| Exam Code | Professional-Data-Engineer |
| Exam Name | Google Professional Data Engineer Exam |
| Questions | 330 Questions Answers With Explanation |
| Update Date | July 15,2024 |
| Price |
Was : |
Prepare Yourself Expertly for Professional-Data-Engineer Exam:
Our team of highly skilled and experienced professionals is dedicated to delivering up-to-date and precise study materials in PDF format to our customers. We deeply value both your time and financial investment, and we have spared no effort to provide you with the highest quality work. We ensure that our students consistently achieve a score of more than 95% in the Google Professional-Data-Engineer exam. You provide only authentic and reliable study material. Our team of professionals is always working very keenly to keep the material updated. Hence, they communicate to the students quickly if there is any change in the Professional-Data-Engineer dumps file. The Google Professional-Data-Engineer exam question answers and Professional-Data-Engineer dumps we offer are as genuine as studying the actual exam content.
You can reach out to our agents at any time for guidance; we are available 24/7. Our agent will provide you information you need; you can ask them any questions you have. We are here to provide you with a complete study material file you need to pass your Professional-Data-Engineer exam with extraordinary marks.
Pass4surexams provide trusted study material. If you want to meet a sweeping success in your exam you must sign up for the complete preparation at Pass4surexams and we will provide you with such genuine material that will help you succeed with distinction. Our experts work tirelessly for our customers, ensuring a seamless journey to passing the Google Professional-Data-Engineer exam on the first attempt. We have already helped a lot of students to ace IT certification exams with our genuine Professional-Data-Engineer Exam Question Answers. Don't wait and join us today to collect your favorite certification exam study material and get your dream job quickly.
Enroll with confidence at Pass4surexams, and not only will you access our comprehensive Google Professional-Data-Engineer exam question answers and dumps, but you will also benefit from a remarkable offer – 90 days of free updates. In the dynamic landscape of certification exams, our commitment to your success doesn't waver. If there are any changes or updates to the Google Professional-Data-Engineer exam content during the 90-day period, rest assured that our team will promptly notify you and provide the latest study materials, ensuring you are thoroughly prepared for success in your exam."
Quality is the heart of our service that's why we offer our students real exam questions with 100% passing assurance in the first attempt. Our Professional-Data-Engineer dumps PDF have been carved by the experienced experts exactly on the model of real exam question answers in which you are going to appear to get your certification.
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query – -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?
A. Create a separate table for each ID.
B. Use the LIMIT keyword to reduce the number of rows returned.
C. Recreate the table with a partitioning column and clustering column.
D. Use the bq query - -maximum_bytes_billed flag to restrict the number of bytes billed.
You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants. What should you do?
A. Increase the size of the dataset by collecting additional data.
B. Train a linear regression to predict a credit default risk score.
C. Remove the bias from the data and collect applications that have been declined loans.
D. Match loan applicants with their social profiles to enable feature engineering
You’ve migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average 200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you’d like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you’d like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload. What should you do?
A. Increase the size of your parquet files to ensure them to be 1 GB minimum.
B. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.
C. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
D. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)
A. Configure your Cloud Dataflow pipeline to use local execution
B. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
C. Increase the number of nodes in the Cloud Bigtable cluster
D. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
E. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
Your neural network model is taking days to train. You want to increase the training speed. What can you do?
A. Subsample your test dataset.
B. Subsample your training dataset.
C. Increase the number of input features to your model.
D. Increase the number of layers in your neural network.
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
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 query. Share the dataset that contains the view with the application service account.
C. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.
D. Create a Cloud Dataflow pipeline using BigQueryIO to 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.
You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?
A. BigQuery
B. Cloud Bigtable
C. Cloud Datastore
D. Cloud SQL for PostgreSQL
You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once, and must be ordered within windows of 1 hour. How should you design the solution?
A. Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.
B. Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.
C. Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.
D. Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.
You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?
A. Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the
Cloud Bigtable data.
B. Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.
C. Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.
D. Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Data flow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?
A. Add a SideInput that returns a Boolean if the element is corrupt.
B. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
C. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
D. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
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
You’re using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes. You’ve recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload. What should you do?
A. Export Bigtable dump to GCS and run your analytical job on top of the exported files.
B. Add a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
C. Add a second cluster to an existing instance with a single-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
D. Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.
Your financial services company is moving to cloud technology and wants to store 50 TB of financial timeseries data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data. Which product should they use to store the data?
A. Cloud Bigtable
B. Google BigQuery
C. Google Cloud Storage
D. Google Cloud Datastore
You have an Apache Kafka Cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins. What should you do?
A. Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to
the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to
GCS.
B. Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.
C. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Source connector. Use a Dataflow job to read fron PubSub and write to GCS.
D. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read fron PubSub and write to GCS.
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?
A. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour
every 5 minutes. Compute the average when the window closes, and send an alert if the average is less
than 4000 messages.
B. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour.
Compute the average when the window closes, and send an alert if the average is less than 4000 messages
C. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow
template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a
script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that
number falls below 4000, send an alert
D. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow
template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script
every five minutes that counts the number of rows created in BigQuery in the last hour. If that number
falls below 4000, send an alert.
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. Integrate with a single sign-on (SSO) platform, and pass each user’s credentials along with the query request
C. Create a service account and grant dataset access to that account. Use the service account’s private key to access the dataset
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
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.
A. Denormalize the data as must as possible.
B. Preserve the structure of the data as much as possible.
C. Use BigQuery UPDATE to further reduce the size of the dataset.
D. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
E. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery’s support for external data sources to query.
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?
A. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application.
B. Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the
models using Cloud Dataproc. Call the models from your application.
C. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user’s viewing history to generate preferences.
D. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user’s viewing history to generate preferences.
You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?
A. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
B. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
C. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
D. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
Vinod S
Jul 27, 2024
Good question set for practice!
Richard Joseph
Jul 26, 2024
I couldn't have passed the Professional-Data-Engineer exam without Pass4surexams authentic study material.
John
Jul 26, 2024
I gave the Google Professional-Data-Engineer test and studied through PASS4SUREXAMS as it has latest mock tests available which helped me score 90%.
Charles1989
Jul 25, 2024
You are really the best of best! Qualifying for Professional Data Engineer exam means that you have completed something most important in your IT career. Having this knowledge, I wanted to get this dream certification at all costs and Pass4surexams Q&As guide made it possible for me. I’m all praise for the wonderful study material for my success in Professional Data Engineer exam and suggest it to everyone here! It is was very helpful for me Thanks to Pass4surexams!
kumar roshan
Jul 25, 2024
Got the Professional Data Engineer questions easily. I am glad that I selected this website and get 92%. Great site
Robert John
Jul 24, 2024
Pass4surexams provides its Study material for the Google Professional Data Engineer in an extremely clear and comprehensive manner, in efforts of ensuring that all of his students understand the content fully within the given timeframe. The Practice tests recap the concepts multiple times to memorize all concepts clearly. I passed the exam by following their study content. Thank you so much for all your efforts.
Kenneth
Jul 24, 2024
One of my juniors passed the Professional Data Engineer exam and surprised everyone in the office. It not only enhanced the skills of our team but also put enormous pressure on me to get this exam cleared as well. Thanks to www.pass4surexams.com for providing such a fantastic Professional Data Engineer study guide to get through this exam in first attempt.
