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Google Associate-Data-Practitioner Google Cloud Associate Data Practitioner (ADP Exam) Exam Practice Test

Google Cloud Associate Data Practitioner (ADP Exam) Questions and Answers

Question 1

You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

Options:

A.

Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

B.

Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.

C.

Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.

D.

Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.

Question 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 (Pll) 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?

Options:

A.

1. Cloud Storage

2. CloudSQL for PostgreSQL

3. Bigtable

B.

1. Filestore

2. Cloud SQL for PostgreSQL

3. Datastore

C.

1. Cloud Storage

2. BigQuery

3. Firestore

D.

1. Filestore

2. Bigtable

3. BigQuery

Question 3

Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

Options:

A.

Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.

B.

Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job ID.

C.

Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.

D.

Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.

Question 4

Your organization needs to store historical customer order data. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

Options:

A.

Store the data in Cloud Storaqe usinq Nearline storaqe.

B.

Store the data in Cloud Storaqe usinq Coldline storaqe.

C.

Store the data in Cloud Storage using Standard storage.

D.

Store the data in Cloud Storage using Archive storage.

Question 5

Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store. Which query should you use?

A)

B)

C)

D)

Options:

A.

Option A

B.

Option B

C.

Option C

D.

Option D

Question 6

Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?

Options:

A.

Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.

B.

Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.

C.

Use the existing data pipeline tool’s BigQuery connector to reconfigure the data mapping.

D.

Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.

Question 7

Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

Options:

A.

Use Cloud Scheduler to schedule the jobs to run.

B.

Use Cloud Tasks to schedule and run the jobs asynchronously.

C.

Create directed acyclic graphs (DAGs) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

D.

Create directed acyclic graphs (DAGs) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

Question 8

Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability. What should you do?

Options:

A.

Create a dual-region bucket, and upload the files to this bucket.

B.

Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.

C.

Create a multi-region bucket, and upload the files to this bucket.

D.

Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.

Question 9

Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization’s storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

Options:

A.

Create a scheduled query that periodically runs an update statement in SQL that sets the “deleted" column to “yes” for data that is more than one year old. Create a view that filters out rows that have been marked deleted.

B.

Create a view that filters out rows that are older than one year.

C.

Require users to specify a partition filter using the alter table statement in SQL.

D.

Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.

Question 10

Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

Options:

A.

Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.

B.

Create a saved query in the BigQuery console that uses the SUM( ) function and the DATE_SUB( ) function. Re-run the saved query every month, and save the results to a BigQuery table.

C.

Create a BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.

D.

Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.

Question 11

Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company’s products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?

Options:

A.

Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

B.

Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

C.

Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

D.

Apply a filter to only show products with a positive profit margin.

Question 12

You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

Options:

A.

Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.

B.

Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.

C.

Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.

D.

Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.

Question 13

Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

Options:

A.

Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

B.

Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.

C.

Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.

D.

Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.

Question 14

Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys. What should you do?

Options:

A.

Use Google-managed encryption keys (GMEK).

B.

Use customer-managed encryption keys (CMEK).

C.

Use customer-supplied encryption keys (CSEK).

D.

Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.

Question 15

You are a Looker analyst. You need to add a new field to your Looker report that generates SQL that will run against your company's database. You do not have the Develop permission. What should you do?

Options:

A.

Create a new field in the LookML layer, refresh your report, and select your new field from the field picker.

B.

Create a calculated field using the Add a field option in Looker Studio, and add it to your report.

C.

Create a table calculation from the field picker in Looker, and add it to your report.

D.

Create a custom field from the field picker in Looker, and add it to your report.

Question 16

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?

Options:

A.

Compare the two different models.

B.

Evaluate the data skewness.

C.

Evaluate data drift.

D.

Compare the confusion matrix.

Question 17

You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

Options:

A.

Create asales_regionuser attribute, and assign each manager’s region as the value of their user attribute. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.

B.

Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.

C.

Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

D.

Create separate Looker instances for each regional manager. Copy the LookML model and dashboard to each instance. Provision viewer access to the corresponding manager.

