Up To The Immediate Present Google Professional Data Engineer Exam Professional-Data-Engineer Latest Exam

NEW QUESTION 1

Your company has a hybrid cloud initiative. You have a complex data pipeline that moves data between cloud provider services and leverages services from each of the cloud providers. Which cloud-native service should you use to orchestrate the entire pipeline?

• A. Cloud Dataflow
• B. Cloud Composer
• C. Cloud Dataprep
• D. Cloud Dataproc

Answer: D

NEW QUESTION 2

You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?

• A. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
• B. Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
• C. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
• D. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.

Answer: D

NEW QUESTION 3

A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL ‘dataset.model’, table user_features). How should you create the ML pipeline?

• A. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
• B. Create an Authorized View with the provided quer
• C. Share the dataset that contains the view with the application service account.
• D. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the quer
• E. Grant the Dataflow Worker role to the application service account.
• F. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.Write the results to Cloud Bigtable using BigtableI
• G. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.

Answer: D

NEW QUESTION 4

To give a user read permission for only the first three columns of a table, which access control method would you use?

• A. Primitive role
• B. Predefined role
• C. Authorized view
• D. It's not possible to give access to only the first three columns of a table.

Answer: C

Explanation:
An authorized view allows you to share query results with particular users and groups without giving them
read access to the underlying tables. Authorized views can only be created in a dataset that does not contain the tables queried by the view.
When you create an authorized view, you use the view's SQL query to restrict access to only the rows and columns you want the users to see.
Reference: https://cloud.google.com/bigquery/docs/views#authorized-views

NEW QUESTION 5

You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

• A. Make a call to the Stackdriver API to list all logs, and apply an advanced filter.
• B. In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.
• C. In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.
• D. Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

Answer: B

NEW QUESTION 6

Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

• A. Preemptible workers cannot use persistent disk.
• B. Preemptible workers cannot store data.
• C. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
• D. A Dataproc cluster cannot have only preemptible workers.

Answer: BD

Explanation:
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster: Processing only—Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters—To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size—As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits. Reference: https://cloud.google.com/dataproc/docs/concepts/preemptible-vms

NEW QUESTION 7

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?

• A. Transform text files to compressed Avro using Cloud Dataflo
• B. Use BigQuery for storage and query.
• C. Transform text files to compressed Avro using Cloud Dataflo
• D. Use Cloud Storage and BigQuerypermanent linked tables for query.
• E. Compress text files to gzip using the Grid Computing Tool
• F. Use BigQuery for storage and query.
• G. Compress text files to gzip using the Grid Computing Tool
• H. Use Cloud Storage, and then import into Cloud Bigtable for query.

Answer: D

NEW QUESTION 8

You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? Choose 2 answers.

• A. Publisher throughput quota is too small.
• B. Total outstanding messages exceed the 10-MB maximum.
• C. Error handling in the subscriber code is not handling run-time errors properly.
• D. The subscriber code cannot keep up with the messages.
• E. The subscriber code does not acknowledge the messages that it pulls.

Answer: CD

NEW QUESTION 9

You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps.
You have the following requirements:
You will batch-load the posts once per day and run them through the Cloud Natural Language API.
You will extract topics and sentiment from the posts.
You must store the raw posts for archiving and reprocessing.
You will create dashboards to be shared with people both inside and outside your organization.
You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?

• A. Store the social media posts and the data extracted from the API in BigQuery.
• B. Store the social media posts and the data extracted from the API in Cloud SQL.
• C. Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.
• D. Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.

Answer: D

NEW QUESTION 10

What are two of the benefits of using denormalized data structures in BigQuery?

• A. Reduces the amount of data processed, reduces the amount of storage required
• B. Increases query speed, makes queries simpler
• C. Reduces the amount of storage required, increases query speed
• D. Reduces the amount of data processed, increases query speed

Answer: B

Explanation:
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data
structure, you don't have to use JOINs, since all of the data has been combined into one table. Denormalization also makes queries simpler because you do not have to use JOIN clauses.
Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
Reference:
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

NEW QUESTION 11

Google Cloud Bigtable indexes a single value in each row. This value is called the .

