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Google Professional Data Engineer Practice Test - Questions Answers, Page 26

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List of questions

Question 251

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You have uploaded 5 years of log data to Cloud Storage A user reported that some data points in the log data are outside of their expected ranges, which indicates errors You need to address this iss

Question 252

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An aerospace company uses a proprietary data format to store its night dat a. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import th

Question 253

(0)

An aerospace company uses a proprietary data format to store its night dat a. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import th

Question 254

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You are using BigQuery and Data Studio to design a customer-facing dashboard that displays large quantities of aggregated dat a. You expect a high volume of concurrent users. You need to optimize ti

Question 255

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You need ads data to serve Al models and historical data tor analytics longtail and outlier data points need to be identified You want to cleanse the data n near-reel time before running it through

Question 256

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The Development and External teams nave the project viewer Identity and Access Management (1AM) role m a folder named Visualization. You want the Development Team to be able to read data from both C

Question 257

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A TensorFlow machine learning model on Compute Engine virtual machines (n2-standard -32) takes two days to complete framing. The model has custom TensorFlow operations that must run partially on a C

Question 258

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An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and cat the company

Question 259

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You are designing a pipeline that publishes application events to a Pub/Sub topic. You need to aggregate events across hourly intervals before loading the results to BigQuery for analysis. Your solu

Question 260

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Your company is migrating its on-premises data warehousing solution to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply daily updates from transactional da

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?

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?

You work for a large real estate firm and are preparing 6 TB of home sales data lo be used for machine learning You will use SOL to transform the data and use BigQuery ML lo create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

Question 251

You have uploaded 5 years of log data to Cloud Storage A user reported that some data points in the log data are outside of their expected ranges, which indicates errors You need to address this issue and be able to run the process again in the future while keeping the original data for compliance reasons. What should you do?

Import the data from Cloud Storage into BigQuery Create a new BigQuery table, and skip the rows with errors.

Create a Compute Engine instance and create a new copy of the data in Cloud Storage Skip the rows with errors

Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to a new dataset in Cloud Storage

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Question 252

An aerospace company uses a proprietary data format to store its night dat a. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible.

What should you do?

Use a standard Dataflow pipeline to store the raw data m BigQuery and then transform the format later when the data is used

Write a she script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source

Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format

Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format

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Question 253

What should you do?

Use a standard Dataflow pipeline to store the raw data in BigQuery and then transform the format later when the data is used.

Write a shell script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source

Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format

Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format

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Question 254

You are using BigQuery and Data Studio to design a customer-facing dashboard that displays large quantities of aggregated dat a. You expect a high volume of concurrent users. You need to optimize tie dashboard to provide quick visualizations with minimal latency. What should you do?

Use BigQuery BI Engine with materialized views

Use BigQuery BI Engine with streaming data.

Use BigQuery Bl Engine with authorized views

Use BigQuery Bl Engine with logical reviews

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Question 255

You need ads data to serve Al models and historical data tor analytics longtail and outlier data points need to be identified You want to cleanse the data n near-reel time before running it through Al models What should you do?

Use BigQuery to ingest prepare and then analyze the data and then run queries to create views

Use Cloud Storage as a data warehouse shell scripts tor processing, and BigQuery to create views tor desired datasets

Use Dataflow to identity longtail and outber data points programmatically with BigQuery as a sink

Use Cloud Composer to identify longtail and outlier data points, and then output a usable dataset to BigQuery

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Question 256

The Development and External teams nave the project viewer Identity and Access Management (1AM) role m a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?

Remove Cloud Storage IAM permissions to the External Team on the acme-raw-data project

Create Virtual Private Cloud (VPC) firewall rules on the acme-raw-data protect that deny all Ingress traffic from the External Team CIDR range

Create a VPC Service Controls perimeter containing both protects and BigQuery as a restricted API Add the External Team users to the perimeter s Access Level

Create a VPC Service Controls perimeter containing both protects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter's Access Level

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Question 257

A TensorFlow machine learning model on Compute Engine virtual machines (n2-standard -32) takes two days to complete framing. The model has custom TensorFlow operations that must run partially on a CPU You want to reduce the training time in a cost-effective manner. What should you do?

Change the VM type to n2-highmem-32

Change the VM type to e2 standard-32

Train the model using a VM with a GPU hardware accelerator

Train the model using a VM with a TPU hardware accelerator

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Question 258

An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and cat the company's Python API to execute trades. You need to efficiently implement a solution. What should you do?

Use Cloud Composer to subscribe to a Pub/Sub tope and can the Python API.

Use a Pub/Sub push subscription to trigger a Cloud Function to pass the data to tie Python API.

Write an application that makes a queue in a NoSQL database

Write an application hosted on a Compute Engine instance that makes a push subscription to the Pub/Sub topic

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Question 259

You are designing a pipeline that publishes application events to a Pub/Sub topic. You need to aggregate events across hourly intervals before loading the results to BigQuery for analysis. Your solution must be scalable so it can process and load large volumes of events to BigQuery. What should you do?

Create a streaming Dataflow job to continually read from the Pub/Sub topic and perform the necessary aggregations using tumbling windows

Schedule a batch Dataflow job to run hourly, pulling all available messages from the Pub-Sub topic and performing the necessary aggregations

Schedule a Cloud Function to run hourly, pulling all avertable messages from the Pub/Sub topic and performing the necessary aggregations

Create a Cloud Function to perform the necessary data processing that executes using the Pub/Sub trigger every time a new message is published to the topic.

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Question 260

Your company is migrating its on-premises data warehousing solution to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply daily updates from transactional database sources Your company wants to use BigQuery to improve its handling of CDC and to optimize the performance of the data warehouse Source system changes must be available for query m near-real time using tog-based CDC streams You need to ensure that changes in the BigQuery reporting table are available with minimal latency and reduced overhead. What should you do?

Choose 2 answers

Perform a DML INSERT UPDATE, or DELETE to replicate each CDC record in the reporting table m real time.

Periodically DELETE outdated records from the reporting tablePeriodically use a DML MERGE to simultaneously perform DML INSERT. UPDATE, and DELETE operations in the reporting table

Insert each new CDC record and corresponding operation type into a staging table in real time

Insert each new CDC record and corresponding operation type into the reporting table in real time and use a materialized view to expose only the current version of each unique record.

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