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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q26-Q31):

NEW QUESTION # 26
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. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
B. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
C. Merge the transformed tables together with a SQL query
D. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
E. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
F. 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

Answer: A

NEW QUESTION # 27
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A. Instead of feeding in each feature individually, average their values in batches of 3.
B. Remove the features that have null values for more than 50% of the training records.
C. Eliminate features that are highly correlated to the output labels.
D. Combine highly co-dependent features into one representative feature.

Answer: D

NEW QUESTION # 28
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. Add secondary indexes to support query patterns.
B. Use Cloud Spanner for storag
C. Use Cloud Dataflow to transform data to support query patterns.
D. Use Cloud SQL for storag
E. Use Cloud SQL for storag
F. Use Cloud Spanner for storag
G. Use Cloud Dataflow to transform data to support query patterns.
H. Add secondary indexes to support query patterns.

Answer: G

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

NEW QUESTION # 29
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 stock symbol followed by a timestamp
C. A timestamp followed by a stock symbol
D. A sequential numeric ID

Answer: C,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 # 30
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. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
B. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
C. Consume the stream of data in Cloud Dataflow using Kafka I
D. If that number falls below 4000, send an alert.
E. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
F. Set a sliding time window of 1 hour every 5 minute
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 Kafka Connect to link your Kafka message queue to Cloud Pub/Su
I. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
J. If that number falls below 4000, send an alert.
K. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
L. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
M. Consume the stream of data in Cloud Dataflow using Kafka I

Answer: K

NEW QUESTION # 31
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