Does Google GCP-DE Certification Help you Polish your Skills?

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Building and operationalizing data processing systems	30%	- Data pipeline implementation 1. Dataproc and Spark-based processing 2. Dataflow pipeline development - Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows
Topic 2: Maintaining and optimizing data and ML solutions	20%	- Machine learning integration 1. Vertex AI integration for pipelines 2. BigQuery ML usage - Security and governance 1. IAM and access control 2. Data encryption and compliance
Topic 3: Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Performance optimization and debugging 2. Logging and observability - Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring pipelines
Topic 4: Designing data processing systems	20%	- Storage and data modeling 1. Data lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery - Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection

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Real Data Engineer Pass4sure Torrent - GCP-DE Study Pdf & Data Engineer Practice Questions

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Google Data Engineer Sample Questions (Q55-Q60):

NEW QUESTION # 55
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. Eliminate features that are highly correlated to the output labels.
B. Combine highly co-dependent features into one representative feature.
C. Instead of feeding in each feature individually, average their values in batches of 3.
D. Remove the features that have null values for more than 50% of the training records.

Answer: B

NEW QUESTION # 56
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. Use BigQuery'ssupport for external data sources to query.
B. Denormalize the data as must as possible.
C. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
D. Use BigQuery UPDATE to further reduce the size of the dataset.
E. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
F. Preserve the structure of the data as much as possible.

Answer: C,E

NEW QUESTION # 57
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

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

Answer: C

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 # 58
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
B. Use the service account's private key to access the dataset
C. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
D. Create groups for your users and give those groups access to the dataset
E. Create a service account and grant dataset access to that accoun
F. Create a dummy user and grant dataset access to that use

Answer: E

NEW QUESTION # 59
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. Before you test, run a heavy pre-test for several minutes.
B. Run your test for at least 10 minutes.
C. Use at least 300 GB of data.
D. Do not use a production instance.

Answer: D

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 # 60
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