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Google Professional Data Engineer Practice Test - Questions Answers, Page 14

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Question 131

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What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

Question 132

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You are training a spam classifier. You notice that you are overfitting the training dat a. Which three actions can you take to resolve this problem? (Choose three.)

Question 133

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You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files con

Question 134

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You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query: SELECT country, state, city FROM [

Question 135

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Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids.

Question 136

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Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce

Question 137

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You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to

Question 138

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Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically iden

Question 139

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You architect a system to analyze seismic dat a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process

Question 140

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An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly

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 131

What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

create a third instance and sync the data from the two storage types via batch jobs

export the data from the existing instance and import the data into a new instance

run parallel instances where one is HDD and the other is SDD

the selection is final and you must resume using the same storage type

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Question 132

You are training a spam classifier. You notice that you are overfitting the training dat a. Which three actions can you take to resolve this problem? (Choose three.)

Get more training examples

Reduce the number of training examples

Use a smaller set of features

Use a larger set of features

Increase the regularization parameters

Decrease the regularization parameters

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You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non-public information from

Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud Dataproc cluster, and depositing the results into Google BigQuery.

How should you securely run this workload?

Restrict the Google Cloud Storage bucket so only you can see the files

Grant the Project Owner role to a service account, and run the job with it

Use a service account with the ability to read the batch files and to write to BigQuery

Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery

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Question 134

You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:

SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

Users are running too many concurrent queries in the system

The [myproject:mydataset.mytable] table has too many partitions

Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values

Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew

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Question 135

Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

Create a file on a shared file and have the application servers write all bid events to that file.Process the file with Apache Hadoop to identify which user bid first.

Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.

Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.

Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.

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Question 136

Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of dat a. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)

Create a new view over events using standard SQL

Create a new partitioned table using a standard SQL query

Create a new view over events_partitioned using standard SQL

Create a service account for the ODBC connection to use for authentication

Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared "events"

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Question 137

You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

Use the TABLE_DATE_RANGE function

Use the WHERE_PARTITIONTIME pseudo column

Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD

Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD

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Question 138

Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

They have not assigned the timestamp, which causes the job to fail

They have not set the triggers to accommodate the data coming in late, which causes the job to fail

They have not applied a global windowing function, which causes the job to fail when the pipeline is created

They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

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Question 139

You architect a system to analyze seismic dat a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.

Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

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Question 140

An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.

They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose.

Which Google Cloud database should they choose?

BigQuery

Cloud SQL

Cloud BigTable

Cloud Datastore

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