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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

A) Evaluate data drift.
B) Evaluate the data skewness.
C) Compare the two different models.
D) Compare the confusion matrix.

2. You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?

A) Filestore Cloud SQL for PostgreSQL Datastore
B) Cloud Storage CloudSQL for PostgreSQL Bigtable
C) Filestore Bigtable BigQuery
D) Cloud Storage BigQuery Firestore

3. Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

A) Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
B) Migrate the Spark jobs to Dataproc Serverless.
C) Migrate the Spark jobs to Dataproc on Compute Engine.
D) Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.

4. You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?

A) Use Dataform to build, orchestrate, and monitor the pipelines.
B) Use Cloud Data Fusion to visually design and manage the pipelines.
C) Use Cloud Composer to orchestrate and run data workflows.
D) Use Dataproc to run MapReduce jobs for distributed data processing.

5. You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

A) Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
B) Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.
C) Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline's metrics, such as CPU utilization and memory usage.
D) Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.

Solutions: