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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Ingesting and processing data	25%	- Transforming data 1. Optimizing transformations for performance 2. Applying data processing logic 3. Managing schema evolution - Building data pipelines 1. Developing batch and streaming workflows 2. Handling data quality and consistency 3. Implementing ingestion mechanisms
Topic 2: Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Implementing data sharing and governance 2. Optimizing query performance 3. Preparing datasets for querying and reporting - Preparing data for ML 1. Supporting model training and serving 2. Feature engineering and data preparation
Topic 3: Maintaining and automating data workloads	18%	- Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management - Operationalizing workloads 1. Ensuring reliability and recoverability 2. Orchestrating and scheduling jobs 3. Monitoring and troubleshooting pipelines
Topic 4: Designing data processing systems	24%	- Designing data pipelines 1. Designing for data transformation and enrichment 2. Planning for data migration and integration 3. Defining architecture for batch and streaming processing - Planning data solutions 1. Planning for data security and compliance 2. Selecting appropriate storage solutions 3. Designing for reliability, scalability, and efficiency
Topic 5: Storing and managing data	20%	- Implementing storage solutions 1. Using data storage services appropriately 2. Managing data lifecycle and retention 3. Designing data warehouses and data lakes - Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies

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Google Data Engineer Sample Questions (Q53-Q58):

NEW QUESTION # 53
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
B. Create a cron schedule in Cloud Dataprep.
C. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
D. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.

Answer: D

NEW QUESTION # 54
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. Use feature engineering to add features for eyes, noses, and mouths to the input data.
B. Use K-means Clustering to detect faces in the pixels.
C. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
D. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.

Answer: D

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 # 55
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A. Create a view on the table to present to the virtualization tool.
B. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
C. Create an additional table with only the necessary columns.
D. Export the data into a Google Sheet for virtualization.

Answer: A

NEW QUESTION # 56
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. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
B. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
C. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
D. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
E. Create an authorized view in BigQuery to restrict access to tables with sensitive data.

Answer: E

NEW QUESTION # 57
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
B. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
C. Install the Cloud Storage connector, and store the data in Cloud Storag
D. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
E. Use a standard persistent disk and 50% preemptible worker
F. Deploy a Cloud Dataproc cluste
G. Use an SSD persistent disk and 50% preemptible worker
H. Deploy a Cloud Dataproc cluste
I. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
J. Change references in scripts from hdfs:// to gs://
K. Change references in scripts from hdfs:// to gs://

Answer: H

NEW QUESTION # 58
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