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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Designing data processing systems	20%	- Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection - Storage and data modeling 1. Data warehouse design using BigQuery 2. Data lake architecture on Google Cloud Storage
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
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. BigQuery ML usage 2. Vertex AI integration for pipelines
Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows - Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging

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Google Data Engineer Sample Questions (Q35-Q40):

NEW QUESTION # 35
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A. Write an Apache Beam pipeline that creates a BigQuery table per data
B. Partition the tables by a column containing a TIMESTAMP or DATE Type.
C. Re-create the tables using DD
D. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
E. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
F. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

Answer: E

NEW QUESTION # 36
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. 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.
B. If that number falls below 4000, send an alert.
C. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
D. Set a sliding time window of 1 hour every 5 minute
E. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
F. Consume the stream of data in Cloud Dataflow using Kafka I
G. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
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. Consume the stream of data in Cloud Dataflow using Kafka I
K. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
L. If that number falls below 4000, send an alert.
M. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour

Answer: C

NEW QUESTION # 37
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. Denormalize the data as must as possible.
B. Preserve the structure of the data as much as possible.
C. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
D. Use BigQuery UPDATE to further reduce the size of the dataset.
E. Use BigQuery'ssupport for external data sources to query.
F. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil

Answer: C,F

NEW QUESTION # 38
What are two methods that can be used to denormalize tables in BigQuery?

A. 1) Split table into multiple tables; 2) Use a partitioned table
B. 1) Use nested repeated fields; 2) Use a partitioned table
C. 1) Join tables into one table; 2) Use nested repeated fields
D. 1) Use a partitioned table; 2) Join tables into one table

Answer: C

Explanation:
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information. The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

NEW QUESTION # 39
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. Combine highly co-dependent features into one representative feature.
C. Eliminate features that are highly correlated to the output labels.
D. Instead of feeding in each feature individually, average their values in batches of 3.

Answer: B

NEW QUESTION # 40
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