GCP-DE Test Collection Pdf - GCP-DE Valid Test Simulator

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Performance optimization and debugging 2. Logging and observability - Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows
Topic 2: Building and operationalizing data processing systems	30%	- Data ingestion and transformation 1. ETL/ELT workflows 2. Pub/Sub streaming ingestion - Data pipeline implementation 1. Dataproc and Spark-based processing 2. Dataflow pipeline development
Topic 3: Maintaining and optimizing data and ML solutions	20%	- Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines - Security and governance 1. IAM and access control 2. Data encryption and compliance
Topic 4: Designing data processing systems	20%	- Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection - Storage and data modeling 1. Data lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery

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Google Data Engineer Sample Questions (Q11-Q16):

NEW QUESTION # 11
Which of these sources can you not load data into BigQuery from?

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

Answer: D

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 # 12
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. Train your own image recognition model leveraging transfer learning techniques.
B. Use Cloud Vision API by providing custom labels as recognition hints.
C. Use Cloud Vision AutoML with the existing dataset.
D. Use Cloud Vision AutoML, but reduce your dataset twice.

Answer: C

NEW QUESTION # 13
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A. Preemptible workers cannot store data.
B. A Dataproc cluster cannot have only preemptible workers.
C. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
D. Preemptible workers cannot use persistent disk.

Answer: A,B

Explanation:
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster: Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits. Reference:
https://cloud.google.com/dataproc/docs/concepts/preemptible-vms

NEW QUESTION # 14
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. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
B. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
C. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
D. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

Answer: A

NEW QUESTION # 15
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. If that number falls below 4000, send an alert.
B. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
C. Consume the stream of data in Cloud Dataflow using Kafka I
D. Consume the stream of data in Cloud Dataflow using Kafka I
E. If that number falls below 4000, send an alert.
F. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
G. 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.
H. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
I. Set a sliding time window of 1 hour every 5 minute
J. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
K. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
L. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
M. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Answer: M

NEW QUESTION # 16
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