[Mar 13, 2025] Fully Updated Dumps PDF – Latest Professional-Data-Engineer Exam Questions and Answers [Q188-Q210]

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[Mar 13, 2025] Fully Updated Dumps PDF – Latest Professional-Data-Engineer Exam Questions and Answers [Q188-Q210]

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[Mar 13, 2025] Fully Updated Dumps PDF – Latest Professional-Data-Engineer Exam Questions and Answers

100% Free Professional-Data-Engineer Exam Dumps to Pass Exam Easily from 2Pass4sure

NEW QUESTION 188
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?

Rowkey: date#device_idColumn data: data_point

Rowkey: dateColumn data: device_id, data_point

Rowkey: device_idColumn data: date, data_point

Rowkey: data_pointColumn data: device_id, date

Rowkey: date#data_pointColumn data: device_id

NEW QUESTION 189
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat
a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have
asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

Increase the CPU size on your server.

Increase the size of the Google Persistent Disk on your server.

Increase your network bandwidth from your datacenter to GCP.

Increase your network bandwidth from Compute Engine to Cloud Storage.

NEW QUESTION 190
You have a network of 1000 sensors. The sensors generate time series data: one metric per sensor per second, along with a timestamp. You already have 1 TB of data, and expect the data to grow by 1 GB every day You need to access this data in two ways. The first access pattern requires retrieving the metric from one specific sensor stored at a specific timestamp, with a median single-digit millisecond latency. The second access pattern requires running complex analytic queries on the data, including joins, once a day. How should you store this data?

Store your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day.

Store your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key.

Store your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day.

Store your data in BigQuery. Use the metric as a primary key.

To store your data in a way that meets both access patterns, you should:
* A. Store your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day. This option allows you to leverage the high performance and scalability of Bigtable for low-latency point queries on sensor data, as well as the powerful analytics capabilities of BigQuery for complex queries on large datasets. By using the sensor ID and timestamp as the row key, you can ensure that your data is sorted and distributed evenly across Bigtable nodes, and that you can easily retrieve the metric for a specific sensor and time. By performing an export to BigQuery every day, you can transfer your data to a columnar storage format that is optimized for analytical queries, and take advantage of BigQuery’s features such as partitioning, clustering, and caching.
* B. Store your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key. This option is not optimal because BigQuery is not designed for low-latency point queries, and using a concatenated primary key may result in poor performance and high costs. BigQuery does not support primary keys natively, and you would have to use a unique constraint or a hash function to enforce uniqueness. Moreover, BigQuery charges by the amount of data scanned, so using a long and complex primary key may increase the query cost and complexity.
* C. Store your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day. This option is not optimal because using the sensor ID and metric as the row key may result in data skew and hotspots in Bigtable, as some sensors may generate more metrics than others, or some metrics may be more common than others. This may affect the performance and availability of Bigtable, as well as the efficiency of the export to BigQuery.
* D. Store your data in BigQuery. Use the metric as a primary key. This option is not optimal because using the metric as a primary key may result in data duplication and inconsistency in BigQuery, as multiple sensors may generate the same metric at different times, or the same sensor may generate different metrics at the same time. This may affect the accuracy and reliability of your analytical queries, as well as the query cost and complexity.

NEW QUESTION 191
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients’ personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

Create an authorized view in BigQuery to restrict access to tables with sensitive data.

Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.

Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.

Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

NEW QUESTION 192
Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?

Run a local version of Jupiter on the laptop.

Grant the user access to Google Cloud Shell.

Host a visualization tool on a VM on Google Compute Engine.

Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.

NEW QUESTION 193
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old.
What should you do?

Disable caching by editing the report settings.

Disable caching in BigQuery by editing table details.

Refresh your browser tab showing the visualizations.

Clear your browser history for the past hour then reload the tab showing the virtualizations.

Explanation
Reference https://support.google.com/datastudio/answer/7020039?hl=en

NEW QUESTION 194
You work for an advertising company, and you’ve developed a Spark ML model to predict click-through rates at advertisement blocks. You’ve been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be closing soon, so a rapid lift-and- shift migration is necessary. However, the data you’ve been using will be migrated to migrated to BigQuery.
You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

Use Cloud ML Engine for training existing Spark ML models

Rewrite your models on TensorFlow, and start using Cloud ML Engine

Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery

Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

NEW QUESTION 195
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

Create two separate authorized datasets; one for the data analytics team and another for the consumer support team.

Ensure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags.

Enforce access control in the policy tag taxonomy.

Remove the bigquery. dataViewer role from the data analytics team on the authorized datasets.

Replace the authorized dataset with an authorized view Use row-level security and apply filter_ expression to limit data access.

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 196
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?

Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google cp Cloud.

Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket.
Manually destroy the key previously used for encryption, and rotate the key once.

Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.

Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.