• A. primary key
• B. unique key
• C. row key
• D. master key

Answer: C

Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION 12

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-to-date YouTube channels log data. How should you set up the log data transfer into Google Cloud?

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

Answer: B

NEW QUESTION 13

You store historic data in Cloud Storage. You need to perform analytics on the historic data. You want to use a solution to detect invalid data entries and perform data transformations that will not require programming or knowledge of SQL.
What should you do?

• A. Use Cloud Dataflow with Beam to detect errors and perform transformations.
• B. Use Cloud Dataprep with recipes to detect errors and perform transformations.
• C. Use Cloud Dataproc with a Hadoop job to detect errors and perform transformations.
• D. Use federated tables in BigQuery with queries to detect errors and perform transformations.

Answer: A

NEW QUESTION 14

You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

• A. Cloud Speech-to-Text API
• B. Cloud Natural Language API
• C. Dialogflow Enterprise Edition
• D. Cloud AutoML Natural Language

Answer: D

NEW QUESTION 15

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 data. 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.)

• A. Create a new view over events using standard SQL
• B. Create a new partitioned table using a standard SQL query
• C. Create a new view over events_partitioned using standard SQL
• D. Create a service account for the ODBC connection to use for authentication
• E. Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connectionand shared “events”

Answer: AE

NEW QUESTION 16

You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

• A. Cloud Scheduler
• B. Cloud Dataflow
• C. Cloud Functions
• D. Cloud Composer

Answer: A

NEW QUESTION 17

You need to compose visualization for operations teams with the following requirements:
Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
The report must not be more than 3 hours delayed from live data.
The actionable report should only show suboptimal links.
Most suboptimal links should be sorted to the top.
Suboptimal links can be grouped and filtered by regional geography.
User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

• A. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
• B. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
• C. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
• D. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.

Answer: B

NEW QUESTION 18

You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?

• A. Store and process the entire dataset in BigQuery.
• B. Store and process the entire dataset in Cloud Bigtable.
• C. Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
• D. Store the warm data as files in Cloud Storage, and store the active data in BigQuer
• E. Keep this ratio as 80% warm and 20% active.

Answer: D

NEW QUESTION 19

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?

• A. Convert your batch BQ queries into interactive BQ queries.
• B. Create an additional project to overcome the 2K on-demand per-project quota.
• C. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
• D. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.

Answer: C

Explanation:
Reference https://cloud.google.com/blog/products/gcp/busting-12-myths-about-bigquery

NEW QUESTION 20

You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

• A. ParDo
• B. Sink API
• C. Source API
• D. Data extraction

Answer: A

Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do

NEW QUESTION 21

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

• A. Linear regression
• B. Logistic classification
• C. Recurrent neural network
• D. Feedforward neural network

Answer: A

NEW QUESTION 22

Which of the following is NOT true about Dataflow pipelines?

• A. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
• B. Dataflow pipelines can consume data from other Google Cloud services
• C. Dataflow pipelines can be programmed in Java
• D. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

Answer: A

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs
Reference: https://cloud.google.com/dataflow/

NEW QUESTION 23

You have data pipelines running on BigQuery, Cloud Dataflow, and Cloud Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products of features of the platform. What should you do?

• A. Export the information to Cloud Stackdriver, and set up an Alerting policy
• B. Run a Virtual Machine in Compute Engine with Airflow, and export the information to Stackdriver
• C. Export the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs
• D. Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs

Answer: B

NEW QUESTION 24

You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

• A. Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
• B. Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.
• C. Change the data pipeline to use BigQuery for storing stock trades, and update your application.
• D. Use Cloud Dataflow to write summary of each day’s stock trades to an Avro file on Cloud Storage.Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.

Answer: A

NEW QUESTION 25
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