All About Approved Professional-Data-Engineer Training Tools

NEW QUESTION 1

A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL ‘dataset.model’, table user_features). How should you create the ML pipeline?

• A. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
• B. Create an Authorized View with the provided quer
• C. Share the dataset that contains the view with the application service account.
• D. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the quer
• E. Grant the Dataflow Worker role to the application service account.
• F. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.Write the results to Cloud Bigtable using BigtableI
• G. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.

Answer: D

NEW QUESTION 2

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 3

Which of these are examples of a value in a sparse vector? (Select 2 answers.)

• A. [0, 5, 0, 0, 0, 0]
• B. [0, 0, 0, 1, 0, 0, 1]
• C. [0, 1]
• D. [1, 0, 0, 0, 0, 0, 0]

Answer: CD

Explanation:
Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent 'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1. [0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and 1s. Reference: https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations

NEW QUESTION 4

You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?

• A. Batch job, PubSubIO, side-inputs
• B. Streaming job, PubSubIO, JdbcIO, side-outputs
• C. Streaming job, PubSubIO, BigQueryIO, side-inputs
• D. Streaming job, PubSubIO, BigQueryIO, side-outputs

Answer: A

NEW QUESTION 5

What are all of the BigQuery operations that Google charges for?

• A. Storage, queries, and streaming inserts
• B. Storage, queries, and loading data from a file
• C. Storage, queries, and exporting data
• D. Queries and streaming inserts

Answer: A

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION 6

You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.
What should you do?

• A. Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.
• B. Place the MariaDB instances in an Instance Group with a Health Check.
• C. Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.
• D. Install the StackDriver Agent and configure the MySQL plugin.

Answer: C

NEW QUESTION 7

You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

• A. Add capacity (memory and disk space) to the database server by the order of 200.
• B. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
• C. Normalize the master patient-record table into the patient table and the visits table, and create othernecessary tables to avoid self-join.
• D. Partition the table into smaller tables, with one for each clini
• E. Run queries against the smaller table pairs, and use unions for consolidated reports.

Answer: B

NEW QUESTION 8

Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

• A. Disable writes to certain tables.
• B. Restrict access to tables by role.
• C. Ensure that the data is encrypted at all times.
• D. Restrict BigQuery API access to approved users.
• E. Segregate data across multiple tables or databases.
• F. Use Google Stackdriver Audit Logging to determine policy violations.

Answer: BDF

NEW QUESTION 9

What are two of the benefits of using denormalized data structures in BigQuery?

• A. Reduces the amount of data processed, reduces the amount of storage required
• B. Increases query speed, makes queries simpler
• C. Reduces the amount of storage required, increases query speed
• D. Reduces the amount of data processed, increases query speed

Answer: B

Explanation:
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data
structure, you don't have to use JOINs, since all of the data has been combined into one table. Denormalization also makes queries simpler because you do not have to use JOIN clauses.
Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
Reference:
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

NEW QUESTION 10

Your company receives both batch- and stream-based event data. You want to process the data using Google Cloud Dataflow over a predictable time period. However, you realize that in some instances data can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is late or out of order?

• A. Set a single global window to capture all the data.
• B. Set sliding windows to capture all the lagged data.
• C. Use watermarks and timestamps to capture the lagged data.
• D. Ensure every datasource type (stream or batch) has a timestamp, and use the timestamps to define the logic for lagged data.

Answer: B

NEW QUESTION 11

Cloud Dataproc is a managed Apache Hadoop and Apache service.

• A. Blaze
• B. Spark
• C. Fire
• D. Ignite

Answer: B

Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/

NEW QUESTION 12

An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?

• A. Use federated data sources, and check data in the SQL query.
• B. Enable BigQuery monitoring in Google Stackdriver and create an alert.
• C. Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.
• D. Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.

Answer: D

NEW QUESTION 13

You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30–90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

• A. Re-create the tables using DD
• B. Partition the tables by a column containing a TIMESTAMP or DATE Type.
• C. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
• D. Modify your pipeline to maintain the last 30–90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
• E. Write an Apache Beam pipeline that creates a BigQuery table per da
• F. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

Answer: C

NEW QUESTION 14

Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

• A. BETWEEN
• B. WHERE
• C. SELECT
• D. LIMIT

Answer: C

Explanation:
SELECT allows you to query specific columns rather than the whole table.
LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by BigQuery.
Reference:
https://cloud.google.com/bigquery/launch-checklist#architecture_design_and_development_checklist

NEW QUESTION 15

You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

• A. Create a table in BigQuery, and append the new samples for CPU and memory to the table
• B. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
• C. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
• D. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

Answer: D

NEW QUESTION 16

Why do you need to split a machine learning dataset into training data and test data?

• A. So you can try two different sets of features
• B. To make sure your model is generalized for more than just the training data
• C. To allow you to create unit tests in your code
• D. So you can use one dataset for a wide model and one for a deep model

Answer: B

Explanation:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/

NEW QUESTION 17

You work for an advertising company, and you’ve developed a Spark ML model to predict click-through rates at advertisement blocks. You’ve been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

• A. Use Cloud ML Engine for training existing Spark ML models
• B. Rewrite your models on TensorFlow, and start using Cloud ML Engine
• C. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
• D. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

Answer: A

NEW QUESTION 18

As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

• A. Use Cloud Deployment Manager to automate access provision.
• B. Introduce resource hierarchy to leverage access control policy inheritance.
• C. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
• D. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
• E. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
• F. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Answer: AC

NEW QUESTION 19

Your financial services company is moving to cloud technology and wants to store 50 TB of financial timeseries data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?

• A. Cloud Bigtable
• B. Google BigQuery
• C. Google Cloud Storage
• D. Google Cloud Datastore

Answer: A

Explanation:
Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series

NEW QUESTION 20

Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day’s events. They also want to use streaming ingestion. What should you do?

• A. Create a table called tracking_table and include a DATE column.
• B. Create a partitioned table called tracking_table and include a TIMESTAMP column.
• C. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.
• D. Create a table called tracking_table with a TIMESTAMP column to represent the day.

Answer: B

NEW QUESTION 21

Flowlogistic’s CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they’ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they’ve been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

• A. Export the data into a Google Sheet for virtualization.
• B. Create an additional table with only the necessary columns.
• C. Create a view on the table to present to the virtualization tool.
• D. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

Answer: C

NEW QUESTION 22

You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?

• A. Continuously retrain the model on just the new data.
• B. Continuously retrain the model on a combination of existing data and the new data.
• C. Train on the existing data while using the new data as your test set.
• D. Train on the new data while using the existing data as your test set.

Answer: D

NEW QUESTION 23

You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
Decoupling producer from consumer
Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
Near real-time SQL query
Maintain at least 2 years of historical data, which will be queried with SQ
Which pipeline should you use to meet these requirements?

• A. Create an application that provides an AP
• B. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
• C. Create an application that writes to a Cloud SQL database to store the dat
• D. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
• E. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
• F. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.

Answer: A

NEW QUESTION 24
......