to facilitate data sharing.
Create authorized datasets to publish shared data in the subscribing team's project.
Use BigQuery Data Transfer Service to copy datasets to a centralized BigQuery project for sharing.

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Question 6

You store and analyze your relational data in BigQuery on Google Cloud with all data that resides in US regions. You also have a variety of object stores across Microsoft Azure and Amazon Web Services (AWS), also in US regions. You want to query all your data in BigQuery daily with as little movement of data as possible. What should you do?

Use BigQuery Data Transfer Service to load files from Azure and AWS into BigQuery.
Load files from AWS and Azure to Cloud Storage with Cloud Shell gsutil rsync arguments.
Create a Dataflow pipeline to ingest files from Azure and AWS to BigQuery.
Use the BigQuery Omni functionality and BigLake tables to query files in Azure and AWS.

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

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