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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q53-Q58):

NEW QUESTION # 53
What are the minimum permissions needed for a service account used with Google Dataproc?

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

Answer: A

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 # 54
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
B. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
C. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
D. Create an application that writes to a Cloud SQL database to store the dat
E. Create an application that provides an AP
F. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.

Answer: E

NEW QUESTION # 55
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. Deploy the model using Cloud Machine Learning Engin
B. Call the model from your application and process the results as labels.
C. Build and train a text classification model using TensorFlo
D. Process the generated Sentiment Analysis as labels.
E. Call the Cloud Natural Language API from your applicatio
F. Build and train a text classification model using TensorFlo
G. Deploy the model using a KubernetesEngine cluste
H. Call the Cloud Natural Language API from your applicatio
I. Call the model from your application and process the results as labels.
J. Process the generated Entity Analysis as labels.

Answer: J

NEW QUESTION # 56
Which of the following is NOT true about Dataflow pipelines?

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

Answer: C

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 # 57
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 non-sequential numeric ID
B. A timestamp followed by a stock symbol
C. A sequential numeric ID
D. A stock symbol followed by a timestamp

Answer: B,C

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 # 58
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