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Free Professional-Data-Engineer Sample Questions and 100% Cover Real Exam Questions (Updated 386 Questions)
Google Professional-Data-Engineer certification exam is a comprehensive assessment of an individual’s skills and knowledge in designing, building, and managing data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification validates an individual's ability to use Google Cloud technologies to solve complex data processing challenges and create data-driven solutions.
NEW QUESTION # 137
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?
- A. A non-sequential numeric ID
- B. A sequential numeric ID
- C. A stock symbol followed by a timestamp
- D. A timestamp followed by a stock symbol
Answer: B,D
Explanation:
...using a timestamp as the first element of a row key can cause a variety of problems. In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting. Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema- design] Reference: https://cloud.google.com/bigtable/docs/schema-design-time- series#ensure_that_your_row_key_avoids_hotspotting
NEW QUESTION # 138
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world.
The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure.
We also need environments in which our data scientists can carefully study and quickly adapt our models.
Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last
2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?
- A. Rowkey: date
Column data: device_id, data_point - B. Rowkey: date#data_point
Column data: device_id - C. Rowkey: data_point
Column data: device_id, date - D. Rowkey: device_id
Column data: date, data_point - E. Rowkey: date#device_id
Column data: data_point
Answer: C
NEW QUESTION # 139
You have several different unstructured data sources, within your on-premises data center as well as in the cloud. The data is in various formats, such as Apache Parquet and CSV. You want to centralize this data in Cloud Storage. You need to set up an object sink for your data that allows you to use your own encryption keys. You want to use a GUI-based solution. What should you do?
- A. Use Dataflow to move files into Cloud Storage.
- B. Use Storage Transfer Service to move files into Cloud Storage.
- C. Use Cloud Data Fusion to move files into Cloud Storage.
- D. Use BigQuery Data Transfer Service to move files into BigQuery.
Answer: C
Explanation:
To centralize unstructured data from various sources into Cloud Storage using a GUI-based solution while allowing the use of your own encryption keys, Cloud Data Fusion is the most suitable option. Here's why:
Cloud Data Fusion:
Cloud Data Fusion is a fully managed, cloud-native data integration service that helps in building and managing ETL pipelines with a visual interface.
It supports a wide range of data sources and formats, including Apache Parquet and CSV, and provides a user-friendly GUI for pipeline creation and management.
Custom Encryption Keys:
Cloud Data Fusion allows the use of customer-managed encryption keys (CMEK) for data encryption, ensuring that your data is securely stored according to your encryption policies.
Centralizing Data:
Cloud Data Fusion simplifies the process of moving data from on-premises and cloud sources into Cloud Storage, providing a centralized repository for your unstructured data.
Steps to Implement:
Set Up Cloud Data Fusion:
Deploy a Cloud Data Fusion instance and configure it to connect to your various data sources.
Create ETL Pipelines:
Use the GUI to create data pipelines that extract data from your sources and load it into Cloud Storage. Configure the pipelines to use your custom encryption keys.
Run and Monitor Pipelines:
Execute the pipelines and monitor their performance and data movement through the Cloud Data Fusion dashboard.
Reference:
Cloud Data Fusion Documentation
Using Customer-Managed Encryption Keys (CMEK)
NEW QUESTION # 140
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?
- A. Create groups for your users and give those groups access to the dataset
- B. Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset
- C. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
- D. 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
Answer: B
NEW QUESTION # 141
You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company's mobile app You have reviewed old chat logs and lagged each conversation for intent based on each customer's stated intention for contacting customer service About 70% of customer requests are simple requests that are solved within 10 intents The remaining 30% of inquiries require much longer, more complicated requests Which intents should you automate first?
- A. Automate intents in places where common words such as "payment" appear only once so the software isn't confused
- B. Automate the more complicated requests first because those require more of the agents' time
- C. Automate a blend of the shortest and longest intents to be representative of all intents
- D. Automate the 10 intents that cover 70% of the requests so that live agents can handle more complicated requests
Answer: D
NEW QUESTION # 142
Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was
previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the
recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways
the development team could increase the responsiveness of the analytics without increasing costs. What
should you recommend they do?
- A. Increase the size of the Hadoop cluster.
- B. Rewrite the job in Pig.
- C. Decrease the size of the Hadoop cluster but also rewrite the job in Hive.
- D. Rewrite the job in Apache Spark.
Answer: B
NEW QUESTION # 143
Your business users need a way to clean and prepare data before using the data for analysis. Your business users are less technically savvy and prefer to work with graphical user interfaces to define their transformations. After the data has been transformed, the business users want to perform their analysis directly in a spreadsheet. You need to recommend a solution that they can use. What should you do?
- A. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Connected Sheets.
- B. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.
- C. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.
- D. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Looker Studio.
Answer: A
Explanation:
For business users who are less technically savvy and prefer graphical user interfaces, Dataprep is an ideal tool for cleaning and preparing data, as it offers a user-friendly interface for defining data transformations without the need for coding. Once the data is cleaned and prepared, writing the results to BigQuery allows for the storage and management of large datasets. Analyzing the data using Connected Sheets enables business users to work within the familiar environment of a spreadsheet, leveraging the power of BigQuery directly within Google Sheets. This solution aligns with the needs of the users and follows Google's recommended practices for data cleaning, preparation, and analysis.
References:
Connected Sheets | Google Sheets | Google for Developers
Professional Data Engineer Certification Exam Guide | Learn - Google Cloud Engineer Data in Google Cloud | Google Cloud Skills Boost - Qwiklabs
NEW QUESTION # 144
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?