Question 18

Your organization’s business analysts require near real-time access to streaming data. However, they are reporting that their dashboard queries are loading slowly. After investigating BigQuery query performance, you discover the slow dashboard queries perform several joins and aggregations.

You need to improve the dashboard loading time and ensure that the dashboard data is as up-to-date as possible. What should you do?

Options:

A.

Disable BiqQuery query result caching.

B.

Modify the schema to use parameterized data types.

C.

Create a scheduled query to calculate and store intermediate results.

D.

Create materialized views.

Question 19

Your company’s ecommerce website collects product reviews from customers. The reviews are loaded as CSV files daily to a Cloud Storage bucket. The reviews are in multiple languages and need to be translated to Spanish. You need to configure a pipeline that is serverless, efficient, and requires minimal maintenance. What should you do?

Options:

A.

Load the data into BigQuery using Dataproc. Use Apache Spark to translate the reviews by invoking the Cloud Translation API. Set BigQuery as the sink.U

B.

Use a Dataflow templates pipeline to translate the reviews using the Cloud Translation API. Set BigQuery as the sink.

C.

Load the data into BigQuery using a Cloud Run function. Use the BigQuery ML create model statement to train a translation model. Use the model to translate the product reviews within BigQuery.

D.

Load the data into BigQuery using a Cloud Run function. Create a BigQuery remote function that invokes the Cloud Translation API. Use a scheduled query to translate new reviews.

Question 20

You manage an ecommerce website that has a diverse range of products. You need to forecast future product demand accurately to ensure that your company has sufficient inventory to meet customer needs and avoid stockouts. Your company's historical sales data is stored in a BigQuery table. You need to create a scalable solution that takes into account the seasonality and historical data to predict product demand. What should you do?

Options:

A.

Use the historical sales data to train and create a BigQuery ML time series model. Use the ML.FORECAST function call to output the predictions into a new BigQuery table.

B.

Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python.

C.

Use the historical sales data to train and create a BigQuery ML linear regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.

D.

Use the historical sales data to train and create a BigQuery ML logistic regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.

Question 21

You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re-run risk reports when new data becomes available. What should you do?

Options:

A.

Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.

B.

Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.

C.

Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.

D.

Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.

Question 22

You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

Options:

A.

Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.

B.

Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.

C.

Develop a batch process using Dataflow that runs weekly and deletes files based on their age.

D.

Create a Cloud Run function that runs daily and deletes files older than seven days.

Question 23

Your team uses the Google Ads platform to visualize metrics. You want to export the data to BigQuery to get more granular insights. You need to execute a one-time transfer of historical data and automatically update data daily. You want a solution that is low-code, serverless, and requires minimal maintenance. What should you do?

Options:

A.

Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use Cloud Composer for daily automation.

B.

Export the historical data to Cloud Storage by using Storage Transfer Service. Use Pub/Sub to trigger a Dataflow template that loads data for daily automation.

C.

Export the historical data as a CSV file. Import the file into BigQuery for analysis. Use Cloud Composer for daily automation.

D.

Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use BigQuery Data Transfer Service for daily automation.

Question 24

Your organization plans to move their on-premises environment to Google Cloud. Your organization’s network bandwidth is less than 1 Gbps. You need to move over 500 ТВ of data to Cloud Storage securely, and only have a few days to move the data. What should you do?

Options:

A.

Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.

B.

Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.

C.

Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.

D.

Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.

Question 25

You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rowsand transactional data. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

Options:

A.

Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.

B.

Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.

C.

Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.

D.

Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

Question 26

You work for a healthcare company that has a large on-premises data system containing patient records with personally identifiable information (PII) such as names, addresses, and medical diagnoses. You need a standardized managed solution that de-identifies PII across all your data feeds prior to ingestion to Google Cloud. What should you do?

Options:

A.

Use Cloud Run functions to create a serverless data cleaning pipeline. Store the cleaned data in BigQuery.

B.

Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.

C.

Load the data into BigQuery, and inspect the data by using SQL queries. Use Dataflow to transform the data and remove any errors.

D.

Use Apache Beam to read the data and perform the necessary cleaning and transformation operations. Store the cleaned data in BigQuery.