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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q45-Q50):

NEW QUESTION # 45
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. Merge the transformed tables together with a SQL query
B. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
C. 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
D. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
E. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
F. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio

Answer: F

NEW QUESTION # 46
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. Remove the features that have null values for more than 50% of the training records.
B. Eliminate features that are highly correlated to the output labels.
C. Combine highly co-dependent features into one representative feature.
D. Instead of feeding in each feature individually, average their values in batches of 3.

Answer: C

NEW QUESTION # 47
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?

A. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
B. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
C. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
D. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination

Answer: B

NEW QUESTION # 48
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A. Preserve the structure of the data as much as possible.
B. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
C. Denormalize the data as must as possible.
D. Use BigQuery UPDATE to further reduce the size of the dataset.
E. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
F. Use BigQuery'ssupport for external data sources to query.

Answer: B,E

NEW QUESTION # 49
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

A. Cloud Bigtable
B. Cloud SQL
C. Cloud Datastore
D. Cloud Spanner

Answer: B

NEW QUESTION # 50
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