Home / Google / Professional Data Engineer

Google Professional Data Engineer Practice Test - Questions Answers, Page 16

Question list

List of questions

Question 151

(0)

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

Question 152

(0)

You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values

Question 153

(0)

You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for r

Question 154

(0)

Your financial services company is moving to cloud technology and wants to store 50 TB of financial timeseries data in the cloud. This data is updated frequently and new data will be streaming in al

Question 155

(0)

An organization maintains a Google BigQuery dataset that contains tables with user-level dat

Question 156

(0)

Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of dat

Question 157

(0)

Your neural network model is taking days to train. You want to increase the training speed. What can you do?

Question 158

(0)

You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to

Question 159

(0)

Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel up

Question 160

(0)

After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You've loaded a table containing the output of the ori

Related questions

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?

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

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 151

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?

Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.

Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.

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.

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.

Show Answer Comment (0)

Question 152

You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud.

Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.

Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.

Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.

Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.

Show Answer Comment (0)

Question 153

You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud.

You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

Use Cloud SQL for storage. Add secondary indexes to support query patterns.

Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.

Use Cloud Spanner for storage. Add secondary indexes to support query patterns.

Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

Show Answer Comment (0)

Question 154

Your financial services company is moving to cloud technology and wants to store 50 TB of financial timeseries data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.

Which product should they use to store the data?

Cloud Bigtable

Google BigQuery

Google Cloud Storage

Google Cloud Datastore

Show Answer Comment (0)

Question 155

An organization maintains a Google BigQuery dataset that contains tables with user-level dat

They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

Create and share an authorized view that provides the aggregate results.

Create and share a new dataset and view that provides the aggregate results.

Create and share a new dataset and table that contains the aggregate results.

Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

Show Answer Comment (0)

Question 156

Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of dat

Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?

Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.

In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.

In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.

In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.

Show Answer Comment (0)

Question 157

Your neural network model is taking days to train. You want to increase the training speed. What can you do?

Subsample your test dataset.

Subsample your training dataset.

Increase the number of input features to your model.

Increase the number of layers in your neural network.

Show Answer Comment (0)

Question 158

You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster.

The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

PigLatin using Pig

HiveQL using Hive

Java using MapReduce

Python using MapReduce

Show Answer Comment (0)

Question 159

Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

Increase the CPU size on your server.

Increase the size of the Google Persistent Disk on your server.

Increase your network bandwidth from your datacenter to GCP.

Increase your network bandwidth from Compute Engine to Cloud Storage.

Show Answer Comment (0)

Question 160

After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You've loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.

What should you do?

Select random samples from the tables using the RAND() function and compare the samples.

Select random samples from the tables using the HASH() function and compare the samples.

Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.

Create stratified random samples using the OVER() function and compare equivalent samples from each table.

Show Answer Comment (0)

Total 372 questions

14 15 16 17 18

Go to page: of 38