NEW QUESTION 197
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub
streaming data, one of the important business requirements is to be able to periodically identify the inputs
and their timings during their campaign. Engineers have decided to use windowing and transformation in
Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud
Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

They have not assigned the timestamp, which causes the job to fail

They have not set the triggers to accommodate the data coming in late, which causes the job to fail

They have not applied a global windowing function, which causes the job to fail when the pipeline is
created

They have not applied a non-global windowing function, which causes the job to fail when the pipeline
is created

NEW QUESTION 198
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 sequential numeric ID

A timestamp followed by a stock symbol

A non-sequential numeric ID

A stock symbol followed by a timestamp

…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 199
When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

Your gcloud does not have access to the BigQuery resources

BigQuery cannot be accessed from local machines

You are missing gcloud on your machine

Pipelines cannot be run locally

Explanation
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source/sink Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun

NEW QUESTION 200
The _________ for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

Cloud Dataflow connector

DataFlow SDK

BiqQuery API

BigQuery Data Transfer Service

The Cloud Dataflow connector for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline. You can use the connector for both batch and streaming operations.

NEW QUESTION 201
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Load the data every 30 minutes into a new partitioned table in BigQuery.

Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

NEW QUESTION 202
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing data. Which Stackdriver alerts should you create?

An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination

An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination

An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination

An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination

NEW QUESTION 203
Your company needs to upload their historic data to Cloud Storage. The security rules don’t allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?

Execute gsutil rsync from the on-premises servers.

Use Cloud Dataflow and write the data to Cloud Storage.

Write a job template in Cloud Dataproc to perform the data transfer.

Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.

NEW QUESTION 204
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 two.)

Publisher throughput quota is too small.

Total outstanding messages exceed the 10-MB maximum.

Error handling in the subscriber code is not handling run-time errors properly.

The subscriber code cannot keep up with the messages.

The subscriber code does not acknowledge the messages that it pulls.

C, E: By not acknowleding the pulled message, this result in it be putted back in Cloud Pub/Sub, meaning the messages accumulate instead of being consumed and removed from Pub/Sub. The same thing can happen ig the subscriber maintains the lease on the message it receives in case of an error. This reduces the overall rate of processing because messages get stuck on the first subscriber. Also, errors in Cloud Function do not show up in Stackdriver Log Viewer if they are not correctly handled.
A: No problem with publisher rate as the observed result is a higher number of messages and not a lower number.
B: if messages exceed the 10MB maximum, they cannot be published.
D: Cloud Functions automatically scales so they should be able to keep up.

NEW QUESTION 205
You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow You want to improve the performance of these queries What should you do?

Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.

Create an individual external table for each Hive partition by using a common table name prefix Use wildcard table queries to reference the partitioned data.

Change the storage class of the Hive partitioned data objects from Coldline to Standard.

Upgrade the external table to a BigLake table Enable metadata caching for the table.

BigLake is a Google Cloud service that allows you to query structured data in external data stores such as Cloud Storage, Amazon S3, and Azure Blob Storage with access delegation and governance. BigLake tables extend the capabilities of BigQuery to data lakes and enable a flexible, open lakehouse architecture. By upgrading an external table to a BigLake table, you can improve the performance of your queries by leveraging the BigQuery storage API, which supports data format conversion, predicate pushdown, column projection, and metadata caching. Metadata caching reduces the number of requests to the external data store and speeds up query execution. To upgrade an external table to a BigLake table, you can use the ALTER TABLE statement with the SET OPTIONS clause and specify the enable_metadata_caching option as true.
For example:
SQL
ALTER TABLE hive_partitioned_data
SET OPTIONS (
enable_metadata_caching=true
);
AI-generated code. Review and use carefully. More info on FAQ.
References:
* Introduction to BigLake tables
* Upgrade an external table to BigLake
* BigQuery storage API

NEW QUESTION 206
Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Threading

Serialization

Dropout Methods

Dimensionality Reduction

Reference
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505

NEW QUESTION 207
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?

Specify the TableReferenceobject in the code.

Use .fromQueryoperation to read specific fields from the table.

Use of both the Google BigQuery TableSchemaand TableFieldSchemaclasses.

Call a transform that returns TableRowobjects, where each element in the PCollectionrepresents a single row in the table.

NEW QUESTION 208
Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?

Use a row key of the form <timestamp>.

Use a row key of the form <sensorid>.

Use a row key of the form <timestamp>#<sensorid>.

Use a row key of the form >#<sensorid>#<timestamp>.

NEW QUESTION 209
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?

Provide latitude and longtitude as input vectors to your neural net.

Create a numeric column from a feature cross of latitude and longtitude.

Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.

Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.

Explanation
Reference https://cloud.google.com/bigquery/docs/gis-data

NEW QUESTION 210
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?

BigQuery

Cloud SQL

Cloud Bigtable

Cloud Datastore

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To become a Google Certified Professional Data Engineer, candidates must pass a rigorous certification exam that consists of multiple-choice and scenario-based questions. Professional-Data-Engineer exam is designed to test the candidate’s knowledge and skills in working with big data and cloud technologies, as well as their ability to design and implement scalable and efficient data processing systems. Google Certified Professional Data Engineer Exam certification is ideal for professionals who work with data pipelines, data warehousing, and data analytics, and who have a deep understanding of cloud computing and distributed systems. By earning this certification, data engineers can demonstrate their expertise in the field and increase their career opportunities and earning potential.

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