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

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Google Data Engineer Sample Questions (Q54-Q59):

NEW QUESTION # 54
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. Combine highly co-dependent features into one representative feature.
B. Instead of feeding in each feature individually, average their values in batches of 3.
C. Remove the features that have null values for more than 50% of the training records.
D. Eliminate features that are highly correlated to the output labels.

Answer: A

NEW QUESTION # 55
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

A. Cloud Dataflow
B. BigQuery
C. Cloud Bigtable
D. Cloud Datastore

Answer: B

Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive

NEW QUESTION # 56
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
B. Add a new column called FullName to the Users tabl
C. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
D. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
E. Use BigQuery to export the data for the table to a CSV fil
F. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

Answer: C

NEW QUESTION # 57
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

A. Use at least 300 GB of data.
B. Before you test, run a heavy pre-test for several minutes.
C. Do not use a production instance.
D. Run your test for at least 10 minutes.

Answer: C

Explanation:
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 58
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
B. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
C. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
D. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.

Answer: D

NEW QUESTION # 59
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