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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

A) Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
B) Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
C) Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
D) Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.

2. Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

A) Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
B) Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
C) Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
D) Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.

3. You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

A) Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
B) Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.
C) Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
D) Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.

4. You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

A) Use Dataform to define the transformations in SQLX.
B) Use Dataflow with a custom template for the transformation logic.
C) Use Dataproc to run the transformations on a cluster.
D) Use Cloud Data Fusion to visually design and manage the pipeline.

5. You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A) Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
B) Use Cloud Composer to orchestrate the Spark job and email the report.
C) Use Cloud Run functions to trigger the Spark job and email the report.
D) Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.

Solutions: