Google Certified Professional-Data-Engineer Dumps Questions Valid Professional-Data-Engineer Materials [Q131-Q155]

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Google Certified Professional-Data-Engineer Dumps Questions Valid Professional-Data-Engineer Materials

Current Professional-Data-Engineer Exam Dumps [2024] Complete Google Exam Smoothly

Google Professional-Data-Engineer certification is a highly respected credential that validates the knowledge and skills of professionals working in the field of data engineering. Google Certified Professional Data Engineer Exam certification is designed to test the ability of candidates to design, build, operate, and manage data processing systems that are scalable, secure, and reliable. Google Certified Professional Data Engineer Exam certification exam is conducted by Google and is intended for individuals who have a good understanding of the Google Cloud Platform and data engineering best practices.

QUESTION 131
You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors.
You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

Deploy small Kafka clusters in your data centers to buffer events.

Have the data acquisition devices publish data to Cloud Pub/Sub.

Establish a Cloud Interconnect between all remote data centers and Google.

Write a Cloud Dataflow pipeline that aggregates all data in session windows.

QUESTION 132
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.

QUESTION 133
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

QUESTION 134
Your company’s data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?

1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.
2. Load the booking and user profile data into a BigQuery table.

1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type.
2. Load the booking and user profile data into a BigQuery table.

1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the email mask as the data masking rule.
3. Assign the policy to the email field in both tables. A
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.

1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the default masking value as the data masking rule.
3. Assign the policy to the email field in both tables.
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts

QUESTION 135
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?

Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.

Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.

Use the NOW () function in BigQuery to record the event’s time.

Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

QUESTION 136
Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

Field promotion

Randomization

Salting

Hashing

QUESTION 137
Your company’s customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?

Add a node to the MySQL cluster and build an OLAP cube there.

Use an ETL tool to load the data from MySQL into Google BigQuery.

Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.

Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.

QUESTION 138
The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?

Workers

Masters, workers, and parameter servers

Workers and parameter servers

Parameter servers

QUESTION 139
You are developing a software application using Google’s Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

PCollection

Transform

Pipeline

Sink API

QUESTION 140
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?

Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.

Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.

Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.

Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.

QUESTION 141
Which of these are examples of a value in a sparse vector? (Select 2 answers.)

[0, 5, 0, 0, 0, 0]

[0, 0, 0, 1, 0, 0, 1]

[0, 1]

[1, 0, 0, 0, 0, 0, 0]

QUESTION 142
Which of the following statements about Legacy SQL and Standard SQL is not true?

Standard SQL is the preferred query language for BigQuery.

If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.

One difference between the two query languages is how you specify fully-qualified table names (i.e.
table names that include their associated project name).

You need to set a query language for each dataset and the default is Standard SQL.

QUESTION 143
What are two of the characteristics of using online prediction rather than batch prediction?

It is optimized to handle a high volume of data instances in a job and to run more complex models.

Predictions are returned in the response message.

Predictions are written to output files in a Cloud Storage location that you specify.

It is optimized to minimize the latency of serving predictions.

QUESTION 144
As your organization expands its usage of GCP, many teams have started to create their own projects.
Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? (Choose two.)

Use Cloud Deployment Manager to automate access provision.

Introduce resource hierarchy to leverage access control policy inheritance.

Create distinct groups for various teams, and specify groups in Cloud IAM policies.

Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

QUESTION 145
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?

Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.

Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.

Create a Cloud Dataflow pipeline using BigQueryIOto read results from the query. Grant the Dataflow Worker role to the application service account.

Create a Cloud Dataflow pipeline using BigQueryIOto read predictions for all users from the query. Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.

QUESTION 146
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

An hourly watermark

An event time trigger

The with Allowed Lateness method

A processing time trigger

QUESTION 147
Which of the following are feature engineering techniques? (Select 2 answers)

Hidden feature layers

Feature prioritization

Crossed feature columns

Bucketization of a continuous feature

QUESTION 148
You are administering a BigQuery dataset that uses a customer-managed encryption key (CMEK). You need to share the dataset with a partner organization that does not have access to your CMEK. What should you do?

Create an authorized view that contains the CMEK to decrypt the data when accessed.

Provide the partner organization a copy of your CMEKs to decrypt the data.

Copy the tables you need to share to a dataset without CMEKs Create an Analytics Hub listing for this dataset.

Export the tables to parquet files to a Cloud Storage bucket and grant the storageinsights. viewer role on the bucket to the partner organization.

QUESTION 149
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time.
This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

Implement clustering in BigQuery on the ingest date column.

Implement clustering in BigQuery on the package-tracking ID column.

Tier older data onto Cloud Storage files, and leverage extended tables.

Re-create the table using data partitioning on the package delivery date.

QUESTION 150
Your company is running their first dynamic campaign, serving different offers by analyzing real-time data
during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every
hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and
collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable.
The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data.
They want to improve this performance while minimizing cost. What should they do?

Redefine the schema by evenly distributing reads and writes across the row space of the table.

The performance issue should be resolved over time as the site of the BigDate cluster is increased.

Redesign the schema to use a single row key to identify values that need to be updated frequently in
the cluster.

Redesign the schema to use row keys based on numeric IDs that increase sequentially per user
viewing the offers.

QUESTION 151
You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?

The current epoch time

A concatenation of the product name and the current epoch time

A random universally unique identifier number (version 4 UUID)

The original order identification number from the sales system, which is a monotonically increasing integer

QUESTION 152
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

Preemptible workers cannot use persistent disk.

Preemptible workers cannot store data.

If a preemptible worker is reclaimed, then a replacement worker must be added manually.

A Dataproc cluster cannot have only preemptible workers.

QUESTION 153
You are migrating your data warehouse to Google Cloud and decommissioning your on-premises data center Because this is a priority for your company, you know that bandwidth will be made available for the initial data load to the cloud. The files being transferred are not large in number, but each file is 90 GB Additionally, you want your transactional systems to continually update the warehouse on Google Cloud in real time What tools should you use to migrate the data and ensure that it continues to write to your warehouse?

Storage Transfer Service for the migration, Pub/Sub and Cloud Data Fusion for the real-time updates

BigQuery Data Transfer Service for the migration, Pub/Sub and Dataproc for the real-time updates

gsutil for the migration; Pub/Sub and Dataflow for the real-time updates

gsutil for both the migration and the real-time updates

QUESTION 154
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

Cloud SQL

BigQuery

Cloud Bigtable

Cloud Datastore

QUESTION 155
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
Decoupling producer from consumer
Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
Near real-time SQL query
Maintain at least 2 years of historical data, which will be queried with SQ
Which pipeline should you use to meet these requirements?

Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.

Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.

Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.

Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.

Google Professional-Data-Engineer exam is a certification offered by Google to those who want to showcase their expertise in designing, building, and maintaining data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is designed for professionals who have a deep understanding of data engineering and can leverage Google Cloud technologies to create scalable and efficient data pipelines. Professional-Data-Engineer exam assesses the candidate’s ability to design data processing systems, build and operationalize data pipelines, and manage and monitor data processing infrastructure.

Exam Details

The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.

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