Current GCP-DE Exam Content - GCP-DE Certification Questions

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Google GCP-DE Exam Syllabus Topics:

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

>> Current GCP-DE Exam Content <<

GCP-DE Certification Questions | Latest GCP-DE Dumps Book

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Google Data Engineer Sample Questions (Q30-Q35):

NEW QUESTION # 30
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 loaded in BigQuery is not flagged as CSV.
C. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
D. The CSV data has not gone through an ETL phase before loading into BigQuery.

Answer: A

NEW QUESTION # 31
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 feature engineering to add features for eyes, noses, and mouths to the input data.
C. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
D. Use K-means Clustering to detect faces in the pixels.

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 # 32
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A. Use Cloud Dataflow to transform data to support query patterns.
B. Use Cloud Dataflow to transform data to support query patterns.
C. Use Cloud SQL for storag
D. Add secondary indexes to support query patterns.
E. Add secondary indexes to support query patterns.
F. Use Cloud Spanner for storag
G. Use Cloud Spanner for storag
H. Use Cloud SQL for storag

Answer: A

Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform

NEW QUESTION # 33
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. Resize the number of worker nodes in your cluster via the command line.
B. Monitor CPU utilization for the cluste
C. Configure the job to use non-default Compute Engine machine types when needed.
D. Use Cloud Dataproc to run your transformation
E. Use Cloud Dataflow to run your transformation
F. Monitor the job system lag with Stackdrive
G. Use the default autoscaling setting for worker instances.
H. Locate the bottleneck and adjust cluster resources.
I. Use the diagnose command to generate an operational output archiv
J. Use Cloud Dataproc to run your transformation
K. Monitor the total execution time for a sampling of job
L. Use Cloud Dataflow to run your transformation

Answer: B

NEW QUESTION # 34
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
B. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
C. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project

Answer: B

NEW QUESTION # 35
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