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Google Professional Data Engineer Practice Test - Questions Answers, Page 17

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List of questions

Question 161

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You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent

Question 162

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You have an Apache Kafka Cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replica

Question 163

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You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet f

Question 164

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Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of th

Question 165

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You're training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you've discovered that the datase

Question 166

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You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status f

Question 167

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You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a mode

Question 168

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You need to migrate a 2TB relational database to Google Cloud Platform. You do not have the resources to significantly refactor the application that uses this database and cost to operate is of prim

Question 169

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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 analyti

Question 170

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You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipe

Related questions

What are all of the BigQuery operations that Google charges for?

You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups: * Data engineers, which require lull data lake access * Analytic users, which require access to curated data You need to assign access rights to these two groups. What should you do?

Your infrastructure team has set up an interconnect link between Google Cloud and the on-premises network. You are designing a high-throughput streaming pipeline to ingest data in streaming from an Apache Kafka cluster hosted on-premises. You want to store the data in BigQuery, with as minima! latency as possible. What should you do?

You are designing the architecture to process your data from Cloud Storage to BigQuery by using Dataflow. The network team provided you with the Shared VPC network and subnetwork to be used by your pipelines. You need to enable the deployment of the pipeline on the Shared VPC network. What should you do?

You need to connect multiple applications with dynamic public IP addresses to a Cloud SQL instance. You configured users with strong passwords and enforced the SSL connection to your Cloud SOL instance. You want to use Cloud SQL public IP and ensure that you have secured connections. What should you do?

Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training dat a. However, when tested against new data, it performs poorly. What method can you employ to address this?

You have a BigQuery table that ingests data directly from a Pub/Sub subscription. The ingested data is encrypted with a Google-managed encryption key. You need to meet a new organization policy that requires you to use keys from a centralized Cloud Key Management Service (Cloud KMS) project to encrypt data at rest. What should you do?

You are using BigQuery with a regional dataset that includes a table with the daily sales volumes. This table is updated multiple times per day. You need to protect your sales table in case of regional failures with a recovery point objective (RPO) of less than 24 hours, while keeping costs to a minimum. What should you do?

An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?

You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests data. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table. This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?

Question 161

You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.

What should you do?

Convert your batch BQ queries into interactive BQ queries.

Create an additional project to overcome the 2K on-demand per-project quota.

Switch to flat-rate pricing and establish a hierarchical priority model for your projects.

Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.

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Question 162

You have an Apache Kafka Cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.

What should you do?

Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.

Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.

Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Source connector. Use a Dataflow job to read fron PubSub and write to GCS.

Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read fron PubSub and write to GCS.

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Question 163

You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average 200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.

What should you do?

Increase the size of your parquet files to ensure them to be 1 GB minimum.

Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.

Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.

Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.

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Question 164

Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).

What should you do?

Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.

Add a tryÖ catch block to your DoFn that transforms the data, extract erroneous rows from logs.

Add a tryÖ catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.

Add a tryÖ catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.

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Question 165

You're training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.

What should you do?

Provide latitude and longtitude as input vectors to your neural net.

Create a numeric column from a feature cross of latitude and longtitude.

Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.

Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.

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Question 166

You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.

What should you do?

Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.

Place the MariaDB instances in an Instance Group with a Health Check.

Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.

Install the StackDriver Agent and configure the MySQL plugin.

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Question 167

You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants.

What should you do?

Increase the size of the dataset by collecting additional data.

Train a linear regression to predict a credit default risk score.

Remove the bias from the data and collect applications that have been declined loans.

Match loan applicants with their social profiles to enable feature engineering.

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Question 168

You need to migrate a 2TB relational database to Google Cloud Platform. You do not have the resources to significantly refactor the application that uses this database and cost to operate is of primary concern.

Which service do you select for storing and serving your data?

Cloud Spanner

Cloud Bigtable

Cloud Firestore

Cloud SQL

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Question 169

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?

Export Bigtable dump to GCS and run your analytical job on top of the exported files.

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.

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.

Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.

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Question 170

You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to

BigQuery for analysis. Which job type and transforms should this pipeline use?

Batch job, PubSubIO, side-inputs

Streaming job, PubSubIO, JdbcIO, side-outputs

Streaming job, PubSubIO, BigQueryIO, side-inputs

Streaming job, PubSubIO, BigQueryIO, side-outputs

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Total 372 questions