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Career Path
Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.
NEW QUESTION # 29
Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the dat
a. Which three machine learning applications can you use? (Choose three.)
- A. Unsupervised learning to determine which transactions are most likely to be fraudulent.
- B. Supervised learning to predict the location of a transaction.
- C. Clustering to divide the transactions into N categories based on feature similarity.
- D. Unsupervised learning to predict the location of a transaction.
- E. Supervised learning to determine which transactions are most likely to be fraudulent.
- F. Reinforcement learning to predict the location of a transaction.
Answer: A,B,C
NEW QUESTION # 30
You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the "Trust No One" (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your data. What should you do?
- A. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encryptto encrypt each archival file with the key. Use gsutil cpto upload each encrypted file to the Cloud Storage bucket.
Manually destroy the key previously used for encryption, and rotate the key once. - B. Use gcloud kms keys createto create a symmetric key. Then use gcloud kms encryptto encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.
- C. Specify customer-supplied encryption key (CSEK) in the .botoconfiguration file. Use gsutil cpto upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.
- D. Specify customer-supplied encryption key (CSEK) in the .botoconfiguration file. Use gsutil cpto upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.
Answer: A
Explanation:
Explanation/Reference:
NEW QUESTION # 31
Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl.
being able to reprocess all failing data).
What should you do?
- A. Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.
- B. Add a try... catch block to your DoFn that transforms the data, extract erroneous rows from logs.
- C. Add a try... catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.
- D. Add a try... catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.
Answer: D
Explanation:
https://cloud.google.com/blog/products/gcp/handling-invalid-inputs-in-dataflow
NEW QUESTION # 32
If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?
- A. Clustering estimator
- B. Unsupervised learning
- C. Classifier
- D. Regressor
Answer: D
Explanation:
Explanation
Regression is the supervised learning task for modeling and predicting continuous, numeric variables.
Examples include predicting real-estate prices, stock price movements, or student test scores.
Classification is the supervised learning task for modeling and predicting categorical variables. Examples include predicting employee churn, email spam, financial fraud, or student letter grades.
Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis.
Reference: https://elitedatascience.com/machine-learning-algorithms
NEW QUESTION # 33
You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?
- A. Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
- B. Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.
- C. Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.
- D. Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.
Answer: B
NEW QUESTION # 34
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?
- A. Specify the TableReference object in the code.
- B. Use .fromQuery operation to read specific fields from the table.
- C. Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.
- D. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
Answer: C
NEW QUESTION # 35
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. Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs
- B. 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
- C. Export the information to Cloud Stackdriver, and set up an Alerting policy
- D. Run a Virtual Machine in Compute Engine with Airflow, and export the information to Stackdriver
Answer: D
Explanation:
Explanation/Reference:
NEW QUESTION # 36
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?
- A. Use Cloud Vision AutoML, but reduce your dataset twice.
- B. Use Cloud Vision AutoML with the existing dataset.
- C. Use Cloud Vision API by providing custom labels as recognition hints.
- D. Train your own image recognition model leveraging transfer learning techniques.
Answer: B
NEW QUESTION # 37
Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery. Which three approaches can you take? (Choose three.)
- A. Restrict BigQuery API access to approved users.
- B. Ensure that the data is encrypted at all times.
- C. Segregate data across multiple tables or databases.
- D. Restrict access to tables by role.
- E. Use Google Stackdriver Audit Logging to determine policy violations.
- F. Disable writes to certain tables.
Answer: A,D,E
NEW QUESTION # 38
If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?
- A. Clustering estimator
- B. Unsupervised learning
- C. Classifier
- D. Regressor
Answer: D
Explanation:
Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Examples include predicting real-estate prices, stock price movements, or student test scores.
Classification is the supervised learning task for modeling and predicting categorical variables. Examples include predicting employee churn, email spam, financial fraud, or student letter grades.
Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis.
Reference: https://elitedatascience.com/machine-learning-algorithms
NEW QUESTION # 39
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?
- 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
NEW QUESTION # 40
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?
- A. Cloud Bigtable
- B. Cloud Datastore
- C. Cloud SQL
- D. BigQuery
Answer: D
NEW QUESTION # 41
You're training a model to predict housing prices based on an available dataset with real estate properties.
Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?
- A. Create a numeric column from a feature cross of latitude and longtitude.
- B. Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.
- C. Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.
- D. Provide latitude and longtitude as input vectors to your neural net.
Answer: A
Explanation:
Explanation
Explanation/Reference:
Reference https://cloud.google.com/bigquery/docs/gis-data
NEW QUESTION # 42
The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?
- A. Masters, workers, and parameter servers
- B. Workers
- C. Workers and parameter servers
- D. Parameter servers
Answer: C
Explanation:
Explanation
The CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines:
You must set TrainingInput.masterType to specify the type of machine to use for your master node.
You may set TrainingInput.workerCount to specify the number of workers to use.
You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use.
You can specify the type of machine for the master node, but you can't specify more than one master node.
Reference: https://cloud.google.com/ml-engine/docs/training-overview#job_configuration_parameters
NEW QUESTION # 43
Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?
- A. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
- B. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.
- C. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.
- D. Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.
Answer: A
Explanation:
From Cloud SQL we can fetch the record on timestamp basis using where clause and it satisfies near real time.
NEW QUESTION # 44
Which of these operations can you perform from the BigQuery Web UI?
- A. Upload a 20 MB file.
- B. Upload multiple files using a wildcard.
- C. Upload a file in SQL format.
- D. Load data with nested and repeated fields.
Answer: D
Explanation:
Explanation
You can load data with nested and repeated fields using the Web UI.
You cannot use the Web UI to:
- Upload a file greater than 10 MB in size
- Upload multiple files at the same time
- Upload a file in SQL format
All three of the above operations can be performed using the "bq" command.
Reference: https://cloud.google.com/bigquery/loading-data
NEW QUESTION # 45
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster
____.
- A. conditional node
- B. application node
- C. worker node
- D. master node
Answer: D
Explanation:
Explanation
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m".
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces
NEW QUESTION # 46
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?
- A. Restrict the Google Cloud Storage bucket so only you can see the files
- B. Grant the Project Owner role to a service account, and run the job with it
- C. Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files
and write to BigQuery - D. Use a service account with the ability to read the batch files and to write to BigQuery
Answer: B
NEW QUESTION # 47
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?
- A. Sessions
- B. Windows
- C. OutputCriteria
- D. Triggers
Answer: D
Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
NEW QUESTION # 48
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?
- A. 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.
- B. 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.
- C. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
- D. 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.
Answer: C
NEW QUESTION # 49
Does Dataflow process batch data pipelines or streaming data pipelines?
- A. Both Batch and Streaming Data Pipelines
- B. Only Streaming Data Pipelines
- C. None of the above
- D. Only Batch Data Pipelines
Answer: A
Explanation:
Explanation
Dataflow is a unified processing model, and can execute both streaming and batch data pipelines Reference: https://cloud.google.com/dataflow/
NEW QUESTION # 50
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The Google Professional-Data-Engineer exam is designed for data engineers, data analysts, and data scientists who work with data processing systems and data analytics solutions on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification demonstrates their ability to design, build, and manage scalable, reliable, and cost-effective data solutions on Google Cloud.
Q&As with Explanations Verified & Correct Answers: https://vcetorrent.passreview.com/Professional-Data-Engineer-exam-questions.html