Home / Google / Professional Data Engineer

Google Professional Data Engineer Practice Test - Questions Answers, Page 15

Question list

List of questions

Question 141

(0)

You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have b

Question 142

(0)

Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the mod

Question 143

(0)

Your company receives both batch- and stream-based event dat a. You want to process the data using Google Cloud Dataflow over a predictable time period. However, you realize that in some instances

Question 144

(0)

You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a line

Question 145

(0)

You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQue

Question 146

(0)

You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can

Question 147

(0)

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels

Question 148

(0)

You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and a

Question 149

(0)

Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution

Question 150

(0)

You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input l

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 141

You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges.

Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

Convert all daily log tables into date-partitioned tables

Convert the sharded tables into a single partitioned table

Enable query caching so you can cache data from previous months

Create separate views to cover each month, and query from these views

Show Answer Comment (0)

Question 142

Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google

BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?

Migrate the workload to Google Cloud Dataflow

Use pre-emptible virtual machines (VMs) for the cluster

Use a higher-memory node so that the job runs faster

Use SSDs on the worker nodes so that the job can run faster

Show Answer Comment (0)

Question 143

Your company receives both batch- and stream-based event dat a. You want to process the data using Google Cloud Dataflow over a predictable time period.

However, you realize that in some instances data can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is late or out of order?

Set a single global window to capture all the data.

Set sliding windows to capture all the lagged data.

Use watermarks and timestamps to capture the lagged data.

Ensure every datasource type (stream or batch) has a timestamp, and use the timestamps to define the logic for lagged data.

Show Answer Comment (0)

Question 144

You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm.

To do this you need to add a synthetic feature. What should the value of that feature be?

X^2+Y^2

X^2

Y^2

cos(X)

Show Answer Comment (0)

Question 145

You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.

What should you do?

Create groups for your users and give those groups access to the dataset

Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request

Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset

Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset

Show Answer Comment (0)

Question 146

You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed.

What should you do?

Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.

Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.

Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service.Use those keys to encrypt your data in all of the Compute Engine cluster instances.

Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.

Show Answer Comment (0)

Question 147

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat a. What should you do?

Build and train a complex classification model with Spark MLlib to generate labels and filter the results.Deploy the models using Cloud Dataproc. Call the model from your application.

Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.

Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.

Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.

Show Answer Comment (0)

Question 148

You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.

Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.

Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.

Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.

Show Answer Comment (0)

Question 149

Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-todate YouTube channels log dat a. How should you set up the log data transfer into Google Cloud?

Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.

Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi- Regional storage bucket as a final destination.

Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional storage bucket as a final destination.

Show Answer Comment (0)

Question 150

You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.

Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.

Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.

Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.

Show Answer Comment (0)

Total 372 questions

13 14 15 16 17

Go to page: of 38