- A. Create groups for your users and give those groups access to the dataset
- B. Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset
- C. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
- D. 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
Answer: B
Explanation:
Explanation
NEW QUESTION # 145
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?
- A. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
- B. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
- C. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
- D. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
Answer: D
Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance
NEW QUESTION # 146
You have a BigQuery table that contains customer data, including sensitive information such as names and addresses. You need to share the customer data with your data analytics and consumer support teams securely.
The data analytics team needs to access the data of all the customers, but must not be able to access the sensitive data. The consumer support team needs access to all data columns, but must not be able to access customers that no longer have active contracts. You enforced these requirements by using an authorized dataset and policy tags After implementing these steps, the data analytics team reports that they still have access to the sensitive columns. You need to ensure that the data analytics team does not have access to restricted data What should you do?
Choose 2 answers
- A. Enforce access control in the policy tag taxonomy.
- B. Remove the bigquery. dataViewer role from the data analytics team on the authorized datasets.
- C. Replace the authorized dataset with an authorized view Use row-level security and apply filter_ expression to limit data access.
- D. Create two separate authorized datasets; one for the data analytics team and another for the consumer support team.
- E. Ensure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags.
Answer: A,E
Explanation:
To ensure that the data analytics team does not have access to sensitive columns, you should:
* B. Ensure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags. This role allows users to read metadata for data assets that have policy tags applied, which could include sensitive information.
* C. Enforce access control in the policy tag taxonomy. By setting access control at the policy tag level, you can restrict access to specific columns within a dataset, ensuring that only authorized users can view sensitive data.
NEW QUESTION # 147
Case Study: 3,
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost. Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments ?development/test, staging, and production ?
to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community. Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?
- A. The zone
- B. The disk size per worker
- C. The maximum number of workers
- D. The number of workers
Answer: A
NEW QUESTION # 148
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. Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.
- B. Store the social media posts and the data extracted from the API in BigQuery.
- C. Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.
- D. Store the social media posts and the data extracted from the API in Cloud SQL.
Answer: A
NEW QUESTION # 149
In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.
- A. SSH tunnel
- B. FTP connection
- C. VPN connection
- D. Special browser
Answer: A
Explanation:
To connect to the web interfaces, it is recommended to use an SSH tunnel to create a secure connection to the master node.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web- interfaces#connecting_to_the_web_interfaces
NEW QUESTION # 150
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes.
The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.
You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)
- A. MongoDB
- B. MySQL
- C. Cassandra
- D. Redis
- E. HDFS with Hive
- F. HBase
Answer: A,E,F
NEW QUESTION # 151
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. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.
- B. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
- C. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.
- D. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
Answer: B
Explanation:
Avro is compressed format and dataflow for parallel pipeline and bigquery for storage.
NEW QUESTION # 152
Which of the following statements is NOT true regarding Bigtable access roles?
- A. To give a user access to only one table in a project, you must configure access through your application.
- B. To give a user access to only one table in a project, grant the user the Bigtable Editor role for
that table. - C. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
- D. You can configure access control only at the project level.
Answer: B
Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances.
Read from and write to any table within the project, and manage instances.
NEW QUESTION # 153
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. 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. - D. Change the data pipeline to use BigQuery for storing stock trades, and update your application.
Answer: B
Explanation:
Timestamp at starting of rowkey causes bottleneck issues.
NEW QUESTION # 154
Different teams in your organization store customer and performance data in BigOuery. Each team needs to keep full control of their collected data, be able to query data within their projects, and be able to exchange their data with other teams. You need to implement an organization-wide solution, while minimizing operational tasks and costs. What should you do?
- A. Enable each team to create materialized views of the data they need to access in their projects.
- B. Ask each team to publish their data in Analytics Hub. Direct the other teams to subscribe to them.
- C. Ask each team to create authorized views of their data. Grant the biquery. jobUser role to each team.
- D. Create a BigQuery scheduled query to replicate all customer data into team projects.
Answer: B
Explanation:
To enable different teams to manage their own data while allowing data exchange across the organization, using Analytics Hub is the best approach. Here's why option C is the best choice:
Analytics Hub:
Analytics Hub allows teams to publish their data as data exchanges, making it easy for other teams to discover and subscribe to the data they need.
This approach maintains each team's control over their data while facilitating easy and secure data sharing across the organization.
Data Publishing and Subscribing:
Teams can publish datasets they control, allowing them to manage access and updates independently.
Other teams can subscribe to these published datasets, ensuring they have access to the latest data without duplicating efforts.
Minimized Operational Tasks and Costs:
This method reduces the need for complex replication or data synchronization processes, minimizing operational overhead.
By centralizing data sharing through Analytics Hub, it also reduces storage costs associated with duplicating large datasets.
Steps to Implement:
Set Up Analytics Hub:
Enable Analytics Hub in your Google Cloud project.
Provide training to teams on how to publish and subscribe to data exchanges.
Publish Data:
Each team publishes their datasets in Analytics Hub, configuring access controls and metadata as needed.
Subscribe to Data:
Teams that need access to data from other teams can subscribe to the relevant data exchanges, ensuring they always have up-to-date data.
Reference:
Analytics Hub Documentation
Publishing Data in Analytics Hub
Subscribing to Data in Analytics Hub
NEW QUESTION # 155
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 non- key columns. What should you do?
- A. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
- B. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
- C. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
- D. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
Answer: D
Explanation:
Explanation/Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform
NEW QUESTION # 156
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 2 answers.
- A. 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.
- B. Use Cloud Deployment Manager to automate access provision.
- C. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- D. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
- E. Introduce resource hierarchy to leverage access control policy inheritance.
Answer: B,C
Explanation:
Explanation/Reference:
NEW QUESTION # 157
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