2026 Updated Verified Pass GCP-DE Study Guides & Best Courses [Q17-Q39]

2026 Updated Verified Pass GCP-DE Study Guides & Best Courses

Ultimate Guide to the GCP-DE - Latest Edition Available Now

NEW QUESTION # 17
Which of these sources can you not load data into BigQuery from?

• A. Google Cloud SQL
• B. Google Drive
• C. File upload
• D. Google Cloud Storage

Answer: A

Explanation:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference: https://cloud.google.com/bigquery/loading-data

NEW QUESTION # 18
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

• A. Add a new column called FullName to the Users tabl
• B. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
• C. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
• D. Use BigQuery to export the data for the table to a CSV fil
• E. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
• F. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

Answer: B

NEW QUESTION # 19
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

• A. Single, Global Window
• B. Windows at every 10 minutes
• C. Windows at every 1 minute
• D. Windows at every 100 MB of data

Answer: A

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection

NEW QUESTION # 20
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

• A. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
• B. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
• C. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
• D. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.

Answer: A

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 # 21
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

• A. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
• B. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
• C. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
• D. Cloud Pub/Sub, Cloud SQL, and Cloud Storage

Answer: D

NEW QUESTION # 22
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

• A. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
• B. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
• C. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
• D. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
• E. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
• F. Merge the transformed tables together with a SQL query

Answer: B

NEW QUESTION # 23
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow 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 GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
• 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 SideInput that returns a Boolean if the element is corrupt.

Answer: B

NEW QUESTION # 24
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

• A. Replicate external Hive tables to the native ones.
• B. Load the ORC files into BigQuer
• C. Mount the Hive tables locally.
• D. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
• E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
• F. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
• G. Then run the Hadoop utility to copy them do HDF
• H. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
• I. Mount the Hive tables locally.
• J. Mount the Hive tables from HDFS.
• K. Replicate external Hive tables to the native ones.
• L. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table

Answer: C,E

NEW QUESTION # 25
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 dat a. Which Stackdriver alerts should you create?

• A. 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
• B. 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
• C. 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
• D. 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

Answer: A

NEW QUESTION # 26
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

• A. Cloud Speech-to-Text API
• B. Cloud AutoML Natural Language
• C. Cloud Natural Language API
• D. Dialogflow Enterprise Edition

Answer: B

NEW QUESTION # 27
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

• A. The CSV data has invalid rows that were skipped on import.
• B. The CSV data has not gone through an ETL phase before loading into BigQuery.
• C. The CSV data loaded in BigQuery is not flagged as CSV.
• D. The CSV data loaded in BigQuery is not using BigQuery's default encoding.

Answer: A

NEW QUESTION # 28
What are the minimum permissions needed for a service account used with Google Dataproc?

• A. Write to Google Cloud Storage; read to Google Cloud Logging
• B. Read and write to Google Cloud Storage; write to Google Cloud Logging
• C. Execute to Google Cloud Storage; write to Google Cloud Logging
• D. Execute to Google Cloud Storage; execute to Google Cloud Logging

Answer: B

Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

NEW QUESTION # 29
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

• A. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
• B. Use K-means Clustering to detect faces in the pixels.
• C. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
• D. Use feature engineering to add features for eyes, noses, and mouths to the input data.

Answer: C

Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K- means Clustering is an unsupervised learning method used to categorize unlabeled data. Reference: https://deeplearning4j.org/neuralnet-overview

NEW QUESTION # 30
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

• A. before
• B. once
• C. after
• D. only if

Answer: A

Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 31
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

• A. Create an API using App Engine to receive and send messages to the applications
• B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
• C. Create a table on Cloud SQL, and insert and delete rows with the job information
• D. Create a table on Cloud Spanner, and insert and delete rows with the job information

Answer: B

NEW QUESTION # 32
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

• A. Process the generated Sentiment Analysis as labels.
• B. Call the Cloud Natural Language API from your applicatio
• C. Build and train a text classification model using TensorFlo
• D. Build and train a text classification model using TensorFlo
• E. Call the model from your application and process the results as labels.
• F. Call the model from your application and process the results as labels.
• G. Deploy the model using Cloud Machine Learning Engin
• H. Deploy the model using a KubernetesEngine cluste
• I. Call the Cloud Natural Language API from your applicatio
• J. Process the generated Entity Analysis as labels.

Answer: J

NEW QUESTION # 33
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

• A. Cloud Datastore
• B. BigQuery
• C. Cloud SQL
• D. Cloud Bigtable

Answer: C

NEW QUESTION # 34
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 timestamp followed by a stock symbol
• B. A non-sequential numeric ID
• C. A stock symbol followed by a timestamp
• D. A sequential numeric ID

Answer: A,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_hotspotti

NEW QUESTION # 35
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

• A. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
• B. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
• C. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
• D. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.

Answer: D

NEW QUESTION # 36
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

• A. HiveQL using Hive
• B. Python using MapReduce
• C. Java using MapReduce
• D. PigLatin using Pig

Answer: B

NEW QUESTION # 37
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. 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. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
• C. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
• D. Introduce resource hierarchy to leverage access control policy inheritance.
• E. Use Cloud Deployment Manager to automate access provision.
• F. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

Answer: B,D

NEW QUESTION # 38
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

• A. Source API
• B. Data extraction
• C. ParDo
• D. Sink API

Answer: C

Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do

NEW QUESTION # 39
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