[Aug-2026 Newly Released] GCP-DE Dumps for Google Cloud Certified Certified [Q28-Q52]

[Aug-2026 Newly Released] GCP-DE Dumps for Google Cloud Certified Certified

Updated Verified GCP-DE dumps Q&As - 100% Pass

NEW QUESTION # 28
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 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
• B. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
• C. Merge the transformed tables together with a SQL query
• D. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
• E. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
• F. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

Answer: D

NEW QUESTION # 29
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

• A. Use Cloud Dataproc to run your transformation
• B. Use the diagnose command to generate an operational output archiv
• C. Use Cloud Dataproc to run your transformation
• D. Use Cloud Dataflow to run your transformation
• E. Monitor the total execution time for a sampling of job
• F. Configure the job to use non-default Compute Engine machine types when needed.
• G. Use the default autoscaling setting for worker instances.
• H. Monitor the job system lag with Stackdrive
• I. Monitor CPU utilization for the cluste
• J. Resize the number of worker nodes in your cluster via the command line.
• K. Locate the bottleneck and adjust cluster resources.
• L. Use Cloud Dataflow to run your transformation

Answer: I

NEW QUESTION # 30
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 a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
• B. 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
• C. 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
• D. 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

Answer: D

NEW QUESTION # 31
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

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

Answer: D

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 # 32
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

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

Answer: C

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 # 33
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?

• A. Store the data in a file in a regional Google Cloud Storage bucke
• B. Load the data every 30 minutes into a new partitioned table in BigQuery.
• C. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
• D. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
• E. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
• F. Store the data in Google Cloud Datastor

Answer: B

NEW QUESTION # 34
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. Train your own image recognition model leveraging transfer learning techniques.
• C. Use Cloud Vision AutoML with the existing dataset.
• D. Use Cloud Vision API by providing custom labels as recognition hints.

Answer: C

NEW QUESTION # 35
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

• A. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
• B. Change the zone of your Cloud Dataflow pipeline to run in us-central1
• C. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
• D. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
• E. Use a larger instance type for your Cloud Dataflow workers
• F. Create a temporary table in Cloud Spanner that will act as a buffer for new data
• G. Increase the number of max workers

Answer: A,E

NEW QUESTION # 36
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?

• A. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
• B. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
• C. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
• D. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
• E. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

Answer: B

NEW QUESTION # 37
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

• A. Pipeline
• B. Sink API
• C. Transform
• D. PCollection

Answer: C

Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
Reference: https://cloud.google.com/dataflow/model/programming-model

NEW QUESTION # 38
Cloud Dataproc is a managed Apache Hadoop and Apache service.

• A. Spark
• B. Ignite
• C. Fire
• D. Blaze

Answer: A

Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/

NEW QUESTION # 39
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. Mount the Hive tables from HDFS.
• C. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
• D. Mount the Hive tables locally.
• E. Then run the Hadoop utility to copy them do HDF
• F. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
• G. Load the ORC files into BigQuer
• H. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
• I. Replicate external Hive tables to the native ones.
• J. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
• K. Mount the Hive tables locally.
• L. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste

Answer: D,H

NEW QUESTION # 40
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

• A. Run your test for at least 10 minutes.
• B. Use at least 300 GB of data.
• C. Do not use a production instance.
• D. Before you test, run a heavy pre-test for several minutes.

Answer: C

Explanation:
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 41
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 deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
• C. Use K-means Clustering to detect faces in the pixels.
• D. Use feature engineering to add features for eyes, noses, and mouths to the input data.

Answer: B

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 # 42
Which is not a valid reason for poor Cloud Bigtable performance?

• A. The table's schema is not designed correctly.
• B. There are issues with the network connection.
• C. The workload isn't appropriate for Cloud Bigtable.
• D. The Cloud Bigtable cluster has too many nodes.

Answer: D

Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 43
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

• A. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
• B. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
• C. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
• D. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet

Answer: B

NEW QUESTION # 44
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

• A. VPN
• B. SOCKS
• C. HTTPS
• D. HTTP

Answer: B

Explanation:
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces

NEW QUESTION # 45
Which of the following is NOT true about Dataflow pipelines?

• A. Dataflow pipelines can be programmed in Java
• B. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
• C. Dataflow pipelines can consume data from other Google Cloud services
• D. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

Answer: D

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

NEW QUESTION # 46
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

• A. Enable query caching so you can cache data from previous months
• B. Convert the sharded tables into a single partitioned table
• C. Convert all daily log tables into date-partitioned tables
• D. Create separate views to cover each month, and query from these views

Answer: C

NEW QUESTION # 47
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

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

Answer: B

NEW QUESTION # 48
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. Cloud Bigtable
• B. Cloud SQL for PostgreSQL
• C. Cloud Datastore
• D. BigQuery

Answer: D

NEW QUESTION # 49
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

• A. Cloud Scheduler
• B. Cloud Composer
• C. Cloud Functions
• D. Cloud Dataflow

Answer: A

NEW QUESTION # 50
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

• A. Trigger based on time
• B. Trigger based on element count
• C. Trigger that is a combination of other triggers
• D. Trigger based on element size in bytes

Answer: D

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION # 51
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 stock symbol followed by a timestamp
• C. A non-sequential numeric ID
• 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 # 52
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